Search Results

The CT 5300 is a Computed Tomography X-Ray System intended to produce images of the head and body by computer reconstruction of x-ray transmission data taken at different angles and planes. These devices may include signal analysis and display equipment, patient and equipments and accessories. The CT 5300 is indicated for head, whole body, cardiac and vascular X-ray Computed Tomography applications in patients of all ages.

These scanners are intended to be used for diagnostic imaging and for low dose CT lung cancer screening for the early detection of lung nodules that may represent cancer*. The screening must be performed within the established inclusion criteria of programs / protocols that have been approved and published by etther a governmental body or professional medical society.

• Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information.

The proposed device is a whole-body computed tomography (CT) X-Ray System featuring a continuously rotating x-ray tube, detectors, and gantry with multi-slice capability. The acquired x-ray transmission data is reconstructed by computer into cross-sectional images of the body taken at different angles and planes. This system also includes signal analysis and display equipment, patient and equipment supports, components, and accessories. The CT 5300 has a 72 cm bore and includes a detector array that provides 50 cm scan field of view (FOV). The main components (detection system, the reconstruction algorithm, and the x-ray system) that are used in the proposed device have the same fundamental design characteristics and are based on comparable technologies as the current marketed predicate Philips Incisive CT K212441(April 27, 2022).

The key system modules and functionalities are:

1. Gantry
   The Gantry consists of 4 main internal units:
   a. X-Ray Tube – produces X-rays necessary for scanning.
   b. High voltage generator - produces high voltage power supply to X-ray tube, consists of system Interface Unit, Power Block Unit and Anode Drive Unit.
   c. A-plane: adjusts the slice thickness during axial scan and monitor the changes of X-ray.
   d. DMS (Data Management System) – absorbs X-ray radiation by detectors and converts it to digital readout.
2. Patient Table (Couch)
   The Couch is used to position the patient. Carries the patient in and out through the Gantry bore synchronized with the scan.
3. Console
   The console is used to operate the system and monitor the scan. The Operator console includes a computer, monitors and CTBOX.
4. CT on Trailer Kit
   The CT 5300 installed and secured on a trailer requires locking motion parts during trailer transportation and unlocking motion parts before CT operations. Besides being installed in hospital, the CT may also be installed on trailer to be transported to designated locations for use within a professional healthcare environment.

The CT 5300 on Trailer Kit has the same fundamental design characteristics and technologies as the current marketed Philips Incisive CT on trailer (K211168 - November 22, 2021). The CT on Trailer configuration is identical to the K211168 trailer configuration. The CT system should only be used in designated locations for use with appropriate radiation controls and safety measures.

In addition to the above components and the software operating them, each system includes hardware and software for data acquisition, display, manipulation, storage and filming as well as post-processing into views other than the original axial images.

Upgrades Kit is available to upgrade earlier Incisive CT installations to latest version.

The provided document is a 510(k) Premarket Notification from Philips Healthcare (Suzhou) Co., Ltd. for their CT 5300 Computed Tomography X-Ray System. The purpose of this document is to demonstrate "Substantial Equivalence" to a legally marketed predicate device (Philips Incisive CT, K212441) and thus does not include acceptance criteria or detailed study results for standalone AI/ML device performance.

Instead, the document focuses on showing that the new CT 5300 system (which incorporates an updated version of the "Precise Image" and "Precise Position" algorithms) is as safe and effective as the predicate device.

However, based on the limited information provided regarding the "Precise Image" algorithm update, I can attempt to extract the relevant details and structure them according to your request, while highlighting what information is not present.

Please note: This document's primary goal is to establish substantial equivalence of the entire CT system, not to provide a detailed clinical validation study for a new AI/ML algorithm that is the subject of separate regulatory submissions (like a De Novo or full PMA). The "Precise Image" and "Precise Position" modifications are treated as part of the overall system's equivalence demonstration.

Device: CT 5300 (incorporating updated Precise Image and Precise Position algorithms)

Study Proving Device Meets Acceptance Criteria (as described for the specific algorithms):

The document states: "Non-Clinical verification and or validation tests have been performed... Non-Clinical verification and or validation test results demonstrate that the proposed device... Meets the acceptance criteria and is adequate for its intended use."

Specifically for "Precise Image": "Precise Image (K210760) was modified for use in the CT 5300 system. With no changes to the algorithm architecture, new models were introduced to enable the reconstruction of a new organ type (cardiac), support more slice thickness and increment combinations, a new scan mode (high resolution head), and more clinical scenarios for body and head. All models were adequately trained and successfully compared using half-dose Precise Image reconstructions with full-dose iDose4 reconstructions. The comparative image quality assessment using phantoms demonstrated acceptable performance for the new models used in Precise Image. Additionally, a comparative image evaluation by two US Board Certified Radiologists of 126 image set pairs (including cases with pathology) comprising 31 unique patients representing the newly supported reconstructions. The comparative image assessment demonstrated that half-dose images processed by Precise Image in CT 5300, including both new and original existing models, are of equal or greater diagnostic quality compared to full dose images processed by iDose4. The comparative external image assessment confirms the validity of successful bench testing and clinical image quality evaluations, and when taken together, demonstrate Precise Image in CT 5300 to be as safe and effective as the predicate, and thus substantially equivalent to Precise Image (K210760) in predicate Incisive CT (K212441)."

For "Precise Position": "Precise Position (originally cleared in K203514) was modified for use in the CT 5300 with no change to the design of the AI algorithm, the body joints detection algorithm including CNN architecture, model parameters, inference pipeline, pre- and post-processing is same as what is used in the predicate Incisive CT. The original model was trained using a broad dataset and performance data using clinical images demonstrate the model can further support more exams (cardiac, spine, runoff)."

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Precise Image:

  • Clinical Image Evaluation: 126 image set pairs "comprising 31 unique patients".
  • Data Provenance: Not explicitly stated, but "two US Board Certified Radiologists" implies the data used for the reading study was likely relevant to US clinical practice. It is mentioned as "retrospective clinical data" in the "Summary of Clinical Data" section (Page 10), but further details on geographical origin or specific institutions are not provided. The study is described as a "comparative image evaluation," which is by nature retrospective.
  • Phantom Study: Not specified (number of phantoms/scans).

• Precise Position:

  • Clinical Images: "performance data using clinical images" was used, but the specific sample size of images/patients for this evaluation is not provided.
  • Data Provenance: Not explicitly stated, but the "original model was trained using a broad dataset," suggesting varied provenance. The evaluation here uses "clinical images" and is implicitly retrospective.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Precise Image:

  • Number of Experts: Two (2)
  • Qualifications: "US Board Certified Radiologists"
  • Experience: Not specified (e.g., number of years of experience).

• Precise Position: Not explicitly stated for performance evaluation, but the "original model was trained using a broad dataset" which often implies some form of expert annotation or clinical data used as ground truth during training. For the evaluation of its extended support, it's mentioned that the model was tested with "performance data using clinical images," but details on expert review for this test set are not provided.

4. Adjudication Method for the Test Set

• Precise Image: The document describes a "comparative image evaluation by two US Board Certified Radiologists." It does not specify an adjudication method (e.g., 2+1, 3+1, or consensus reading). It's possible they read independently and their findings were compared, or they may have reached a consensus without formal arbitration by a third party.

• Precise Position: No details on expert review or adjudication method for the specified performance evaluation are provided.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done, What was the Effect Size of How Much Human Readers Improve with AI vs Without AI Assistance

• No, a traditional MRMC comparative effectiveness study was not performed as described for human readers with/without AI assistance, in the sense of measuring diagnostic accuracy improvement.
• Instead, for "Precise Image," a comparative image quality assessment was performed between two different reconstruction methods: "half-dose Precise Image reconstructions" (new algorithm) vs. "full-dose iDose4 reconstructions" (predicate algorithm). The goal was to show non-inferiority or superiority of the image quality from the new algorithm at a reduced dose, rather than measuring reader performance improvement with AI assistance.
• The effect size related to reader performance is not applicable in this context as the study was about image quality comparison, not AI-assisted human reading. The conclusion was that the image quality was "equal or greater," which implicitly means readers can perform at least as well with the new images.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done

• Yes, for "Precise Image," a standalone image quality assessment was performed using phantoms. This is described as "The comparative image quality assessment using phantoms demonstrated acceptable performance for the new models used in Precise Image." This evaluates the algorithm's output (image quality) independent of human interpretation of complex clinical cases.
• For "Precise Position," it is a "body joints detection algorithm." While it's part of a workflow to assist patient positioning, a standalone performance metric (e.g., accuracy of joint detection) is not explicitly provided in this document, only that the "original model was trained using a broad dataset" and "performance data using clinical images demonstrate the model can further support more exams."

7. The Type of Ground Truth Used

• Precise Image:

  • Phantom Study: Ground truth would be based on the known characteristics of the phantom and imaging parameters, allowing for objective image quality metrics (e.g., spatial resolution, noise, contrast).
  • Clinical Image Evaluation: The ground truth for comparative diagnostic quality seems to be based on the "full-dose iDose4 reconstructions" themselves and inclusion of "cases with pathology." This implies that the full-dose images (processed by the predicate's iDose4 algorithm) were considered the reference truth against which the new algorithm's images were judged for diagnostic quality. The mention of "cases with pathology" likely means these cases had confirmed diagnoses, which serve as the ultimate ground truth for comparison.

• Precise Position: The ground truth for the training set would likely be human-annotated or verified positions of body joints on CT images. For the evaluation, it's implied that the "clinical images" and the intended "support more exams" served as the basis for performance verification.

8. The Sample Size for the Training Set

• Precise Image: The document states "All models were adequately trained." However, the specific sample size (number of images/patients) used for training the "new models" for Precise Image is not provided.
• Precise Position: The document states "The original model was trained using a broad dataset." The specific sample size (number of images/patients) used for training is not provided.

9. How the Ground Truth for the Training Set was Established

• Precise Image: The document states "All models were adequately trained." For deep learning image reconstruction like Precise Image, training typically involves paired low-dose and standard-dose (or high-quality) images, where the standard-dose images serve as the ground truth reference for the algorithm to learn how to enhance low-dose images. This is inferred but not explicitly stated in the provided text.
• Precise Position: The document states "The original model was trained using a broad dataset." For a "body joints detection algorithm," ground truth for training would typically be established by expert (e.g., radiologist or trained annotator) manual annotation of anatomical landmarks or joint locations on a large dataset of CT images. This is inferred but not explicitly stated in the provided text.

Ask a specific question about this device

The Smart Fit Knee 3.0T coil is designed to be used in conjunction with a Philips 3.0T MR system to produce diagnostic images of the Knee that can be interpreted by a trained physician.

The Smart Fit Knee 3.0T coil is designed to be used in conjunction with a Philips 3.0T MR system to produce diagnostic images of the Knee that can be interpreted by a trained physician.
The Smart Fit Knee 3.0T is a 16-element coil designed for high-resolution imaging of the left or right knee. It is a phased-array, Rx volume coil providing an integrated solution with a base plate, an anterior and a posterior part. Positioning pads are also supplied to support comfortable positioning. The coil can be slightly rotated relative to its base plate to ease coil setup and enhance patient comfort. The coil is used independently and cannot be combined with any other coils. This coil is available for 3.0T MR systems and is compatible with Philips 3.0T MR Scanners.

The provided text describes the Philips Smart Fit Knee 3.0T MRI coil, but it does not contain acceptance criteria for device performance or a detailed study proving the device meets specific performance criteria through clinical effectiveness measures.

Instead, the document focuses on demonstrating substantial equivalence to a predicate device (HRK-127-8 KNEE ARRAY COIL K033567) for regulatory clearance. This is primarily done through technological comparisons and compliance with recognized consensus standards.

Here's a breakdown of the information available and what is missing based on your request:

1. Table of Acceptance Criteria and Reported Device Performance

• Acceptance Criteria: Not explicitly stated as quantifiable performance metrics for clinical effectiveness. The acceptance criteria in this document are implicitly related to compliance with recognized standards and showing similarity to the predicate device in design, intended use, and fundamental scientific technology.
• Reported Device Performance:
  • "The performance test results demonstrate that the proposed Smart Fit Knee 3.0T meets the acceptance criteria and is adequate for its intended use." This is a general statement of compliance, not a report of specific numerical performance metrics.
  • "All clinical images on the proposed coils Smart Fit Knee 3.0T were evaluated by qualified radiologists. No issues with the clinical image quality were seen and images were considered have sufficient quality for diagnostic use." This broadly indicates acceptable image quality based on expert review, but no quantifiable performance metrics (e.g., sensitivity, specificity, SNR values) are provided.

Table based on available information (focused on equivalence and general performance):

2. Sample size used for the test set and the data provenance:

• The document states that "All clinical images on the proposed coils Smart Fit Knee 3.0T were evaluated," but it does not specify the sample size (number of patients or knee images) for this clinical image quality evaluation test set.
• Data Provenance: Not explicitly stated. Given that Philips Healthcare (Suzhou) Co., Ltd. is based in China, and the contacts appear to be from China and the Netherlands, the images could originate from various countries where Philips systems are used. The document does not specify if the data was retrospective or prospective.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Number of experts: Not specified beyond "qualified radiologists."
• Qualifications of experts: Only stated as "qualified radiologists." No specific details such as years of experience, subspecialty (e.g., musculoskeletal radiologist), or board certification are provided.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

• The document mentions that clinical images were "evaluated by qualified radiologists." It does not specify an adjudication method (e.g., how disagreements were resolved if multiple radiologists reviewed the same images). It might imply a consensus reading or individual review without further adjudication.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No MRMC comparative effectiveness study was done. The document explicitly states: "The proposed Smart Fit Knee 3.0T did not require clinical study since substantial equivalence to the legally marketed predicate device was demonstrated in comparison."
• This device is an MRI coil, a hardware component, not an AI-powered diagnostic algorithm. Therefore, "human readers improve with AI vs without AI assistance" is not applicable.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

• Not applicable. This is an MRI coil, not an algorithm. Performance is assessed on its ability to produce diagnostic images, which are then interpreted by humans.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

• For image quality, the ground truth was expert opinion/review by qualified radiologists. Their determination that "No issues with the clinical image quality were seen and images were considered have sufficient quality for diagnostic use" served as the basis for performance.

8. The sample size for the training set:

• Not applicable. This device is a passive MRI coil (hardware). It does not involve machine learning algorithms that require a training set.

9. How the ground truth for the training set was established:

• Not applicable, as there is no training set for this hardware device.

In summary, the provided document is a 510(k) summary focused on demonstrating substantial equivalence of a new MRI coil to a predicate device, primarily through non-clinical testing for compliance with standards and a general statement about image quality review by radiologists, rather than a clinical study with detailed performance metrics and ground truth establishment as would be expected for an AI/software device.

Ask a specific question about this device

The Smart Fit TorsoCardiac 1.5T coil is designed to be used in conjunction with a Philips 1.5T MR system to produce diagnostic images of the torso (including chest, abdomen, pelvis), head and neck and heart that can be interpreted by a trained physician.

The Smart Fit Shoulder 1.5T coil is designed to be used in conjunction with a Philips 1.5T MR system to produce diagnostic images of the Shoulder that can be interpreted by a trained physician.

The proposed of Smart Fit Shoulder 1.5T and Smart Fit TorsoCardiac 1.5T are intended to be used in conjunction with a Philips MR-system to enable trained physicians to obtain cross-sectional images of the internal structure of the head, body, or extremities, in any orientation. These images, when interpreted by a trained physician, provide information that may assist diagnosis and therapy planning.

The proposed Smart Fit Torsocardiac 1.5T is a phased array receive-only coil for high resolution diagnostic imaging of the torso (including chest, abdomen, pelvis), head and neck and heart. The coil foam is composed of PU foil, flexible PCB and EVA to provide sufficient flexibility along Left-Right direction for patient body scan. The layers from outside to patient side are: PU foil (outer surface), EVA30 foam, PCBA of the coil and PU foil (inner surface). The foam looks flat at the top surface. A few parts, two feed- board boxes, cable housing and a small connector placed across the central Head-Feet axis are also at the top surface. Inner surface is naturally flat and is bendable along slots to fit well to the patient body.

The proposed Smart Fit Shoulder 1.5T is a phased array receive-only coil for high resolution diagnostic imaging of shoulder. The coil foam is composed of PU foil, flexible PCB and EVA to provide sufficient flexibility along Anterior-Posterior direction for patient shoulder scan. The layers from outside to patient side are: PU foil (outer surface), EVA30 foam, PCBA of the coil, EVA 30 foam, and PU foil (inner surface). A few parts, feed-board boxes, cable housing and a small connector placed across the Head-Feet axis are also at the outer surface. Inner surface is naturally flat and is bendable along slots to fit well to the patient body.

The provided document is a 510(k) Summary of Safety and Effectiveness for Philips Healthcare's Smart Fit TorsoCardiac 1.5T and Smart Fit Shoulder 1.5T MRI coils. It focuses on demonstrating substantial equivalence to predicate devices rather than proving performance against specific acceptance criteria for an AI/ML-driven device through a multi-reader, multi-case study.

Therefore, many of the requested details regarding acceptance criteria for an AI/ML device, ground truth establishment, sample sizes for training/test sets, expert adjudication methods, and MRMC studies are not applicable or explicitly mentioned in this document as it pertains to traditional medical device clearance rather than AI/ML software. The document asserts that "The proposed Smart Fit TorsoCardiac 1.5T and Smart Fit Shoulder 1.5T did not require clinical study since substantial equivalence to the legally marketed predicate device was proven in the comparison in terms of safety and effectiveness."

However, I can extract the information that is present and indicate what is not applicable.

Acceptance Criteria and Device Performance (based on provided text, primarily regarding equivalence to predicate devices and general performance, not AI/ML specific metrics)

Study Information (based on provided text):

1. Sample sizes used for the test set and the data provenance:

   • Test set sample size: Not explicitly stated as a distinct "test set" for performance evaluation in the context of an AI/ML algorithm (which is not this device). However, the document mentions "All clinical images on the proposed coils Smart Fit TorsoCardiac 1.5T and Smart Fit Shoulder 1.5T were evaluated by qualified radiologists." This implies a set of clinical images used for evaluation. The number of images or patients is not provided.
   • Data Provenance: Not specified (e.g., country of origin). The study is descriptive, focusing on demonstrating equivalence to predicates via technical characteristics, standards compliance, and subjective review of image quality rather than analyzing data from a specific patient cohort. The clinical image evaluation mentioned is a retrospective review of images generated by the new coils.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Number of experts: Not specified beyond "qualified radiologists".
   • Qualifications: "qualified radiologists". No specific years of experience or board certifications are detailed in this summary.
   • Note: This is not an AI/ML device that generates a "ground truth" for classification or detection. The radiologists are evaluating the diagnostic quality of the images produced by the new coils.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

   • Not specified. The document only states that images "were evaluated by qualified radiologists." It does not detail any consensus or adjudication process for image quality assessment.

4. If a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done:

   • No, an MRMC study was NOT done. The document explicitly states: "The proposed Smart Fit TorsoCardiac 1.5T and Smart Fit Shoulder 1.5T did not require clinical study since substantial equivalence to the legally marketed predicate device was proven in the comparison in terms of safety and effectiveness." MRMC studies are typically performed for AI/ML devices to assess reader performance with and without AI assistance; this device is an MRI coil, not an AI/ML algorithm.
   • Effect size of human readers improvement with AI vs. without AI assistance: Not applicable, as no AI assistance is involved with these MRI coils, and no such study was performed.

5. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:

   • Not applicable. This device is an MRI coil, which is a hardware component, not a standalone algorithm.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • Not applicable in the context of an AI/ML algorithm generating diagnostic output. For this device (an MRI coil), the "ground truth" is implicitly tied to the expectation that the images produced are of sufficient diagnostic quality as determined by "qualified radiologists" (expert opinion on image quality) and meet established technical performance standards (NEMA, IEC, AAMI). There's no disease pathology ground truth being established/compared for an AI model.

7. The sample size for the training set:

   • Not applicable. This is not an AI/ML device that requires a training set.

8. How the ground truth for the training set was established:

   • Not applicable. This is not an AI/ML device.

Ask a specific question about this device

The Incisive CT is a Computed Tomography X-Ray System intended to produce images of the head and body by computer reconstruction of x-ray transmission data taken at different angles and planes. These devices may include signal analysis and display equipment, patient and equipment supports, components and accessories. The Incisive CT is indicated for head, whole body, cardiac and vascular X-ray Computed Tomography applications in patients of all ages.

These scanners are intended to be used for diagnostic imaging and for low dose CT lung cancer screening for the early detection of lung nodules that may represent cancer*. The screening must be performed within the established inclusion criteria of programs / protocols that have been approved and published by either a governmental body or professional medical society.

*Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information.

The proposed Philips Incisive CT is a whole-body computed tomography (CT) X-Ray System featuring a continuously rotating x-ray tube, detectors, and gantry with multi-slice capability. The acquired x-ray transmission data is reconstructed by computer into cross-sectional images of the body taken at different angles and planes. This system also includes signal analysis and display equipment, patient and equipment supports, components, and accessories. The Philips Incisive CT has a 72cm bore and includes a detector array that provides 50cm scan field of view (FOV).

The main components (detection system, the reconstruction algorithm, and the x-ray system) that are used in the proposed Philips Incisive CT have the same fundamental design characteristics and are based on comparable technologies as the current market predicate Philips Ingenuity CT (K160743, 08/08/2016).

The main system modules and functionalities are:

1. Gantry. The Gantry consists of 4 main internal units:
   a. Stator - a fixed mechanical frame that carries HW and SW
   b. Rotor - A rotating circular stiff frame that is mounted in and supported by the stator.
   c. X-Ray Tube (XRT) and Generator, - fixed to the Rotor frame
   d. Data Measurement System (DMS) – a detectors array, fixed to the Rotor frame
2. Patient Support (Couch) - carries the patient in and out through the Gantry bore synchronized with the scan
3. Console - Containing a Host computer and display that is the primary user interface.

In addition to the above components and the software operating them, each system includes hardware and software for data acquisition, display, manipulation, storage and filming as well as post-processing into views other than the original axial images. Patient supports (positioning aids) are used to position the patient.

The provided text is a 510(k) Summary of Safety and Effectiveness for the Philips Incisive CT, a computed tomography x-ray system. The document focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed acceptance criteria and performance data for an AI/CADe device.

Therefore, the information required to fully answer your request regarding acceptance criteria and a study proving an AI device meets those criteria (especially points 3, 4, 5, 7, 8, 9) is largely missing from this specific document. The document describes a traditional CT scanner, not an AI feature or independent AI device.

However, I can extract the relevant information that is present and note the missing parts:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria here are based on demonstrating substantial equivalence to predicate devices in terms of technical characteristics and imaging features. The "performance" is implicitly the device meeting, matching, or improving upon these characteristics without raising new questions of safety or effectiveness.

2. Sample size used for the test set and the data provenance

The document states: "The proposed Philips Incisive CT did not require clinical study since substantial equivalence to the legally marketed predicate device was proven with the verification/validation testing."

This means there was no dedicated clinical test set in the traditional sense of patient data used for performance claims. The "testing" appears to be primarily engineering verification and validation against design specifications and industry standards, and comparison to the technical specifications of a predicate device. Therefore, no specific sample size, country of origin, or retrospective/prospective nature of a clinical test set is provided.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

As there was no clinical study and no clinical test set for independent performance evaluation (rather, it was a substantial equivalence submission based on technical and functional comparison), this information is not applicable/provided. Ground truth establishment by experts for specific clinical findings is not described.

4. Adjudication method for the test set

Not applicable as no clinical test set and ground truth establishment by experts are described.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. The submission is for a CT scanner itself, not an AI-assisted reading tool. While some newer "Precise" features mention deep learning (e.g., "Precise image reconstruction"), the document explicitly states these are "Different" and refers to other 510(k) clearances (K210760, K203020, K203514) for their safety and effectiveness. This document does not contain the MRMC study details for those specific AI features.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done

Not applicable for the CT scanner itself. For the "Precise" features that hint at AI, the performance data is referenced in other submissions, not detailed here.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

Not applicable for this submission's stated "verification/validation testing" which focuses on technical specifications and functional equivalence to predicates.

8. The sample size for the training set

Not applicable, as this document does not describe the development or training of an AI model.

9. How the ground truth for the training set was established

Not applicable, as this document does not describe the development or training of an AI model.

Summary of what the document does provide:

The document describes the Philips Incisive CT as a Computed Tomography X-Ray System. Its acceptance criteria and proving methodology revolve around demonstrating substantial equivalence to existing, legally marketed predicate CT devices (Philips Ingenuity CT K160743 and Philips Incisive CT K180015). This is achieved through:

• Comparison of technical specifications: Scan characteristics (slices, scan modes, scan time, resolution, noise, slice thickness, FOV, image matrix, display, infrastructure, communication, dose reporting).
• Comparison of imaging features/functionalities: A comprehensive list of features like 2D Viewer, MPR, 3D, O-MAR, Dose Modulation, Bolus Tracking, various analysis applications (Lung Nodule, Cardiac Artery, etc.), and others.
• Compliance with recognized consensus standards: A list of international and FDA-recognized standards (e.g., IEC 60601 series, IEC 62304, ISO 14971, NEMA XR standards) which the device is stated to comply with.
• Non-clinical design verification and validation testing: The document briefly states that "The systems pass the design verification, design validation and consensus standards test as nonclinical tests. The system verification is conducted against the system requirement specifications (SRS). ... Non-Clinical design validation testing covered the intended use and commercial claims. Validation testing included workflow validation."

The core assertion for the Philips Incisive CT's acceptance is that its design, intended use, technology, and principal technological components are substantially equivalent to the predicate devices, and that the product's differences do not raise new questions of safety or effectiveness. For specific features that appear to involve AI (like "Precise image reconstruction"), the document explicitly points to other 510(k) submissions, indicating that their clinical performance and acceptance would be detailed there.

Ask a specific question about this device

The Incisive CT is a Computed Tomography X-Ray System intended to produce images of the head and body by computer reconstruction of x-ray transmission data taken at different angles and planes. These devices may include signal analysis and display equipment, patient and equipments and accessories. The Incisive CT is indicated for head, whole body, cardiac and vascular X-ray Computed Tomography applications in patients of all ages.

These scanners are intended to be used for diagnostic imaging and for low dose CT lung cancer screening for the early detection of lung nodules that may represent cancer*. The screening must be performed within the established inclusion criteria of programs / protocols that have been approved and published by either a governmental body or professional medical society.

Besides installed in hospital, the proposed Philips Incisive CT may also be installed on trailer and be transported to designated locations for use. And Incisive CT installed on trailer has the same intended use as installed in hospital.

The proposed Philips Incisive CT on Trailer is a whole-body computed tomography (CT) X-Ray System featuring a continuously rotating x-ray tube, detectors, and gantry with multi-slice capability. The acquired x-ray transmission data is reconstructed by computer into cross-sectional images of the body taken at different angles and planes. This system also includes signal analysis and display equipment, patient and equipment support, components, and accessories. The Philips Incisive CT has a 72cm bore and includes a detector array that provides 50cm scan field of view (FOV).

Besides installed in hospital, Philips Incisive CT can also be installed on trailer and be transported to designated locations.

The main components (detection system, the reconstruction algorithm, and the x-ray system) that are used in the proposed Philips Incisive CT on trailer are identical to the currently marketed and predicate Philips Incisive CT (K180015, 20/March/2018).

The components of the proposed Philips Incisive CT on trailer include the following:

1. Gantry. The Gantry consists of 4 main internal units:
   a. Stator – a fixed mechanical frame that carries HW and SW
   b. Rotor – A rotating circular stiff frame that is mounted in and supported by the stator
   c. X-Ray Tube (XRT) and Generator, - fixed to the Rotor frame
   d. Data Measurement System (DMS) - a detectors array, fixed to the Rotor frame
2. Patient Support (Couch) - carries the patient in and out through the Gantry bore synchronized with the scan.
3. Console - Containing a Host computer and display that is the primary user interface.
4. CT on Trailer Kit - Modified Incisive CT installed and secured on trailer requires locking motion parts during trailer transportation and unlocking motion parts before CT operations.

In addition to the above components and the software operating them, each system includes hardware and software for data acquisition, display, manipulation, storage and filming as well as post-processing into views other than the original axial images. Patient supports (positioning aids) are used to position the patient.

The provided document is a 510(k) premarket notification for the Philips Incisive CT on Trailer. It seeks to demonstrate substantial equivalence to a predicate device, the Philips Incisive CT (K180015). This type of submission focuses on showing that the new device is as safe and effective as a legally marketed predicate device, rather than on providing extensive clinical performance data for an AI algorithm.

Therefore, the document does not contain the detailed information necessary to answer all parts of your request, especially those pertaining to AI/algorithm-specific studies. The device described is a CT scanner, not an AI algorithm for image analysis.

However, I can extract the relevant information regarding the device's performance criteria based on the context provided for a traditional 510(k) submission for an imaging device.

Here's an analysis based on the document:

1. Table of Acceptance Criteria and Reported Device Performance

The document details that the "Philips Incisive CT on Trailer" is essentially the predicate "Philips Incisive CT" (K180015) installed on a trailer. The justification for substantial equivalence relies heavily on the fact that the core CT system (detection system, reconstruction algorithm, x-ray system) is identical to the predicate.

The primary performance evaluation mentioned, specifically for the "on Trailer" aspect, relates to robustness and image quality after transportation.

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: Not explicitly stated as a number of patient cases. The "test set" in this context refers to the physical device undergoing vibration and QA tests. The tests were performed on "the proposed Philips Incisive CT on Trailer." This likely refers to one or more physical units of the device.
• Data Provenance: The testing was conducted by Philips Healthcare (Suzhou) Co., Ltd. within their development/validation process in China. The data is prospective in the sense that it's generated from testing the new "on Trailer" configuration.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications

• This question is not applicable in the context of this 510(k) submission. This filing is for a physical CT device (mobile CT scanner) and its ability to maintain performance after transport, not for an AI algorithm that generates a "ground truth" often established by expert readers. The "ground truth" here is the physical performance of the CT scanner measured by established imaging quality metrics.

4. Adjudication Method for the Test Set

• Not applicable for this type of device submission. Adjudication generally refers to expert consensus in interpreting medical images or data. The performance of the CT scanner is measured objectively (e.g., noise levels, spatial resolution) against pre-defined engineering and image quality specifications.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and its effect size

• No, an MRMC study was not done. This type of study is relevant for AI algorithms intended to assist human readers in diagnostic tasks. The device in focus is the CT scanner itself, not an AI software for image interpretation.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done

• No, a standalone AI algorithm performance study was not done. This 510(k) is for the CT imaging system, not an AI diagnostic algorithm.

7. The Type of Ground Truth Used

• For the physical performance of the CT scanner (MeanCT, Uniformity, Noise, Spatial Resolution, Slice Thickness, Linearity, Low Contrast Resolution), the "ground truth" is established by engineering specifications and phantoms/test objects yielding objective physical measurements, rather than clinical outcomes or expert consensus on patient images. The claim is that these measurements are "same as the predicate device," implying the predicate's performance serves as the benchmark.

8. The Sample Size for the Training Set

• Not applicable. This document is for a CT scanner hardware device (with integrated software for image acquisition and reconstruction), not a machine learning model that requires a "training set" of data.

9. How the Ground Truth for the Training Set was Established

• Not applicable. See point 8.

Ask a specific question about this device

The Precise Position is intended for use with Philips Incisive CT systems. The following guided workflow.

• Patient orientation identification
• Surview range recommendation
• Automatic centering the patient anatomy
• Provide visual images of patient on the table

Precise position is indicated for use for CT imaging of the head, chest, abdomen, pelvis, and combination of those anatomies.

Patient population limitation: Patient younger than 16 years are not supported.

Precise Position is an optional feature to assist user for position the patient before the body examination such as CT scan. The purpose of this feature is to reduce the patient position time via the camera detection and calculation result. It includes automatic detect patient orientation, patient anatomy scan range and center of patient anatomy.

Precise Position including a camera with both color and depth function is installed in the ceiling of the scan room, in such a way to cover the entire patient on the patient table. The camera control and image data transmit via the high speed fiber and copper hybrid USB cable. The power supply of the camera is from the gantry. Precise position adopts the AI algorithm (Convolution Neural Network) to detect the joints of the patient body, and then identify surview start/end position and patient orientation. The algorithm can also support detect center of patient anatomy.

Limitation for Precise Position
There is no limitation for Precise Position except below items:
• Patients below the age of 16 are not supported.
• Decubitus orientations are not supported.

The Precise Position display results may get affected by the following conditions:
• When the patient is covered by sheet, blanket etc.,
• When the patient is not completely covered by the ceiling camera view, e.g. blocked by the gantry or out of camera's FOV etc.
• When the patient is wearing clothes that reflects light, e.g. plastic-like clothes.
• When the patient is wearing black clothes.
• When the patient is wearing thick clothes.
• When there are other people around the patient.

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Total 80 clinical scan positions.
  • 40 cases used with Precise Position.
  • 40 cases without the usage of Precise Position.
• Data Provenance: The document does not explicitly state the country of origin for the data or if it was retrospective or prospective. However, it mentions a "clinical evaluation… done by 5 Clinical experts" and volunteers not receiving radiation, implying a prospective study conducted for the purpose of this evaluation. The manufacturer is Philips Healthcare (Suzhou) Co., Ltd., which suggests the study likely occurred in China.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

• Number of Experts: 5 Clinical experts.
• Qualifications: The specific qualifications (e.g., number of years of experience, specific specialty like "radiologist") are not explicitly stated beyond "Clinical experts."

4. Adjudication Method for the Test Set

• The document does not specify an explicit adjudication method (e.g., 2+1, 3+1). It states that the "thorough clinical evaluation of this feature is done by 5 Clinical experts," implying they collectively contributed to the evaluation, but the exact consensus or adjudication process is not detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

• This was not a typical MRMC comparative effectiveness study in the sense of multiple readers interpreting cases with and without AI.
• Instead, it was a comparative study on operational efficiency and positioning accuracy. It compared:
  • Time taken for patient positioning by users with and without the Precise Position feature.
  • Accuracy of vertical (iso)center positioning by users with and without the Precise Position feature.
  • Consistency (standard deviation) of positioning by users with and without the Precise Position feature.
• Effect Size (Human Improvement with AI):
  • Time Reduction: Users achieved up to 23% time reduction in patient positioning with the "Precise Positioning workflow" (which includes the AI).
  • Vertical Position Accuracy: Users achieved an increase of up to 50% in vertical position accuracy with "Precise Position."
  • Positioning Consistency (Vertical and Horizontal): Users achieved an increase of up to 70% in consistency with "Precise Position."
    These metrics indicate the improvement in human operators' performance when assisted by the AI-powered Precise Position device.

6. If a Standalone (Algorithm Only) Performance Study Was Done

• The document does not explicitly state whether a standalone (algorithm only, without human-in-the-loop) performance study was conducted for the AI component of the Precise Position device. The clinical evaluation focuses on the human-with-AI system performance. The AI algorithm (Convolution Neural Network) is described as being used to detect joints and then determine positioning parameters, suggesting its performance is evaluated as part of the overall integrated system.

7. The Type of Ground Truth Used

• For the clinical evaluation, the ground truth for measuring time, accuracy, and consistency appears to be based on:
  • Direct measurements of time taken for positioning.
  • Measurements of offset in mm for vertical (iso) center position.
  • Standard deviation calculations for vertical (iso)center positioning and surview (horizontal) start position.
  • These measurements were likely compared against an ideal or intended positioning, which would be implicitly defined by the CT system's requirements and presumably verified by the clinical experts. It's not "pathology," "outcomes data," or a direct "expert consensus" on disease presence/absence, but rather a consensus on the correctness and optimal nature of the patient positioning parameters established by the device.

8. The Sample Size for the Training Set

• The document does not provide the sample size for the training set used for the AI algorithm (Convolution Neural Network). It only discusses the test set used for validating the combined human-AI system.

9. How the Ground Truth for the Training Set Was Established

• The document does not describe how the ground truth for the training set of the AI algorithm was established. It only mentions that the AI algorithm (Convolution Neural Network) is used to detect "joints of the patient body" to then identify surview start/end position and patient orientation, and support detection of the center of patient anatomy. This implies annotation of patient body parts and anatomical landmarks in imaging data for training purposes, but specific details on its establishment are not provided.

Ask a specific question about this device

The DuraDiagnost is intended for use in generating radiographic images of human anatomy by qualified/trained doctor or technician. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position. This device is not intended for mammographic applications.

The DuraDiagnost is a flexible digital radiography (DR) system that is designed to provide fast and smooth radiography examinations of sitting, standing or lying patients.
The DuraDiagnost consist of the following components: Tube column with X-ray assembly, wall stand with detector carrier, patient table with detector carrier and floating table top, high voltage generator, and acquisition and reviewing workstation for post-processing, storage and viewing of images. Images may be transferred via a DICOM network for printing, storage and detailed review.

The provided text describes a 510(k) premarket notification for the Philips DuraDiagnost, an X-ray system. The submission focuses on demonstrating substantial equivalence to a predicate device (DuraDiagnost K141381), rather than proving the performance of a novel AI algorithm. Therefore, many of the requested details, such as acceptance criteria for AI performance metrics, sample sizes for test sets, expert adjudication methods for AI ground truth, MRMC studies, standalone AI performance, and AI training set details, are not applicable to this submission.

The document primarily evaluates the DuraDiagnost against safety and effectiveness standards applicable to X-ray systems and its equivalence to a previous version of the device.

Here's the information that can be extracted, and an explanation of why other requested information is not present:

1. A table of acceptance criteria and the reported device performance

The document does not provide specific quantitative acceptance criteria for image quality or clinical performance metrics in a readily extractable table format for human or AI performance. Instead, it states that the device meets acceptance criteria by:

• Complying with international and FDA-recognized consensus standards.
• Demonstrating substantial equivalence to its predicate device in terms of design, technology, indications for use, safety, and effectiveness.

The "performance" is reported as compliance with the following standards and internal tests:

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not provided in the document as it is a 510(k) submission based on comparison to a predicate device and compliance with general safety and performance standards for X-ray systems, not specific clinical performance studies with AI. The non-clinical verification/validation tests performed would typically use test phantoms or specific equipment testing, not a "test set" of patient data in the way an AI algorithm would.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This is not applicable as the submission is not for an AI algorithm requiring clinical ground truth established by experts.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This is not applicable.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

This is not applicable. The device is an X-ray system, not an AI-powered diagnostic aide. The document explicitly states: "The DuraDiagnost does not require clinical study since substantial equivalence to the primary currently marketed and predicate device was demonstrated..." (Page 15).

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

This is not applicable. The device includes image processing software (UNIQUE 2 and SkyFlow), listed as comparable or updated versions of software present in predicate/reference devices, but it is not presented as a standalone AI diagnostic algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

This is not applicable in the context of an AI study. The "ground truth" for this submission refers to the established safety standards and the performance characteristics of the predicate device, against which the new device (DuraDiagnost) is compared. Compliance with engineering standards and performance specifications (e.g., tube voltage, focal spot size, image matrix, pixel size) serves as the "ground truth" for the device's equivalent performance to what is already on the market.

8. The sample size for the training set

This is not applicable. The document does not describe the development or training of a new AI algorithm.

9. How the ground truth for the training set was established

This is not applicable.

Ask a specific question about this device

The Access CT scanner system can be used as a Whole Body (except cardiac) Computed Tomography X-ray System featuring a continuously rotating X-ray tube and detector array with multislice capability up to 6/16 slices simultaneously. The acquired X-ray transmission data is reconstructed by computer into cross-sectional images of the same axial plane taken at different angles. The system is suitable for all patients.

The proposed Access CT is currently available in two system configurations, 6 slices and 16 slices.

The Access CT system is used clinically as a diagnostic patient imaging device that produces images that correspond to tissue density. The quality of the images depends on the level and amount of X-ray energy delivered to the tissue. CT imaging displays both high-density tissue, such as bone, and soft tissue. When interpreted by a trained physician, CT images yield useful diagnostic information. The system is intended for use in the head and whole body.

The main components (detection system, the reconstruction algorithm, and the x-ray system) that are used in the Access CT have the same fundamental design characteristics and are based on comparable technologies as the predicate.

The main system modules and functionalities are:

1. Gantry. The Gantry consists of 4 main internal units:
   a. Stator – a fixed mechanical frame that carries HW and SW.
   b. Rotor – A rotating circular stiff frame that is mounted in and supported by the stator.
   c. X-Ray Tube (XRT) and Generator – fixed to the Rotor frame.
   d. Data Measurement System (DMS) – a detectors array, fixed to the Rotor frame.
2. Patient Support (Couch) – carries the patient in and out through the Gantry bore synchronized with the scan.
3. Console - Containing a Host computer and display that is the primary user interface.

In addition to the above components and the software operating them, each system includes hardware and software for data acquisition, display, manipulation, storage and filming as well as post-processing into views other than the original axial images. Patient supports (positioning aids) are used to position the patient.

Here's an analysis of the provided text regarding the acceptance criteria and study proving the device meets those criteria:

This document is a 510(k) Premarket Notification for the Philips Access CT system. It does not present a clinical study for proving the device meets specific acceptance criteria in the context of diagnostic accuracy or AI performance. Instead, it focuses on demonstrating substantial equivalence to predicate devices based on technical specifications, performance standards, and non-clinical testing.

The "acceptance criteria" here relate to engineering and performance specifications for a CT scanner, rather than diagnostic performance of an AI algorithm. The "study" described is primarily non-clinical verification and validation testing, ensuring the device meets its design requirements and complies with relevant safety and performance standards.

Therefore, many of the requested points regarding AI performance, human-in-the-loop studies, ground truth establishment for AI, and expert adjudication are not applicable to this document as it describes a non-AI medical device (a CT scanner).

Acceptance Criteria and Device Performance (as presented in the document)

The document outlines acceptance criteria as technical specifications that the device must meet to be considered substantially equivalent to predicate devices. The "reported device performance" are the actual specifications of the Access CT that are compared against these criteria and the predicate device's specifications.

1. Table of Acceptance Criteria and Reported Device Performance

The closest representation of "acceptance criteria" and "reported device performance" in this document is found in the "Table 5-1 Design/fundamental scientific technologies Comparison" on page 8. The "Acceptance Criteria" are implicitly the values or characteristics of the "Predicate Device Philips MX 16-slice (K091195)", and the "Reported Device Performance" are the values for the "Proposed Access CT".

The document also compares against two other reference devices (Philips Ingenuity CT and Philips Incisive CT) for specific "clinical features" (Iterative recon, CTC, LNA, iPlanning, Batch image processing (iBatch)), concluding they are "Identical".

Study Details (as presented in the document, primarily non-clinical)

2. Sample Size Used for the Test Set and the Data Provenance

• Test Set Description: The document refers to "Design Verification planning and testing" and "Design validation of user needs and intended use" with "simulated use testing with production equivalent Access CT Systems."
• Sample Size: The document does not specify a sample size in terms of patient data or scanned images for non-clinical verification. It focuses on testing the system's compliance with engineering requirements and standards.
• Data Provenance: The testing was conducted internally by Philips Healthcare (Suzhou) Co., Ltd. in China ("Philips Healthcare (Suzhou) Co., Ltd., No. 258, Zhongyuan Road, Suzhou Industrial Park, Suzhou Jiangsu, CHINA, 215024"). The data stems from design verification and validation activities rather than a clinical study. The type of data is technical performance measurements, rather than patient scans with clinical findings. The document states: "The proposed Access CT System did not require any external clinical site testing."
• Retrospective or Prospective: Not applicable in the context of clinical data. The verification and validation testing would have been "prospective" in the sense that it was planned and executed for the purpose of demonstrating compliance.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

• Not Applicable: This is a submission for a CT scanner itself, not an AI algorithm that requires expert ground truth for diagnostic accuracy. The "ground truth" for the device's performance is adherence to technical specifications and safety standards, as measured by engineering tests.

4. Adjudication Method for the Test Set

• Not Applicable: There is no clinical test set requiring adjudication in this document. Adjudication methods are relevant for clinical studies where multiple human readers interpret medical images or data.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done

• No: The document explicitly states: "The proposed Access CT System did not require any external clinical site testing. Clinical evaluation of workflow was conducted via simulated use testing and is accounted for in the Summary of Non-Clinical Testing section of the summary."
• This also means there is no effect size given for human readers improving with AI vs. without AI assistance, as AI assistance is not the subject of this 510(k).

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done

• Not Applicable: This device is a CT scanner, not a standalone algorithm.

7. The Type of Ground Truth Used

• For the technical and safety performance of the CT scanner, the "ground truth" is established by international and FDA-recognized consensus standards (listed in the "Summary of Non-Clinical Performance Data" section on pages 4-5) and system design requirements. The device's performance is measured against these established standards and requirements. Examples of standards cited include AAMI / ANSI ES60601-1, IEC 60601-1-2, IEC 60601-1-3, IEC 60601-2-44, IEC 62304, IEC 62366-1, ISO14971, NEMA XR 25-2011, NEMA XR 28-2013, and NEMA XR 29-2013.

8. The Sample Size for the Training Set

• Not Applicable: This is not an AI/machine learning device submission. There is no "training set" in the context of model development.

9. How the Ground Truth for the Training Set was Established

• Not Applicable: As there is no training set for an AI model, this question is not relevant to the content of the document.

In summary, the provided document details the 510(k) submission for a CT scanner, demonstrating its substantial equivalence to previously cleared predicate devices through comparisons of technical specifications and compliance with established safety and performance standards via non-clinical verification and validation. It is not an application for an AI-powered diagnostic device, and thus, many of the questions related to AI study design, clinical efficacy, and human reader performance are not addressed.

Ask a specific question about this device

This system is a Magnetic Resonance Medical Electrical System indicated for use as a diagnostic device. The system can produce cross-sectional images, spectroscopic images and/or spectra in any orientation of the internal structure of the head, body or extremities. Magnetic Resonance images represent the spatial distribution of protons or other nuclei with spin. Image appearance is determined by many different physical properties of the tissue and the anatomy, and the MR scan technique applied. The image acquisition process can be synchronized with the patient's breathing or cardiac cycle. The systems can use combinations of images to produce physical parameters, and related derived images. Images, spectra, and measurements of physical parameters, when interpreted by a trained physician, provide information that may assist the diagnosis and therapy planning. The accuracy of determined physical parameters depends on system and scan parameters, and must be controlled and validated by the clinical user. The use of contrast agents for diagnostic imaging applications should be performed consistent with the approved labeling for the contrast agent. In addition the Philips MR systems provide imaging capabilities, such as MR fluoroscopy, to guide and evaluate interventional and minimally invasive procedures in the head, body and extremities. MR Interventional procedures, performed inside or adjacent to the Philips MR system, must be performed with MR Conditional or MR Safe instrumentation as selected and evaluated by the clinical user for use with the specific MR system configuration in the hospital. The appropriateness and use of information from a Philips MR system for a specific interventional procedure and specific MR system configuration must be validated by the clinical user.

The proposed Prodiva 1.5T CX and Prodiva 1.5T CS R5.4 are 60 cm bore 1.5 Tesla (1.5T) magnetic resonance diagnostic devices. The systems and control software are substantially equivalent to the currently marketed predicate device Ingenia 1.5T CX R5.3 (K162931, 01/06/2017). The system includes a new 1.5T magnet, new set of RF receive coils, new patient support, new in-house Gradient system and RF transmit system and adapted RF receive system which is substantially equivalent to the currently marketed predicate device Ingenia 1.5T CX R5.3.

The provided document is a 510(k) Summary of Safety and Effectiveness for the Philips Prodiva 1.5T CX and Prodiva 1.5T CS R5.4 Magnetic Resonance Diagnostic Devices. This document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a performance study with acceptance criteria in the context of an AI/algorithm-driven device.

Therefore, the requested information regarding acceptance criteria and a study proving the device meets them, especially in the context of AI performance, ground truth, sample sizes for test/training sets, experts, and MRMC studies, cannot be fully extracted as such studies are not described for this type of device submission.

Here's an attempt to answer based on the provided text, highlighting what is available and what is not:

1. Table of Acceptance Criteria and Reported Device Performance

The document states: "The verification and validation test results... demonstrate that the proposed Prodiva 1.5T CX and Prodiva 1.5T CS R5.4... Meets the acceptance criteria and is adequate for its Intended Use."
However, the specific numerical acceptance criteria for performance metrics (e.g., sensitivity, specificity, accuracy, SNR thresholds, geometric distortion limits) are not detailed in this summary document. Instead, the acceptance criteria are implicitly linked to compliance with various international and FDA-recognized consensus standards for medical electrical equipment and MR devices.

2. Sample size used for the test set and the data provenance

The document states: "The proposed Prodiva 1.5T CX and Prodiva 1.5T CS R5.4 did not require clinical study since substantial equivalence to the legally marketed predicate device was proven with the verification/validation testing."
"Clinical sample images were provided to support the ability of proposed Prodiva 1.5T CX and Prodiva 1.5T CS R5.4 to generate diagnostic quality images."

• Test Set Sample Size: Not specified. This submission relies on "sample clinical images" rather than a formal test set with a specified sample size.
• Data Provenance: Not specified. It only mentions "sample clinical images" without details on their origin (e.g., country, retrospective/prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• This is not applicable as a formal "test set" with expert-established ground truth as typically understood for AI performance evaluation is not described. The document indicates that images, spectra, and measurements are to be "interpreted by a trained physician" for diagnosis and therapy planning, but this refers to the intended use of the device, not a specific ground truth establishment for a performance study within this 510(k) submission.

4. Adjudication method for the test set

• Not applicable, as a formal test set and expert review process for ground truth establishment are not described.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• No. This is a submission for a Magnetic Resonance Diagnostic Device (hardware and software system), not an AI-assisted diagnostic algorithm. Therefore, an MRMC study comparing human readers with and without AI assistance was not performed or referenced. The document states its indications for use involve images "when interpreted by a trained physician," implying human interpretation of the device's output, not AI assistance for that interpretation.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done

• No. This is an MR system, not an algorithm being evaluated in a standalone capacity.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• Not applicable as a formal establishment of ground truth for a performance study is not described. The submission focuses on demonstrating that the MR system meets performance standards related to image quality and safety, and its output is intended for interpretation by trained physicians.

8. The sample size for the training set

• Not applicable. This is a 510(k) for an MR system, not an AI/ML algorithm that requires a training set. The "verification/validation testing" relies on compliance with established engineering and medical device standards.

9. How the ground truth for the training set was established

• Not applicable. As there is no AI/ML algorithm requiring a training set, no ground truth needed to be established in this context.

Ask a specific question about this device

The Incisive CT is a Computed Tomography X-Ray System intended to produce images of the head and body by computer reconstruction of x-ray transmission data taken at different angles and planes. These devices may include signal analysis and display equipment, patient and equipment supports, components and accessories. The Incisive CT is indicated for head, whole body, cardiac and vascular X-ray Computed Tomography applications in patients of all ages.

These scanners are intended to be used for diagnostic imaging and for low dose CT lung cancer screening for the early detection of lung nodules that may represent cancer*. The screening must be performed within the established inclusion criteria of programs / protocols that have been approved and published by either a governmental body or professional medical society.

*Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information.

The proposed Philips Incisive CT is a whole-body computed tomography (CT) X-Ray System featuring a continuously rotating x-ray tube, detectors, and gantry with multi-slice capability. The acquired x-ray transmission data is reconstructed by computer into cross-sectional images of the body taken at different angles and planes. This system also includes signal analysis and display equipment, patient and equipment supports, components, and accessories. The Philips Incisive CT has a 72cm bore and includes a detector array that provides 50cm scan field of view (FOV). The main components (detection system, the reconstruction algorithm, and the x-ray system) that are used in the proposed Philips Incisive CT have the same fundamental design characteristics and are based on comparable technologies as the current market predicate Philips Ingenuity CT (K160743, 08/08/2016). The main system modules and functionalities are: 1. Gantry. The Gantry consists of 4 main internal units: a. Stator - a fixed mechanical frame that carries HW and SW b. Rotor - A rotating circular stiff frame that is mounted in and supported by the stator. c. X-Ray Tube (XRT) and Generator,- fixed to the Rotor frame d. Data Measurement System (DMS) - a detectors array, fixed to the Rotor frame 2. Patient Support (Couch) - carries the patient in and out through the Gantry bore synchronized with the scan 3. Console - Containing a Host computer and display that is the primary user interface. In addition to the above components and the software operating them, each system includes hardware and software for data acquisition, display, manipulation, storage and filming as well as post-processing into views other than the original axial images. Patient supports (positioning aids) are used to position the patient.

The Philips Incisive CT scanner is compared to a predicate device, the Philips Ingenuity CT (K160743), for substantial equivalence. The provided document focuses on technical comparisons and non-clinical performance data rather than a clinical study with specific acceptance criteria that would typically be seen for a new AI-powered diagnostic device.

Here's an analysis of the provided information:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly state "acceptance criteria" in a quantitative sense with pass/fail thresholds for clinical performance. Instead, it demonstrates substantial equivalence to a predicate device by comparing technical specifications and imaging features. The core "acceptance criterion" is proving substantial equivalence to the predicate device, Philips Ingenuity CT (K160743).

2. Sample size used for the test set and the data provenance

The document explicitly states: "The proposed Philips Incisive CT did not require clinical study since substantial equivalence to the legally marketed predicate device was proven with the verification/validation testing."

Therefore, there is no mention of a "test set" in the context of patient data, nor any information about data provenance (country of origin, retrospective/prospective). The evaluation was based on non-clinical performance data, primarily engineering verification and validation testing, as well as comparisons to the predicate device's specifications.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Given that no clinical study was performed and no patient-based "test set" was described, there were no experts used to establish ground truth in the traditional sense of clinical outcome assessment for the Incisive CT device. The ground truth for the technical comparisons was the established performance and specifications of the predicate device.

4. Adjudication method for the test set

Not applicable, as no clinical test set requiring adjudication of findings was described.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. This is a CT scanner, not an AI-powered diagnostic software that assists human readers. The context is about the substantial equivalence of the imaging hardware and associated software functionalities.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done

Not applicable. This refers to the performance of the CT scanner itself, a hardware device with integrated software, not a separate standalone algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The ground truth for the "study" (which was non-clinical verification/validation and substantial equivalence comparison) was the established technical specifications and performance characteristics of the predicate device (Philips Ingenuity CT, K160743) and compliance with various international and FDA-recognized consensus standards.

8. The sample size for the training set

Not applicable. This device is a CT scanner, not a machine learning model that requires a training set of data in the AI sense. Its underlying technology and algorithms are based on established CT principles, and its performance is verified through engineering tests.

9. How the ground truth for the training set was established

Not applicable, for the same reason as point 8.

Ask a specific question about this device