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The DigitalDiagnost C50 system 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 DigitalDiagnost C50 Digital Radiography System (DigitalDiagnost C50) is a flexible digital radiography (DR) system that is designed to provide fast and smooth radiography examinations of sitting, standing or lying patients. The DigitalDiagnost C50 consist of the following components: ceiling suspension with X-ray assembly, wall stand with detector carrier, patient table with detector carrier and floating table top, high voltage generator, and an 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.

Here's a breakdown of the acceptance criteria and study information for the DigitalDiagnost C50, based on the provided text.

1. Table of Acceptance Criteria & Reported Device Performance:

Study Proving Acceptance Criteria:

The document states that a non-clinical verification/validation study was performed. The basis for substantial equivalence is that the DigitalDiagnost C50 is considered equivalent to the predicate DigitalDiagnost C50 (K163410) based on:

• Identical Indications for Use.
• Fundamental scientific technology (including largely identical components or components identical to other cleared reference devices).
• Non-clinical performance testing (demonstrating compliance with standards).
• Safety and effectiveness (as supported by the non-clinical testing).

The non-clinical verification and validation tests demonstrated that the DigitalDiagnost C50:

• Complies with the aforementioned international and FDA-recognized consensus standards and FDA guidance documents.
• Meets the acceptance criteria and is adequate for its intended use.

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

The document explicitly states: "The DigitalDiagnost C50 does not require clinical study..." and "Summary of Clinical Data: Based on the information provided above, the DigitalDiagnost C50 is considered substantially equivalent to the currently marketed and predicate DigitalDiagnost C50 (K163410, January 4, 2017) in terms of fundamental scientific technology."

Therefore, there appears to be no clinical test set, sample size, or specific data provenance for a clinical study as part of this 510(k) submission. The evaluation relies on non-clinical performance data and equivalence to predicate/reference devices.

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

Since no clinical study with a test set was performed, this information is not applicable and not provided in the document.

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

Since no clinical study with a test set was performed, this information is not applicable and not provided in the document.

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 mentioned. The device described, DigitalDiagnost C50, is an X-ray system, not an AI-assisted diagnostic device. Therefore, this information is not applicable and not provided in the document.

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

The DigitalDiagnost C50 is an X-ray imaging system. There is no mention of a standalone algorithm-only performance study, as it's not a standalone diagnostic algorithm. Therefore, this information is not applicable and not provided in the document.

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

For the non-clinical verification and validation tests, the "acceptance criteria" were derived from international and FDA-recognized consensus standards and FDA guidance documents (listed on pages 11-12). These standards themselves implicitly define the "ground truth" or acceptable performance ranges for device characteristics, image quality, electrical safety, radiation protection, software lifecycle, usability, and risk management. No other forms of expert consensus, pathology, or outcomes data are mentioned for establishing ground truth for the device's technical specifications.

8. The sample size for the training set:

Since no clinical study or AI algorithm training is described for this device, this information is not applicable and not provided in the document.

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

Since no clinical study or AI algorithm training is described for this device, this information is not applicable and not provided in the document.

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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.

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Intended for use by a qualified/trained doctor or technologist on both adult and pediatric patients for taking diagnostic radiographic exposures of the skull, spinal column, chest, abdomen, extremittes, and other body parts. Applications can be performed with patient sitting, standing or lying in the prone or supine positions. Not for mammography.

The MobileDiagnost wDR 2.2 is a motorized mobile radiographic system consisting of a mobile base unit, a user interface consisting of Eleva Workspot combined with flat solid state X-ray detectors used to operate, generate, process and handle digital X- ray images. The MobileDiagnost wDR 2.2 integrates a new wireless portable detector (SkyPlate E). The family of SkyPlate detectors (Large and Small) have already been integrated into the MobileDiagnost wDR 2.0 based on the K141736 pre-market submission.

The provided text describes a 510(k) premarket notification for the MobileDiagnost wDR 2.2, a mobile X-ray system. The focus of the submission is to demonstrate substantial equivalence to a predicate device, the MobileDiagnost wDR 2.0 (K141895), with several modifications. The document does not contain a clinical study to prove the device meets acceptance criteria in terms of diagnostic effectiveness or a "performance study" in the typical sense of evaluating a new diagnostic algorithm's accuracy. Instead, the submission relies on demonstrating substantial equivalence through non-clinical verification and validation tests against established standards.

Therefore, the requested information needs to be framed within this context of demonstrating substantial equivalence, rather than a traditional diagnostic performance study.

Here's the breakdown of the information based on the provided text:

Acceptance Criteria and Device Performance (within the context of Substantial Equivalence)

The acceptance criteria are implicitly defined by the compliance with recognized international and FDA consensus standards and the outcome of the comparison to the predicate device, showing that modifications do not raise new questions of safety or effectiveness. The device performance is assessed against these standards and through direct comparison of technical characteristics to the predicate.

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a substantial equivalence submission relying on technical changes and compliance with standards, the "acceptance criteria" are compliance with these standards and the "reported device performance" is the verification that these standards are met, and that the technical characteristics of the modified device are acceptably equivalent to the predicate.

• IEC 60601-1 (Edition 3.1)
• IEC 60601-1-2 (Edition 4.0)
• IEC 60601-1-3 (Edition 2.1)
• IEC 60601-1-6 (Edition 3.1)
• IEC 60601-2-54 (Edition 1.1)
• IEC 62304 (Edition 1.0:2006)
• IEC 62366 (Edition 1.0 2015)
• ISO 14971 (Edition 2.0, 2007)
• ISO 10993-1 (Edition 4.0 2009)
• IEC 60601-2-28 (Edition 2.0 2010-03)
• IEC 62220-1 (Edition 1.0 2015-03)
• NEMA PS 3.1 - 3.20 (DICOM)
• "Guidance for the Submission of 510(k)s for Solid State X-Ray Imaging Devices" (Sept 1, 2016)
• "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" (May 11, 2005)
• "Pediatric Information for X-ray Imaging Device Premarket Notifications" (Nov 28, 2017) | Meets acceptance criteria and is adequate for its intended use, demonstrating substantial equivalence. Non-clinical information deemed sufficient to support substantial equivalence. |
  | II. Technical Characteristics Equivalence (MobileDiagnost wDR 2.2 vs. MobileDiagnost wDR 2.0) | | |
  | Base Unit Type, X-ray Tube rotational capabilities, Mains Supply, Mode of Exposure, Available Exposure Methods, X-ray Absorber, Installation type, Readout Mechanism, Maximum X-ray Dose for Linear Response, Maximum Usable Dose, Maximum Lifetime Dose, Image Processing (Eleva Workstation), ADC Digitisation, Signal to Electronic Noise Ratio (SENR), Data Interface to Workstation, X-ray Tube type, material, maximum voltage, nominal focal spot, anode type, nominal anode input power, Generator configurations, Collimator operation mode, beam shape, External Connectivity (DICOM), Software Platform (Eleva WorkSpot with SkyFlow). | Same/Identical to predicate. | Identical; thus, demonstrating Substantial Equivalence (SE). |
  | Dimensions (overall, transport, source-floor distance) | Minor differences in mm measurements. | Differences do not impact safety or effectiveness. Thus, demonstrating SE. |
  | Detector Models | Addition of SkyPlate E Large (Trixell 3543DR). Previously cleared SkyPlate Large/Small retained. | Addition of SkyPlate E detector does not impact safety or effectiveness. Thus, demonstrating SE. All technical detector characteristics influencing image quality assessed per FDA guidance. |
  | Detector Weight | Max 3.1 kg (vs. Max 3 kg for predicate). | Difference has no impact on clinical workflow, safety, or effectiveness. Thus, demonstrating SE. |
  | Image Size (X-ray field) | 345.0 mm x 426.0 mm (vs. 344.8 mm x 421.2 mm for predicate). | Difference does not impact clinical Image Quality, safety, or effectiveness. Thus, demonstrating SE. |
  | Pixel Size | 160 µm (vs. 148 µm for predicate). | Difference of 12 µm pixel size does not impact image resolution "to an extent that can impact the clinical image quality," safety or effectiveness. Thus, demonstrating SE. |
  | Image matrix size (Number of pixels) | 2156 x 2662 pixels (vs. 2330 x 2846 pixels for predicate). | "Infinitesimal change" and reduction in number of pixels due to 160 µm pixel size does not impact clinical Image Quality, safety, or effectiveness. Thus, demonstrating SE. |
  | Nyquist Frequency | 3.125 lp/mm (vs. 3.38 lp/mm for predicate). | Difference does not impact clinical Image Quality, safety, or effectiveness. Thus, demonstrating SE. |
  | Modulation Transfer Function (MTF) & Detective Quantum Efficiency (DQE) | Slightly different typical values reported (e.g., MTF at 1 lp/mm: 62% vs. 61%; DQE at 3 lp/mm: 22% vs. 29%). | Differences do not impact clinical Image Quality, safety, or effectiveness. Thus, demonstrating SE. |
  | Grids | Addition of new large grids for SkyPlate E. Previously cleared grids retained. | Addition of new grids has no impact on clinical workflow, safety, or effectiveness. Thus, demonstrating SE. |
  | Column Configuration | Sliding Column (vs. Standard/Short Column for predicate). | Sliding Column provides better viewing; its introduction does not impact safety or effectiveness. Thus, demonstrating SE. |
  | System Power ON/OFF | Keyless user access (vs. Physical Key for predicate). | Keyless user access provides authenticated access; its introduction does not impact safety or effectiveness. Thus, demonstrating SE. |
  | Exposure @ zero degree column position | Exposures allowed (vs. No Exposure allowed for predicate). | Allows examinations in space-constrained situations; difference does not impact safety or effectiveness. Thus, demonstrating SE. |
  | Exposure during Charging | Exposures allowed (vs. Not allowed for predicate). | Allows charging during exam preparation; exposure energy still drawn from generator batteries, not mains. Does not impact safety or effectiveness. Thus, demonstrating SE. |
  | Handles on Collimator | Handles can be used to move both Tube head and Collimator (vs. only Tube head for predicate). | Provides ease of use; difference does not impact safety or effectiveness. Thus, demonstrating SE. |
  | Image Processing Algorithm | UNIQUE 2 (vs. UNIQUE for predicate). | UNIQUE 2 provides improved image processing (reduced noise, improved contrast) and was already cleared under K182973. Upgrading does not alter clinical workflow, safety, or effectiveness. Thus, demonstrating SE. |

Study Details (as applicable for a Substantial Equivalence submission based on non-clinical data)

The provided text clearly states: "The MobileDiagnost wDR 2.2 does not require clinical study since substantial equivalence to the primary currently marketed and predicate device MobileDiagnost wDR 2.0 (K141895- September 18, 2014) was demonstrated with the following attributes: Indication for use; Technological characteristics; Non-clinical performance testing; and Safety and effectiveness."

This means there was no "study" in the sense of a clinical trial or a diagnostic performance study to evaluate accuracy, sensitivity, specificity, etc., with human or AI readers. The "study" here refers to the comprehensive non-clinical verification and validation process against relevant standards.

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

• Sample Size: Not applicable. No clinical test set of patient data was used for a diagnostic performance evaluation. The "tests" were non-clinical verification and validation tests on the device itself (e.g., electrical safety, electromagnetic compatibility, radiation protection, software validation, usability, risk management).
• Data Provenance: Not applicable. No patient data (retrospective or prospective, from any country) was used for evaluating the device's diagnostic performance for the purpose of this 510(k) submission.

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)

• Not applicable. There was no diagnostic test set requiring ground truth established by experts.

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

• Not applicable. There was no diagnostic test set requiring 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. This device is not an AI-assisted diagnostic product but rather an X-ray imaging system with updated components and software functionalities.

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

• Not applicable. This device is an X-ray system, not a standalone diagnostic algorithm.

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

• Not applicable. There was no diagnostic performance study requiring ground truth. Ground truth for the non-clinical tests would be the established scientific/engineering principles, specifications, and regulatory standards.

8. The sample size for the training set

• Not applicable. This device is an X-ray system, not a machine learning model that requires a "training set" in the context of AI. The software (UNIQUE 2) was previously cleared (K182973), indicating its own development and validation, but details of its training are not provided here.

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

• Not applicable, as there was no training set for the device itself. For the incorporated UNIQUE 2 image processing algorithm, its ground truth establishment (if it involved machine learning) would have been part of its original 510(k) (K182973), but those details are not in this document.

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The DigitalDiagnost C90 is intended to acquire, process, store, display and export digital radiographic images. The DigitalDiagnost C90 is suitable for all routine radiography examinations, including specialist areas like intensive care, trauma or pediatric work, excluding fluoroscopy, angiography and mammography.

The DigitalDiagnost C90 is a high-end digital radiography system consisting of a height adjustable patient support table and a ceiling suspension consisting of a tube including a control handle used to acquire images with a flat panel fixed RAD detector. Additionally, different vertical stands for the radiography examinations are available. The ceiling suspension can be moved in longitudinal and lateral directions and additionally the tube can be tilted and rotated as well. The DigitalDiagnost C90 is configured with a Philips x-ray generator and a flat panel fixed RAD detector, Pixium 4343RCE (K170113 - February 9, 2017), together with the tube these components form the radiography Image Chain. As additional options, a portable detector (K141736 - July 25, 2014) can be used for free exposures as well as in the patient support table or in the vertical stand.

The provided text describes the Philips DigitalDiagnost C90, a stationary X-ray system, and states that it did not require a clinical study. Instead, substantial equivalence was demonstrated through non-clinical performance testing and validation.

However, the document does not contain a table of acceptance criteria or details of a study that proves the device meets specific acceptance criteria related to its performance beyond general safety and equivalence to a predicate device. It confirms that the device meets "the acceptance criteria" in general, but does not quantify them or describe the study in detail.

Therefore, many of the requested elements cannot be extracted from the provided text.

Here's a breakdown of what can and cannot be provided based on the input:

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

• Cannot be provided. The document states "The test results demonstrate that the proposed DigitalDiagnost C90 meets the acceptance criteria," but does not specify what those criteria are or report detailed device performance against them. It focuses on demonstrating substantial equivalence to a predicate device based on similar technological characteristics and adherence to consensus standards.

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

• Cannot be provided. The document refers to "nonclinical verification and validation tests as well as image quality testing" but gives no details about the sample size of any test sets or the provenance of any data used in these non-clinical tests.

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):

• Cannot be provided. Since no specific clinical or performance study with a test set and ground truth is detailed, information about experts and their qualifications is absent.

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

• Cannot be provided. No details on adjudication methods for a test set are mentioned.

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:

• Cannot be provided. The document explicitly states: "The proposed DigitalDiagnost C90 did not require a clinical study". Therefore, no MRMC study with AI assistance was performed or described.

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

• Cannot be provided. The device is an X-ray system, not primarily an AI algorithm. Its performance is evaluated as an imaging system, and no standalone algorithm performance is discussed.

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

• Cannot be provided. As no specific performance study is detailed, the type of ground truth used is not mentioned.

8. The sample size for the training set:

• Cannot be provided. The document does not describe any machine learning or AI components that would require a "training set" in the context of X-ray image interpretation or diagnostics. The device is a radiographic image acquisition, processing, storage, display, and export system.

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

• Cannot be provided. As no training set is discussed, how its ground truth was established is not relevant or provided.

In summary, the provided document focuses on demonstrating substantial equivalence to a predicate device (Philips Eleva Workspot for DigitalDiagnost K141736) through comparisons of technological characteristics, adherence to recognized standards for safety and performance (e.g., ISO 14971, IEC 60601 series), and non-clinical testing for verification and validation. It explicitly states that a clinical study was not required. No specific "acceptance criteria" table or quantitative performance data from a dedicated study assessing diagnostic accuracy or similar metrics are present.

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ProxiDiagnost N90 is a multi-functional general R/F system. It is suitable for all routine radiography and fluoroscopy exams, including specialist areas like angiography or pediatric work, excluding mammography.

The ProxiDiagnost N90 is a multi-functional nearby controlled fluoroscopy system in combination with a high-end digital radiography system consisting of a floor-mounted tiltadjustable patient support table and a scan unit consisting of a tube and a flat panel dynamic detector, Pixium FE4343F, for the fluoroscopy examinations. The tabletop can be moved by a motor in the lateral and longitudinal direction and can be tilted -85° to +90° degrees. The scan unit tilts with the table and can be moved in the longitudinal and lateral direction, relative to the table and to the patient. The fully integrated system is provided with a x-ray tube(s) with collimator and high resolution displays. The ProxiDiagnost N90 is configured with a Philips x-ray generator and a flat panel dynamic detector, Pixium FE4343F, components of the Philips radiography/fluoroscopy Image Chain. As additional options, the ProxiDiagnost N90 can be used as a digital radiography system consisting of a mounted tube in a ceiling suspension together with the portable or fixed detector in the vertical stand.

Based on the provided text, the ProxiDiagnost N90 is evaluated for substantial equivalence to its predicate device, EasyDiagnost Eleva (K031535). The document outlines non-clinical data and makes a case against the need for a clinical study.

Here's an analysis of the requested information:

1. Table of acceptance criteria and the reported device performance:

The document broadly states that "The test results demonstrate that the proposed ProxiDiagnost N90 meets the acceptance criteria and is adequate for its intended use." However, specific numerical acceptance criteria or performance metrics for the ProxiDiagnost N90 are not detailed in a table. The comparison provided focuses on demonstrating technological similarities to the predicate device rather than presenting specific performance thresholds for the new device.

The "Discussion" column in the "Summary of technological characteristics" table implicitly suggests that if a characteristic is "Similar" or "Equivalent" to the predicate, it meets acceptance criteria for substantial equivalence.

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

The document explicitly states: "The proposed ProxiDiagnost N90 did not require a clinical study since substantial equivalence to the currently marketed and predicate device was demonstrated with the following attributes: Design features, Indication for use, Fundamental scientific technology, Non-clinical performance testing including validation; and Safety and effectiveness."

Therefore, there is no test set sample size from a clinical study for the ProxiDiagnost N90 itself. The evaluation relied on non-clinical verification and validation tests against recognized consensus standards and a comparison to the predicate and reference devices.

No information on data provenance for a clinical test set is available, as no clinical study was conducted.

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

Since no clinical study was performed and no test set with clinical data was used for evaluation, there were no experts used to establish ground truth for a test set in this submission.

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

As no clinical test set was used for evaluation of the ProxiDiagnost N90, no adjudication method was applied for a test set.

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:

There is no mention of a multi-reader multi-case (MRMC) comparative effectiveness study being conducted for the ProxiDiagnost N90. The device is a general R/F system, not an AI-assisted diagnostic tool, so such a study would not be relevant in this context.

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

The device is an imaging system, not an algorithm in the AI sense. Its "performance" would relate to image quality and system functionality, which was assessed through non-clinical performance testing (verification and validation tests according to standards like IEC 60601-1, IEC 60601-1-2, IEC 60601-1-3, IEC 60601-2-54, IEC 62220-1). These tests are typically "standalone" in that they evaluate the device's technical specifications and functionality independent of direct human interpretation studies.

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

For the non-clinical performance and validation tests, the "ground truth" would be established by the specified engineering tolerances, recognized international standards (e.g., IEC standards for image quality, safety, and performance), and the performance characteristics of the predicate and reference devices. For instance, the MTF and DQE values for the detector are reported and implicitly "compared" to cleared components, meaning the "ground truth" for the acceptance of these values is their alignment with previously cleared and accepted digital imagers.

8. The sample size for the training set:

As no clinical study was performed and the device is an imaging system (not an AI model requiring a training set), there is no training set sample size.

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

Since no training set was used, ground truth for a training set was not established.

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The MobileDiagnost M50 is a mobile radiographic system used for general X-ray imaging. This system can be used for taking x-ray images of patients who can't be moved to a fixed system. Applications include examinations of chest, upper and lower extremities, spinal column, skull radiography and abdominal cavities. MobileDiagnost M50 is not suitable for Mammography.

The MobileDiagnost M50 is a digital mobile X-ray system, used to perform X-ray examinations in different locations of a hospital, particularly for the patient who cannot be transferred to a fixed X-ray room. The MobileDiagnost M50 allows the user to take X-rays on a digital wireless detector and the image can then be viewed on the Eleva Workspot. The MobileDiagnost M50 consists of a mobile base unit and a user interface combined with a flat solid state X-ray detector. It is used by the operator to generate, process and handle digital X-ray images.

The provided text is a 510(k) summary for the Philips MobileDiagnost M50. It does not describe a study involving an AI/Machine Learning device, but rather a mobile x-ray system. The basis of the submission is demonstrating substantial equivalence to a predicate device through non-clinical performance testing and technical comparisons, rather than a clinical effectiveness study of an AI algorithm improving human performance.

Therefore, many of the requested criteria related to AI/ML device studies (such as MRMC studies, ground truth establishment, sample sizes for training/test sets for AI, expert consensus, etc.) are not applicable to this document.

However, I can extract information regarding the device's technical specifications and the non-clinical tests performed to demonstrate safety and effectiveness, which serve as its "acceptance criteria" and "proof" in the context of this 510(k) submission for substantial equivalence.

Here's a breakdown of the requested information based on the provided document, with explanations for what is not applicable:

Device: MobileDiagnost M50 (Mobile X-ray System)

1. Table of Acceptance Criteria and Reported Device Performance

In the context of this 510(k), acceptance criteria are primarily compliance with recognized standards and demonstration of comparable performance to the predicate device through technical specifications and non-clinical testing. The "reported device performance" is the MobileDiagnost M50's specifications and its compliance with the standards.

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

• Not Applicable: This is a submission for an X-ray system, not an AI/ML device that requires a "test set" of images for AI performance evaluation. The "testing" referred to is non-clinical verification and validation against engineering specifications and regulatory standards. The documentation does not detail specific sample sizes of patients/images for performance testing, as it's not a clinical efficacy study.
• Data Provenance: Not applicable in the context of image data for an AI model.

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

• Not Applicable: No "ground truth" establishment by experts on a test set of images is mentioned as this is not an AI/ML device. The "ground truth" for this device's performance is compliance with established engineering and safety standards.

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

• Not Applicable: No clinical test set requiring 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

• Not Applicable: This is a mobile X-ray hardware system, not an AI assistance tool. No MRMC study was performed or required. The document explicitly states: "The MobileDiagnost M50 did not require a clinical study since substantial equivalence to the currently marketed and predicate MobileDiagnost wDR 2.0 was demonstrated with the following attributes: Design features; Indication for use; Fundamental scientific technology; Non-clinical performance testing including validation; and Safety and effectiveness."

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

• Not Applicable: This is not an algorithm/AI device.

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

• Not Applicable: No clinical ground truth was established from patient data. The "ground truth" for clearance is the device's adherence to technical performance specifications and safety standards as demonstrated through non-clinical testing.

8. The sample size for the training set

• Not Applicable: This is not an AI/ML device. No training set for an algorithm.

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

• Not Applicable: This is not an AI/ML device. No training set or ground truth for it.

Summary of Acceptance/Proof for this Device:

The "acceptance criteria" for the Philips MobileDiagnost M50, as presented in this 510(k) summary, revolve around:

1. Compliance with recognized electrical, radiation safety, usability, and quality management standards. This is demonstrated through non-clinical verification and validation tests.
2. Demonstration of "substantial equivalence" to a legally marketed predicate device (MobileDiagnost wDR 2.0 - K141895). This is proven by showing that any differences in design features or fundamental scientific technology do not impact the safety or effectiveness of the device. Where components differ (e.g., the detector's scintillator), justification is provided by referencing other predicate/cleared devices that use the same component and have already demonstrated safety and effectiveness (e.g., Varian PaxScan 4336Wv4 detector, K161459).

The "study that proves the device meets the acceptance criteria" is the comprehensive set of non-clinical verification and validation tests performed to ensure compliance with the listed international and FDA-recognized consensus standards and internal design requirements. The results of these tests are stated to have demonstrated that the device "met the acceptance criteria and is adequate for its intended use."

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As a part of a radiographic system, the SkyPlate Detector for Philips Radiography/Fluoroscopy Systems is intended to acquire, process, store, display and export digital radiographic images. The SkyPlate Detector for Philips Radiography/ Fluoroscopy Systems is suitable for all routine radiography exams, including specialist areas like intensive care, trauma, or pediatric work, excluding mammography.

The SkyPlate Detector for Philips Radiography/Fluoroscopy Systems is a Solid State X-ray Imaging Device that converts x-ray patterns into electrical signals. The signals are converted into visible images for use in medical diagnosis. A cesium iodide scintillator absorbs the input x-ray photons in the detector. The cesium iodide scintillator in turn emits visible spectrum photons that illuminate an array of photodetectors that create an electrical charge representation of the x-ray input. A matrix scan of the array converts the integrated charges into a modulated electrical signal.

The SkyPlate Detector for Philips Radiography/Fluoroscopy Systems is optionally installed and is intended to be integrated into an x-ray system, where it constitutes an x-ray receptor for direct radiography x-ray imaging. It is electrically powered by and connected with the x-ray system. The SkyPlate Detector for Philips Radiography/Fluoroscopy Systems is connected to the Philips Eleva Workspot with SkyFlow (cleared via K153318) to create a complete x-ray imaging chain, and is intended to be used for radiography in Philips Radiography/Fluoroscopy systems, such as the CombiDiagnost R90 (cleared via K163210). The SkyPlate Detector for Philips Radiography/Fluoroscopy Systems, is used to acquire diagnostic radiographic images during radiographic procedures.

This document is a 510(k) premarket notification for the SkyPlate Detector for Philips Radiography/Fluoroscopy Systems. It outlines the device's characteristics, intended use, and claims of substantial equivalence to a predicate device.

Here's an analysis of the provided text in relation to your questions:

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

The document does not provide a specific table of quantitative acceptance criteria for device performance. Instead, it states that the device:

• "Complies with the aforementioned international and FDA-recognized consensus standards and device specific guidance documents."
• "Meets the acceptance criteria and is adequate for its intended use."
• "is as safe, as effective, and performs as well or better than the predicate device."
• "provides images of equivalent diagnostic capability."

The full list of standards mentioned are:

• ISO 14971: Medical Devices - Application of risk management to medical devices
• NEMA PS 3.1-3.20 Digital Imaging and Communication in Medicine (DICOM) Set
• AAMI ANSI IEC 62304:2006 Medical Device Software - Software lifecycle processes
• IEC 62220-1: Medical electrical equipment, Characteristics of digital x-ray imaging devices
• IEC 62220-1-1:2015: Medical electrical equipment, Characteristics of digital x-ray imaging devices – Part 1-1: Determination of the detective quantum efficiency – Detectors used in radiographic imaging
• IEC 60601-1: Medical electrical equipment – Part 1: General requirements for basic safety and essential performance (IEC 60601-1:2005 + A1:2012, MOD)
• IEC 60601-1-2: Medical electrical equipment. General requirements for basic safety and essential performance. Collateral standard: Electromagnetic compatibility requirements and tests
• IEC 60601-1-3: Medical electrical equipment. General requirements for basic safety and essential performance. Collateral standard: Radiation in diagnostic X-ray equipment
• IEC 60601-2-54: Medical electrical equipment. Particular requirements for basic safety and essential performance of X-ray equipment for radiography and radioscopy
• Guidance for the Submission of 510(k)s for Solid State C X-ray Imaging Device", issued September 1, 2016
• "Guidance for the Content of Premarket Submissions for O software contained in Medical Device", issued May 11, 2005
• Pediatric information for X-Ray Imaging Device Premarket Notifications, Draft, issued May 10, 2012

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

The document explicitly states: "The SkyPlate Detector for Philips Radiography/Fluoroscopy Systems did not require a clinical study since substantial equivalence to the currently marketed and predicate device was demonstrated with the following attributes: Design features; Indication for use; Fundamental scientific technology; Non-clinical performance testing including validation; and Safety and effectiveness." Therefore, no clinical test set was used for this 510(k) submission, and consequently, no clinical sample size or data provenance is provided.

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)

Since no clinical study was conducted and no clinical test set was used, no experts were used to establish ground truth for a test set.

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

As no clinical study was conducted and no clinical test set was used, no adjudication method was applied.

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

The document does not describe any MRMC comparative effectiveness study. This device is an X-ray detector, not an AI-assisted diagnostic tool.

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

This refers to a standalone performance of an algorithm. The SkyPlate Detector is an X-ray imaging device, not an algorithm. Therefore, this question is not applicable to the device described. The submission relies on "non-clinical performance testing including validation" for the physical device.

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

For the non-clinical performance testing, the ground truth would be established by engineering and laboratory measurements demonstrating compliance with the specified international and FDA-recognized consensus standards. This would involve physical measurements of detector characteristics (e.g., image resolution, detective quantum efficiency, electrical signals, safety parameters) against defined benchmarks within those standards, rather than clinical ground truth from patient data.

8. The sample size for the training set

This question typically applies to machine learning or AI models. Since the device is a physical X-ray detector and not an AI algorithm, there is no concept of a "training set" in the context of this submission. The validation was based on non-clinical performance and engineering tests.

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

As explained above, this question is not applicable as there is no training set for this type of device.

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As a part of a radiographic system, the Pixium 4343RCE is intended to acquire digital radiographic images. The Pixium 4343RCE is suitable for all routine radiography exams, including specialist areas like intensive care, trauma, or pediatric work, excluding fluoroscopy, angiography and mammography.

The pixium 4343RCE is a Stationary x-ray system that converts x-ray patterns into electrical signals. The signals are converted into visible images for use in medical diagnosis. In the device, a cesium iodide scintillator absorbs the input x-ray photons. The scintillator in turn emits visible spectrum photons that illuminate an array of photodetectors that create an electrical charge representation of the x-ray input. A matrix scan of the array converts the integrated charges into a modulated electrical signal.

The detector is permanently installed and intended to be integrated into an x-ray system, where it constitutes an x-ray receptor for direct x-ray imaging. It is electrically powered by and connected with the x-ray system. The device is connected to the Philips Eleva Workspot to create a complete x-ray imaging chain, and it is intended to be used in Philips x-ray systems such as the DigitalDiagnost.

The provided text describes the Philips pixium 4343RCE, a stationary x-ray system. The document is a 510(k) summary for premarket notification to the FDA, asserting substantial equivalence to a predicate device (Philips pixium 4343RC, K131483).

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present specific numerical acceptance criteria (e.g., minimum DQE, MTF values that must be met) for the pixium 4343RCE, nor does it provide a direct side-by-side comparison of claimed performance data against such defined criteria. Instead, the acceptance criteria are implicitly stated as demonstrating similar performance to the predicate device and compliance with various standards. The "Discussion" column in the comparison table serves as the "reported device performance" against the implicitly accepted criterion of not negatively impacting safety or effectiveness compared to the predicate.

Here's a table summarizing relevant performance metrics and the discussion provided, which indicates how the device meets (or is considered to meet) implied acceptance criteria of non-inferiority or slight improvement:

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CombiDiagnost R90 is a multi-functional general R/F system. It is suitable for all routine radiography and fluoroscopy exams, including specialist areas like angiography or pediatric work, excluding mammography.

The CombiDiagnost R90 is a multi-functional remote controlled fluoroscopy system in combination with a high-end digital radiography system consisting of a floor-mounted tilt- and heightadjustable patient support and a scan unit consisting of a tube and a flat panel detector, Pixium FE 4343F. The tabletop can be moved by a motor in the lateral direction and can be tilted +/- 90 degrees. The scan unit tilts with the table and can be moved in the longitudinal direction, relative to the table and to the patient. The fully integrated system is provided with a touch screen console, glass or metal x-ray tube(s) with collimator and high resolution displays. As a fully integrated system, the proposed CombiDiagnost R90 can be configured with a Philips generator, the flat panel detector Pixium FE 4343F, and the Philips Dynamic Eleva Image Chain acquisitionstation also provided with the currently marketed and reference device, Eleva Workspot with SkyFlow (Eleva Workspot). The proposed CombiDiagnost R90 uses the same workflow from the currently marketed and reference device, Eleva Workspot with SkyFlow. The only modification to the Eleva Workspot is integration with the Pixium FE 4343F detector.

The provided text is a 510(k) summary for the Philips CombiDiagnost R90, an X-ray system. This document focuses on demonstrating substantial equivalence to a predicate device, rather than providing a detailed study proving the device meets specific acceptance criteria in the manner one might expect for a diagnostic AI algorithm.

Therefore, much of the requested information regarding acceptance criteria for a diagnostic performance study, sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, or standalone performance of an AI algorithm is not applicable to this type of submission. This document describes a medical device, not a diagnostic AI.

However, I can extract the information related to the device's technical specifications and the non-clinical testing performed to show its safety and effectiveness.

Here's the breakdown of the available information:

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

The document does not provide a specific table of quantitative acceptance criteria for diagnostic performance and corresponding reported performance values for the CombiDiagnost R90's imaging capabilities (e.g., sensitivity, specificity for a particular pathology). Instead, it presents a comparison of technological characteristics between the proposed device and its predicate, along with a statement that non-clinical tests met acceptance criteria.

Technological Characteristics Comparison (Proposed Device vs. Predicate Device)

The study (non-clinical verification and validation tests as well as image quality testing) "demonstrate[d] that the proposed CombiDiagnost R90 meets the acceptance criteria and is adequate for its intended use." The acceptance criteria for these non-clinical tests are defined by the referenced international and FDA-recognized consensus standards.

2. Sample size 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 for an X-ray system, not a diagnostic AI algorithm. The "test set" here refers to the physical device undergoing non-clinical technical testing, not a dataset of patient images.

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 information is not applicable as the document describes non-clinical technical testing of an X-ray system, not a diagnostic study requiring expert ground truth for image interpretation.

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

This information is not applicable for the same reasons as #3.

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

An MRMC study was not done. This type of study is relevant for evaluating the impact of AI on human reader performance, which is not the purpose of this 510(k) submission for an X-ray system.

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

A standalone performance study of a diagnostic algorithm was not done. This document concerns an X-ray imaging system.

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

For the non-clinical testing of the CombiDiagnost R90, the "ground truth" would be established by engineering specifications and validated measurement equipment according to the referenced standards (e.g., IEC 62220-1 for detective quantum efficiency, IEC 60601 series for safety and performance). This is a technical ground truth, not a clinical one based on patient outcomes or expert reads.

8. The sample size for the training set

This information is not applicable as the device is an X-ray system, not an AI algorithm requiring a training set of images.

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

This information is not applicable for the same reasons as #8.

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