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PHT-70CHS is intended to produce panoramic, cephalometric, or 3D digital x-ray images. It provides diagnostic details of the dento-maxillofacial, sinus, and TMJ for adult and pediatric patients. The system also utilizes carpal images for orthodontic treatment. The device is to be operated by healthcare professionals.

The PHT-70CHS is a 4-in-1 digital X-ray system designed for both 2D and 3D dental radiographic imaging. The system integrates panoramic imaging, optional cephalometric imaging, dental computed tomography, and model imaging functions into a single unit. It is intended for dental diagnostic purposes and is capable of acquiring and processing multi-field-of-view digital radiographic images.

The PHT-70CHS is a complete digital radiographic imaging system that includes an X-ray generator, dedicated image receptors, and compatible image viewing software. The system supports acquisition of both 2D diagnostic images, including panoramic and cephalometric images, and 3D diagnostic images using cone beam computed tomography.

The materials, safety characteristics, X-ray source, indications for use, and image reconstruction including metal artifact reduction algorithms of the subject device are the same as those of the predicate devices PHT-75CHS (K231796) and PHT-90CHO (K243081).

Green X 12 VE and Green X VE are differentiated by the configuration of the CT and panoramic image receptors, which is reflected in their respective trade naming. Green X 12 VE is equipped with the Xmaru1404CF-PLUS Eth detector, while Green X VE is equipped with the Jupi0606X1 detector. Both configurations utilize the Xmaru2602CF Eth detector for cephalometric imaging.

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The Spectral CT Verida Family is a Computed Tomography X-Ray System intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data taken at different angles and planes. This device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.

The Spectral CT Verida Family system acquires one CT dataset – composed of data from a higher-energy detected x-ray spectrum and a lower- energy detected x-ray spectrum. The two spectra may be used to analyze the differences in the energy dependence of the attenuation coefficient of different materials. This allows for the generation of images at energies selected from the available spectrum and to provide information about the chemical composition of the body materials and/or contrast agents. Additionally, materials analysis provides for the quantification and graphical display of attenuation, material density, and effective atomic number.

This information may be used by a trained healthcare professional as a diagnostic tool for the visualization and analysis of anatomical and pathological structures in patients of all ages, and to be used for diagnostic imaging in radiology, interventional radiology, and cardiology and in oncology as part of treatment preparation and radiation therapy planning. The Extended field of view images and respiratory correlated scanning (4DCT) are for treatment preparation and radiation therapy planning/simulation usage only. This device is indicated for head, whole body, cardiac and vascular X-ray Computed Tomography applications in patients of all ages.

The system is also intended to be used 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.

The system incorporates both conventional iterative reconstruction (IR) and artificial intelligence (AI)-based reconstruction functionality. Spectral Precise Image (SPI) is an AI-based deep learning reconstruction feature intended to optimize image quality by reducing noise and enhancing image appearance in Head, Whole Body, Cardiac, and Vascular X-ray Computed Tomography applications. Clinical performance evaluation of the SPI feature was conducted in adult patients (≥22 years of age). The use of SPI in pediatric populations has not been clinically validated.

AI-based reconstruction outputs are intended to provide supplemental data for clinical interpretation and do not replace professional clinical judgment. Image quality and reconstruction performance may be subject to variability based on patient anatomy, body habitus, physiological motion, and technical acquisition conditions. It is the responsibility of the clinician to select the reconstruction method appropriate for the specific clinical task and patient population.

*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 Spectral CT Verida Family is a Computed Tomography X-Ray System intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data taken at different angles and planes. This device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.

The Spectral CT Verida Family system acquires one CT dataset – composed of data from a higher energy detected X-ray spectrum and a lower- energy detected X-ray spectrum. The two spectra may be used to analyze the differences in the energy dependence of the attenuation coefficient of different materials. This allows for the generation of images at energies selected from the available spectrum and provides information about the chemical composition of the body materials and/or contrast agents. Additionally, materials analysis provides for the quantification and graphical display of attenuation, material density, and effective atomic number.

The Spectral CT Verida Family system consists of three main components – a scanner system that includes a rotating gantry, a movable patient couch, and an operator console for control and image reconstruction; a Spectral Reconstruction System; and a Spectral CT Viewer. On the gantry, the main active components are the X-ray high voltage (HV) power supply, the X-ray tube, and the detection system.

The fundamental design and characteristics of the main components used in the proposed Spectral CT Verida Family system are identical to the cleared to market primary predicate device, Spectral CT 7500 RT system (K240844).

Acceptance Criteria and Study for Spectral CT Verida Family (K253649)

1. Acceptance Criteria and Reported Device Performance

The core of the clinical performance evaluation focused on the Spectral Precise Image (SPI) feature, an AI-based deep learning reconstruction algorithm. The primary performance criteria revolved around demonstrating non-inferiority of SPI compared to the iDose⁴ reconstruction method in terms of image quality and diagnostic confidence.

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

• Sample Size: 147 scans, distributed as follows:
  • Head: 42 scans
  • Body: 42 scans
  • Cardiac: 42 scans
  • Chest: 21 scans
• Data Provenance: Retrospective, multi-site. The data was "previously acquired, anonymized clinical CT data" from adult patients (>22 years of age).
• Country of Origin: Not explicitly stated, but the study sites, Hospital of the University of Pennsylvania (UPenn) and Albert Einstein College of Medicine/Montefiore Medical Center, are located in the United States.

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

The document states that "U.S. board-certified radiologists/cardiologists, blinded to reconstruction method" were used as readers. The specific number of experts used and their detailed experience (e.g., "10 years of experience") are not explicitly mentioned in the provided text.

4. Adjudication Method for the Test Set

The adjudication method is not explicitly mentioned in the provided text. The document states that images were "presented side-by-side, randomized order," and readers assessed with a "5-point Likert scale for image quality and diagnostic confidence," but it does not describe how discrepancies or consensus among multiple readers were handled if multiple readers were used per case. Given that it mentions "readers," it implies more than one.

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

• MRMC Study: Yes, an MRMC comparative effectiveness study was conducted. It was a "multi-site, controlled, blinded, non-inferiority clinical investigation." Readers were "blinded to reconstruction method" and assessed images using a 5-point Likert scale.
• Effect Size of Human Reader Improvement with AI: The study demonstrated that SPI (AI-based reconstruction) was non-inferior to iDose⁴ (standard-of-care reference) for image quality and diagnostic confidence. Furthermore, "SPI offers improved image texture, and higher reader agreement, in clinical image reconstruction." While these findings suggest benefits from SPI, the document does not quantify an "effect size" as a specific improvement metric (e.g., mean difference in diagnostic accuracy scores or specific percentages of improvement) of human readers with AI assistance versus without AI assistance. The focus was on non-inferiority and qualitative improvements in image texture and agreement.

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

Yes, a standalone (algorithm only) performance evaluation was done as part of the Summary of Non-Clinical Performance Testing. This involved "bench performance testing" to evaluate image quality and spectral accuracy using phantoms.

• Image Quality Metrics Testing: Evaluated CT number linearity, image noise, uniformity, CNR, spatial resolution (MTF and SSP), low contrast resolution (visual), and image artifacts using Verida system, Gammex ACR, and Catphan phantoms.
• Spectral Accuracy Testing: Evaluated MonoE, VNC, iodine density, Z effective, electron density, uric acid using Gammex 472, Gammex 467, Gammex ACR, and 20cm MECT phantoms.

7. The Type of Ground Truth Used

• For the clinical study (human-in-the-loop): The ground truth was based on expert consensus/reader judgment using a 5-point Likert scale for image quality and diagnostic confidence. The iDose⁴ images served as the "standard-of-care reference" for comparison.
• For the non-clinical standalone study: The ground truth was based on known phantom properties and physical measurements (e.g., expected CT numbers, noise levels, spatial resolution targets, known material compositions for spectral accuracy).

8. The Sample Size for the Training Set

The document does not specify the sample size for the training set used for the Spectral Precise Image (SPI) AI-based deep learning reconstruction. It only describes the test set used for the clinical validation.

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

The document does not describe how the ground truth for the training set was established. It mentions that SPI is an "AI-based deep learning reconstruction feature" and is "derived from the previously cleared Precise Image framework," but it does not provide details about the training data or its annotation process.

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The proposed CT Rembra RT, CT Areta RT, and CT Rembra are 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 support, components and accessories. The system is indicated for diagnostic imaging in radiology, head and whole-body X-ray Computed Tomography applications in oncology as part of treatment preparation and radiation therapy planning, vascular, interventional, neurology and cardiology, for 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 a 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 CT Rembra RT, CT Areta RT, and CT Rembra proposed devices have similar technological characteristics, software operating platform, and supported software characteristics as the predicate devices.

The proposed devices expand the CT product family with improved performance, workflow and functionality for oncology applications. They also provide a large-bore radiology solution on the Incisive Host software platform.

The design of the proposed CT Rembra RT, CT Areta RT, CT Rembra is based on the currently marketed CT 5300 (K232491), with hardware and software modifications. These include the addition of new oncology software features and workflow enhancements for radiotherapy planning support.

The proposed system 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. It produces CT images in DICOM format, which can be used by trained staff for post-processing applications commercially distributed by Philips. The CT images can be used by trained staff as an aid in diagnosis, treatment and radiation therapy planning as well as for diagnostic and therapeutic interventions. Only trained and qualified users, certified in accordance with country-specific regulations, are authorized to operate the system.

The proposed device has an 85 cm bore and includes a detector array that provides 60 cm scan field of view (SFOV) and 85cm extended field of view (EFOV).

The key system modules and functionalities of proposed device are: Gantry [X-ray tube assembly, HV generator, Collimator, DMS (Data Measurement System), Touch Panels], Patient Table (Couch), Console and optional components as well as accessories. This system also 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.

Upgrade Kits are available to upgrade CT Rembra RT, CT Areta RT, CT Rembra installations to the latest version in forward production.

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True Definition DL is a deep learning based CT reconstruction method intended for high contrast spatial resolution enhancement for bone and lung imaging. True Definition DL may be used for patients of all ages.

Computed Tomography (CT) is an indispensable imaging modality in clinical diagnostics due to its ability to provide detailed cross-sectional images of anatomical structures. However, achieving high spatial resolution remains a persistent challenge, particularly in applications requiring the visualization of fine structural details such as in inner auditory canal imaging, vascular studies, lung imaging, and bone microarchitecture analysis. Reconstruction techniques that attempt to boost spatial resolution typically also would amplify the noise which often result in tradeoffs to be made between resolution and noise.

As part of the continuous innovation to solve the above challenges in CT imaging, GEHC developed a deep learning-based CT reconstruction algorithm specifically designed for high contrast lung and bone imaging, which is the subject of this premarket notification. This reconstruction algorithm, marketed under the name True Definition DL (TDDL), is an additional user-selectable recon option specifically designed for lung and bone imaging. It aims to enhance spatial resolution for both in-plane and cross-plane directions. This algorithm is incorporated into the reconstruction chain of the Revolution CT /Apex Family CT systems including Revolution CT/Revolution CT ES, Revolution Apex Elite, Revolution Apex Plus, Revolution Apex Select all cleared under (K213715) and Revolution Vibe (K250941).

True Definition DL offers three strengths that the user can choose depending on enhancement preferences. The benefits provided by this enhancement include improved spatial resolution measured by MTF.

Here's a breakdown of the acceptance criteria and study details for the "True Definition DL" device, as extracted from the provided FDA 510(k) clearance letter:

Acceptance Criteria and Device Performance

Study Details

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

   • Sample size: Not explicitly stated for the clinical reader study, only referred to as "sample clinical data."
   • Data provenance: Not explicitly stated (e.g., country of origin or retrospective/prospective).

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

   • Number of experts: Not explicitly stated.
   • Qualifications: "US board-certified radiologists."

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

   • Not explicitly stated. The document mentions "readers confirmed," implying a consensus or majority opinion, but the specific adjudication method is not described.

4. 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:

   • A reader study was performed ("clinical testing was carried out in the form of a reader study").
   • The document states that the study "validated that True Definition DL provides diagnostic value by improving high contrast spatial resolution... and improves confidence in the assessment of high contrast structures."
   • However, the document does not explicitly state that it was an MRMC comparative effectiveness study comparing human readers with AI vs. without AI assistance. It primarily focuses on the diagnostic value and confidence with the use of the images enhanced by True Definition DL.
   • No effect size or specific metrics quantifying improvement with AI assistance are provided.

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

   • Yes, a standalone evaluation of the algorithm's performance was indicated through "robustness of the True Definition DL algorithm" testing. This included assessment of hallucination risks, enhancement of anatomical edges, preservation of noise texture, and correlation with input/target images, suggesting an algorithm-level assessment.
   • Bench testing on phantoms also measured the performance of True Definition DL against industry-standard IQ metrics and traditional metrics without direct human interaction.

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

   • For the clinical reader study, the "ground truth" was established by expert opinion/consensus of US board-certified radiologists who evaluated the images and confirmed diagnostic value and lack of artifacts.
   • For bench testing, "well established test methods using industry-standard IQ metrics" provided quantifiable reference points for performance.

7. The sample size for the training set:

   • Not explicitly stated.

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

   • Not explicitly stated. The document only mentions that True Definition DL is a "deep learning-based CT reconstruction algorithm," implying a training phase, but provides no details on how the training data was curated or its ground truth established.

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This computed tomography system is intended to generate and process cross-sectional images of patients by computer reconstruction of X-ray transmission data.

The images delivered by the system can be used by a trained staff as an aid in diagnosis, treatment and radiation therapy planning as well as for diagnostic and therapeutic interventions.

This CT system can be used for low dose lung cancer screening in high risk populations*.

*As defined by professional medical societies. 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.

Siemens intends to market a new software version, SOMARIS/10 syngo CT VB20 for the following SOMATOM Computed Tomography (CT) Scanner Systems:

SOMATOM X. Platform CT scanner systems:

• SOMATOM X.cite
• SOMATOM X.ceed

In this submission, the above listed CT scanner systems are jointly referred to as subject devices by "SOMATOM X. Platform" CT scanner systems.

The subject devices SOMATOM X. Platform CT scanner systems with SOMARIS/10 syngo CT VB20 are Computed Tomography X-ray Systems which feature one continuously rotating tube-detector system and function according to the fan beam principle (single source). The SOMATOM X. Platform CT scanner systems with software SOMARIS/10 syngo CT VB20 produces CT images in DICOM format, which can be used by trained staff for software applications, e.g. post-processing applications, commercially distributed by Siemens Healthcare GmbH and other vendors as an aid in diagnosis, treatment preparation and therapy planning support (including, but not limited to, Brachytherapy, Particle including Proton Therapy, External Beam Radiation Therapy, Surgery). The computer system delivered with the CT scanner is able to run optional post processing applications.

Only trained and qualified users, certified in accordance with country-specific regulations, are authorized to operate the system. For example, physicians, radiologists, or technologists. The user must have the necessary U.S. qualifications in order to diagnose or treat the patient with the use of the images delivered by the system.

The platform software for SOMATOM X. Platform, SOMARIS/10 syngo CT VB20, is a command-based program used for patient management, data management, X-ray scan control, image reconstruction, and image archive/evaluation.

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The Photonova Spectra, Photonova Spectra Select system is a silicon-based spectral photon counting detector X-ray Computed Tomography scanner.

The system is intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission projection data from the same axial plane taken at different angles.

The system acquires multi-energy data in every scan and natively generates high resolution monochromatic images and material density maps to facilitate visualizing and analyzing information about anatomical and pathological structures.

The system is indicated for head, whole body, cardiac, and vascular CT applications. The system is indicated for patients of all ages. The images can be post-processed to produce additional imaging planes or analysis results.

The system is indicated for lung cancer screening for patients meeting the established inclusion criteria of programs/protocols that have been 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.

Photonova Spectra is the next iteration of the predicate, the Revolution Apex platform (K213715), introducing a new Deep Silicon (dSi) photon counting detector for CT imaging. Photonova Spectra aims to realize an improvement in both spatial resolution and spectral imaging performance relative to traditional Energy Integrating Detector (EID) systems for diagnostic CT. With photon-counting detectors that can better discriminate energies, spectral CT imaging can natively provide valuable information about tissue composition and material density without the need for active filtration or kVp modulation by performing material decomposition directly from native multi-energy data.

The Photonova Spectra system is an ultra-premium multi-slice CT scanning system comprised of a gantry, a detector, an x-ray tube, a power distribution unit (PDU), a table, a system cabinet, a scanner desktop computer and user interface, and associated accessories. It is designed as a volumetric CT scanner to provide advanced imaging capability for a range of clinical applications.

Compared to the predicate Revolution Apex, the key differences of the Photonova Spectra System consist of a Deep Silicon (dSi) X-ray detector capable of directly converting X-ray photons to electrical signals, advanced detector data acquisition hardware for managing and processing of large volumes of data, advanced computer hardware and an enhanced image chain for generating High Definition (HD) Spectral and Ultra High Definition (UHD) image series.

The Photonova Spectra image chain is developed to calibrate, pre-process, reconstruct, and post-process images for use in medical imaging applications. Customized for photon counting detection physics and capability, Photonova Spectra does not require user to choose between single kV and dual energy acquisition modes. With Photonova Spectra, all acquisitions are spectral with 8 energy bins over the full high-resolution detector, and the data is stored real-time on the rotating side as the acquisition completes over the full scan sequence.

The system will be offered with either an 80 mm dSi detector and 40 mm dSi detector model configurations, commercialized as Photonova Spectra and Photonova Spectra Select, respectively. The detector size is the key differentiator, but all core technology and functionality are identical.

The provided FDA 510(k) clearance letter and summary for the Photonova Spectra CT System do not contain detailed information about specific acceptance criteria for device performance or the full study design typically expected for such information. The document focuses on regulatory compliance, technological characteristics compared to a predicate, and a general overview of verification and validation testing.

However, based on the information provided, we can infer some aspects and present them to the best of our ability, while noting the missing details.

Missing Information:

• Specific quantitative acceptance criteria: The document describes the types of tests performed (e.g., image quality metrics, LCD studies) but does not provide numerical thresholds that the device had to meet.
• Specific quantitative reported device performance: While it states "substantial equivalence of image quality was demonstrated," it doesn't provide the actual measured values for metrics like CT number accuracy, resolution, or noise texture.
• Detailed sample size for the test set: It mentions a "sample clinical covering a wide range of clinical scenarios" for the reader study but no specific number of cases.
• Data provenance for the test set: The document does not specify the country of origin of the data for the reader study's test set or whether it was retrospective or prospective.
• Detailed qualifications of experts for ground truth: It states "US board-certified Radiologists" but doesn't specify years of experience or subspecialty.
• Adjudication method for the test set.
• Effect size for MRMC study: It implies a reader study was done to compare DL levels, but doesn't quantify improvement with AI assistance.
• Sample size for the training set.
• How ground truth for the training set was established.

Acceptance Criteria and Study for Photonova Spectra CT System

Given the limitations of the provided document, the following is constructed based on the available information and educated inferences regarding CT system clearances.

1. Table of Acceptance Criteria and Reported Device Performance

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

The document states "a reader study of sample clinical covering a wide range of clinical scenarios, including Neuro, Body, and Cardiac/Chest." It also mentions "challenging cases from the above-mentioned reader study."

• Sample Size: Not explicitly stated (e.g., number of cases or images).
• Data Provenance: Not explicitly stated (e.g., country of origin, retrospective or prospective).

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

The document mentions: "Images were evaluated by US board-certified Radiologists."

• Number of Experts: Not explicitly stated.
• Qualifications of Experts: US board-certified Radiologists. Specific years of experience or subspecialty (e.g., Neuroradiologist, Cardiothoracic Radiologist) are not provided.

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method used for the reader study.

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

A reader study was conducted to compare various levels of user-prescribed denoising. It implies a comparative evaluation between the proposed DL reconstruction and FBP-based reconstruction.

• MRMC Study: Yes, a comparative clinical evaluation of challenging cases was performed by "US board certified Radiologists."
• Effect Size: Not quantified. The qualitative finding was: "No reader identified any added, removed, or reduced diagnostic information in any DLIR setting, and all pathologies were consistently visualized across all DL reconstructions." This suggests that the diagnostic interpretability was maintained, implying no negative effect and potential maintenance or improvement in visualization where denoising was effective, though specific metrics of improvement are not provided.

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

Yes, extensive standalone performance testing was done, referred to as "Image Performance Testing (Verification)" and "Summary of Non-Clinical Testing."

• This included "evaluation of a comprehensive set of image quality metrics" and "acquisitions at varying dose levels and phantom sizes."
• Metrics like "CT number, water accuracy, mean CT number over a range of spectral tasks, in-plane resolution, cross-plane resolution and noise texture (as measured by the noise power spectrum)" were assessed.
• "Low contrast detectability (LCD) studies were conducted incorporating a model observer approach."

7. The Type of Ground Truth Used

Based on the description of the studies:

• For standalone (non-clinical) testing: Phantoms (standard IQ, QA, ACR, anthropomorphic pediatric phantoms) and model observer approaches for objective metrics.
• For clinical (reader) testing: Expert consensus/interpretation by US board-certified Radiologists was used to determine diagnostic utility and whether pathologies were consistently visualized across different reconstructions.

8. The Sample Size for the Training Set

The document states that the "proposed TrueFidelity DL for PCCT is intended for routine clinical use and based on the same framework and training methodology as the reference devices (DLIR and DLIR-GSI)." However, the specific sample size for the training set (e.g., number of images, patients) for the Photonova Spectra's TrueFidelity DL for PCCT is not provided.

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

The document does not explicitly state how the ground truth for the training set was established for the TrueFidelity DL for PCCT, beyond mentioning it uses the "same framework and training methodology" as previously cleared DLIR products. Typically, for deep learning reconstructions in CT, the "ground truth" during training refers to high-quality, often low-noise or high-dose, reference images from which the algorithm learns to denoise or reconstruct lower-quality/lower-dose inputs. These reference images are usually generated from the CT scanner itself (e.g., by repeating scans at very high doses or using iterative reconstruction techniques to establish a cleaner image for comparison). Specific details are not provided.

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The Extremity CT Imaging System is intended for x-ray computed tomography imaging of upper extremities of adult patients.

The device is intended to be operated in a professional healthcare environment by qualified and trained healthcare professionals only.

The Extremity CT Imaging System™ is a computed tomography x-ray system designed for imaging of the upper limb. The device acquires volumetric spectral CT imaging using helical rotation of a single x-ray source and photon counting detectors with the limb stationary throughout the scan.

The use of photon-counting technology in the Medipix X-ray detector enables the scanner to detect individual X-ray photons with greater precision in comparison to conventional CT scanners, which typically rely on energy-integrating detectors. By counting the number of photons that pass through the body part and measuring their energy levels, the scanner can capture more detailed information about the composition of the tissues being imaged.

The Extremity CT Imaging System is intended for use in professional healthcare facility environments, excluding oxygen-rich environments, by trained medical professionals The imaging data produced by the system is intended to support diagnosis, treatment planning, and monitoring of patient conditions. It is to be noted however that actual diagnosis is not performed using any part of this software. The images produced by the system serve as a valuable tool for assessing pathologies related to the hand and wrist of adult patients

The device is meant for prescription use only by qualified and trained medical professionals.

The scanner is compact, with a small footprint and an entry port of diameter 12 cm (4.8 inches) ideally suited for imaging the hand and wrist. The scanner is mobile but must be used in a stationary position.

The system is designed to provide a streamlined workflow from patient scanning to the delivery of high-quality images, to ultimately support patients' diagnosis. Once the data has been processed within the scanner's computer, it is accessed from the Workstation using the specialized image processing software, MARS Vision, for post-processing. This stage involves advanced image manipulation, including multiplanar reformats (MPR) and 3D visualization (direct volume rendering and maximum intensity projection). Following the data review and post-processing in MARS Vision, high-quality images in DICOM format are created and uploaded to the facility's Picture Archiving and Communication System (PACS) system, where they are securely stored and made available for review.

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This device is indicated to acquire and display cross sectional volumes of the whole body, to include the head. The Aquilion Serve SP has the capability to provide volume sets. These volume sets can be used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician.

AiCE (Advanced Intelligent Clear IQ Engine) is a noise reduction algorithm that improves image quality and reduces image noise by employing Deep Convolutional Neural Network methods for abdomen, pelvis, lung, cardiac, extremities, head and inner ear applications.

PIQE is a Deep Learning Reconstruction method designed to enhance spatial resolution. By incorporating noise reduction into the Deep Convolutional Neural Network (DCNN), it is possible to achieve both spatial resolution improvement and noise reduction for cardiac, abdomen, pelvis, and lung applications, in comparison to FBP and hybrid iterative reconstruction.

CLEAR Motion is a Deep Learning Reconstruction (DLR) method designed to reduce motion artifacts. A Deep Convolutional Neural Network (DCNN) is used to estimate the patient's motion. This information is used in the reconstruction process to obtain lung images with less motion artifacts.

The Aquilion Serve SP (TSX-307B) V2.0 This device is indicated to acquire and display cross sectional volumes of the whole body, to include the head, with the capability to image whole organs in a single rotation. Whole organs include but are not limited to brain, heart, pancreas, etc.

The Aquilion Serve SP has the capability to provide volume sets of the entire organ. These volume sets can be used to perform specialized studies, using indicated software/hardware, of the whole organ by a trained and qualified physician.

This system is based upon the technology and materials of previously marketed Canon CT Systems.

Here's a breakdown of the acceptance criteria and study information for the Aquilion Serve SP (TSX-307B) V2.0, based on the provided FDA 510(k) clearance letter.

Overview of New Features:
The Aquilion Serve SP (TSX-307B) V2.0 introduces two new Deep Learning Reconstruction (DLR) methods:

• PIQE: Enhances spatial resolution and reduces noise for cardiac, abdomen, pelvis, and lung applications.
• CLEAR Motion: Reduces motion artifacts for lung applications.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with specific numerical targets and results for each new feature. Instead, it describes evaluations and general statements of meeting acceptance criteria.

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

• CLEAR Motion:

  • Clinical Image Quality Evaluation: Five representative clinical cases.
  • Dynamic and Non-Dynamic Phantom Evaluations: Phantoms were used, so not patient data.
  • Data Provenance: Not explicitly stated for the 5 clinical cases, but likely internal Canon Medical Systems data (retrospective, given it's used for evaluating a release). The document mentions "clinical lung CT datasets acquired on the TSX-307B system with iSeries V2.0 SP0000 software."

• PIQE:

  • IQ Metrics Evaluation: Phantom data and "multiple clinical and phantom-based metrics." No specific number of clinical cases mentioned for the testing set.
  • Data Provenance: Not explicitly stated for the testing data.

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

The document does not provide information on the number of experts, their qualifications, or their involvement in establishing ground truth for the test sets for either CLEAR Motion or PIQE. The evaluations primarily focus on objective phantom measurements and qualitative visual assessments described generally (e.g., "visual improvement," "improved clarity").

4. Adjudication Method for the Test Set

The document does not describe any adjudication method (e.g., 2+1, 3+1, none) for the test set.

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

The document does not report a Multi-Reader Multi-Case (MRMC) comparative effectiveness study. The evaluations focus on direct image quality improvements, not human reader performance with or without AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, the performance studies described for both PIQE and CLEAR Motion are standalone algorithm evaluations. They assess the algorithms' direct impact on image characteristics (e.g., noise, spatial resolution, CT number accuracy, motion artifact reduction) using phantoms and clinical datasets, without involving human readers for diagnostic tasks.

7. The Type of Ground Truth Used

• CLEAR Motion:

  • Dynamic Phantom: The "ground truth" is derived from the known setup of the dynamic phantom simulating pulmonary vessel motion, against which the algorithm's ability to reduce artifacts is measured.
  • Non-Dynamic Phantom: The "ground truth" is the known CT number of the water phantom, against which the algorithm's accuracy is measured.
  • Clinical Data: The "ground truth" is implicit and refers to the reduction of visually perceived motion artifacts and preservation of anatomical detail relative to conventional reconstructions. It appears to be based on expert visual assessment (though details on experts are missing).

• PIQE:

  • Phantom Data: The "ground truth" is derived from known phantom characteristics for metrics like CNR, CT number accuracy, uniformity, MTF, NPS, and LCD.
  • Clinical Data: The "ground truth" for the "IQ Metrics Evaluation" is based on improvements in objective image quality metrics and subjective visual assessments related to noise reduction, spatial resolution, and low contrast detectability, likely based on expert visual assessment (again, without specified expert details).

8. The Sample Size for the Training Set

• CLEAR Motion:

  • Trained using 3,400 partial image pairs derived from 37 clinical lung CT cases.
  • Cases covered a range of doses, field-of-view sizes, and helical pitches.

• PIQE:

  • Cardiac imaging: 18 anonymized clinical cases (13 UHR-CT and 5 NR-CT) generating over 13,000 training pairs.
  • Body imaging: 28 cases spanning thoracic to pelvic regions, producing 1,845 large training pairs.
  • Data augmentation techniques were applied.
  • 5% of samples reserved for validation.

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

• CLEAR Motion:
  The document states the DCNN was "trained to produce motion-compensated images." This implies that the training data likely consisted of pairs or sets of images where motion-affected images were "corrected" by human experts or other techniques to serve as the desired "ground truth" for motion compensation. However, the exact methodology for generating these "motion-compensated" ground truth images is not detailed.

• PIQE:

  • The retraining process used high-resolution AiCE images from an ultra-high-resolution CT system (Aquilion Precision) as targets (i.e., ground truth).
  • Simulated normal-resolution AIDR3D images were used as inputs.
  • This setup suggests a supervised learning approach where the model learns to transform lower-quality (simulated AIDR3D) inputs into higher-quality (Aquilion Precision AiCE) outputs, effectively learning noise reduction and resolution enhancement by mimicking the ideal high-resolution images as ground truth.

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ProXIma X6 is an extra-oral X-ray equipment for digital panoramic, tomographic and cephalometric X-rays exams, indicated for the following:

(I) production of orthopanoramic images of the maxillofacial area, diagnostic dental exams on teeth, dental arches and other structures of the oral cavity;

(II) Production of X-ray images of the dental arches, parts of the cranium and of the carpus in support of cephalometric examination, where the configuration includes the CEPH arm;

(III) production of tomographic images of the structures of the maxillofacial area and oral cavity for diagnostic dental exams on teeth, dental arches, the structures of the oral cavity, where the configuration includes the CBCT option.

The device is operated and used by physicians, dentists, x-ray technologists and other legally qualified professionals.

The ProXIma X6 device, with its alternative proprietary names NewTom VG-One and EOS Compact is a panoramic (PAN, 2D), cephalometric (CEPH, 2D) and tomographic (CBCT, 3D) radiological system consisting of an X-ray system that acquires radiological images by rotating around the patient's head.

The X-ray device consists of a rotating arm fitted on a column support for carrying out panoramic X-rays or tomographic examinations.

The rotating arm is able to rotate and translate with motorized movements, allowing the X-ray generator and the image detector to move around the patient according to complex orbits that follow the morphological profile. The rotary arm is applied on a column support which can slide vertically through a motorized movement.

The X-ray device can feature a cephalometric examination arm, fitted on the column support. The arm houses a cephalostat, which keeps the patient position during the exam, and the image detector which translates in synchronization with the X-ray source movement.

User's choice, the X-ray device can be equipped with a single image detector (thus the operator must position it on the rotary arm for panoramic X-rays or on the cephalometric examination arm for tele-X-ray examinations - CEPH) or with two separate image detectors (which cannot be moved, one on the rotary arm and the other on the cephalometric examination arm).

The device is a digital imaging equipment created to simplify the acquisition process for X-ray image, intended for use by qualified professionals in the field, which allows to obtain dental images.

The image is acquired through the use of an X-ray detector and a constant-voltage X-ray source, powered by a high-frequency and high-voltage generator. The image is then transferred to a computer, either in real time (2D or 3D) or subsequently (2D) depending on the operator's selection and requirements.

The equipment performs tomographic examinations through the acquisition of X-ray images through a rotational sequence and the reconstruction of a three-dimensional matrix of the volume examined, producing two- and three-dimensional views of this volume. This technique is known as CBCT.

ProXIma X6 is a digital X-ray unit, suitable for professionals in the sector, allowing them to obtain dental imaging in a simple, automated manner. The image is acquired through the use of an X-ray detector and a constant-voltage X-ray source, powered by a high-frequency high-voltage generator. The image is then transferred to a computer in real time for subsequent processing.

ProXIma X6 allows the following acquisitions to be made:

• paediatric panoramic or standard views (PAN);
• complete or partial view of the teeth, selected by the user (DENT);
• frontal and lateral views of the maxillary sinus (SIN);
• lateral and posterior-anterior views of the temporomandibular joints (TMJ).

Where equipped with CEPH arm, ProXIma X6 offers the following projections:

• cephalographies in latero-lateral view, in different formats;
• cephalographies in anteroposterior and posteroanterior view;
• hand (carpus) X-ray.

If the configuration includes CBCT exams, ProXIma X6 also allows the acquisition of tomographic images.

The configuration of ProXIma X6 device X-ray system consists of the main parts listed below:

1. Tomographic CBCT panel or Sensor for panoramic x-ray exams (alternatively)
2. Patient head support for panoramic and CBCT acquisitions
3. Arm for cephalometric x-ray exams (optional)
4. Sensor for cephalometric x-ray exams (optional)
5. Patient head support for cephalometric acquisitions (optional)
6. Handles for patient positioning
7. Laser pointers
8. X-ray source
9. Telescopic column (optional)
10. Stands for floor support (optional)

The device must be used in conjunction with software for acquisition and management of 2D and 3D images. The software system is identified for simplicity by the name 'Neowise,' which corresponds to the name of the interface module and appears on the user interface screens. The software is installed on a general purpose Personal Computer and offers all the functions necessary to perform the exam (from movements control to X-ray acquisition), save images and patient's data, manage, process, view and share 2D and 3D images, which can be obtained either from the CEFLA device or from external sources, and provides import/export of images or reports in different standard formats.

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Bunkerhill Contrast AVC is a software device intended for use in detecting presence and estimating quantity of aortic valve calcification for adult patients aged 40 years and above. The device automatically analyzes non-gated, contrast-enhanced chest computed tomography (CT) images collected during clinical care and outputs the region of interest (intended for informational purposes only) and quantification of detected calcium.

The output of the subject device is made available to the physician on-demand as part of his or her standard workflow. The device-generated quantification can be viewed in the patient report at the discretion of the physician, and the physician also has the option of viewing the device- generated calcium region of interest in a diagnostic image viewer. The subject device output in no way replaces the original patient report or the original non-gated, contrast-enhanced CT scan; both are still available to be viewed and used at the discretion of the physician.

The device is intended to provide information to the physician to provide assistance during review of the patient's case. Results of the subject device are not intended to be used on a stand- alone basis and are solely intended to aid and provide information to the physician. In all cases, further action taken on a patient should only come at the recommendation of the physician after further reviewing the patient's results.

Bunkerhill Contrast AVC is a software as a medical device (SaMD) product that interfaces with compatible and commercially available computed tomography (CT) systems. Bunkerhill Contrast AVC detects localizes, and quantifies aortic valve calcification in non-gated, contrast-enhanced chest CT studies. The core features of the product are:

• Detection of aortic valve calcification volume at a threshold of 0 mm³.
• Quantification of the overall aortic valve calcification burden in the form of an estimated volume score (mm³).
• Localization of estimated calcium burden in the form of AVC region of interest applied to a copy of the original CT scan.

The device integrates into the clinician's PACS and does not include a built-in viewer. It works in parallel with and runs in the background of the physician's workflow. When chest CT scans are captured, the images are automatically sent via the DICOM protocol to an on-premises server ("Bunkerhill Edge Server") running the Bunkerhill software. Images are de-identified on the Bunkerhill Edge Server and sent to a Bunkerhill Cloud Server for processing, where the artificial intelligence algorithm (Bunkerhill Contrast AVC) is applied to the image to detect, estimate, and localize aortic valve calcium. The results from the algorithm are then relayed back to the on-premises Bunkerhill Edge Server, where the associated chest CT images are re-identified, and results are paired with the appropriate images and routed to be made available in the physician's workflow.

Here's a breakdown of the acceptance criteria and study details for the Bunkerhill Contrast AVC device, based on the provided FDA 510(k) Clearance Letter:

Bunkerhill Contrast AVC Study Analysis

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance for Test Set

• Pivotal Study:
  • Sample Size: Not explicitly stated. The text mentions the dataset was "curated from multiple sites across three geographical regions in the United States."
  • Data Provenance: Multiple sites across three geographical regions in the United States. Retrospective.
• Bridging Study:
  • Sample Size: 65 cases.
  • Data Provenance: Not explicitly stated, but "comparing the volume generated by the Contrast AVC device on a contrast-enhanced image to the calcification volume on the same patient calculated on a non-contrast image." Implies retrospective data from patients who underwent both types of scans.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The acceptance criteria for the pivotal study's agreement metrics (bias, LOA) were derived from "clinical literature in high impact journals that investigate the inter-reader agreement of manual segmentation," implying expert-based ground truth.

4. Adjudication Method for Test Set

• Adjudication Method: Not explicitly stated. The reliance on "inter-reader agreement of manual segmentation" from clinical literature suggests that the ground truth methodology likely involved multiple readers, but the specific adjudication method (e.g., 2+1, 3+1) is not detailed.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was an MRMC study done? No. The studies described are standalone performance evaluations of the algorithm, not comparative effectiveness studies with or without AI assistance for human readers.

6. Standalone Performance

• Was a standalone study done? Yes. Both the pivotal study and the bridging study evaluated the "Contrast AVC device" performance in a "stand-alone retrospective study" for "detection, localization and agreement of the device output compared to the established ground truth."

7. Type of Ground Truth Used

• Pivotal Study: Implied to be expert consensus or manual segmentation by experts, given the reference to "inter-reader agreement of manual segmentation" in clinical literature.
• Bridging Study: Ground truth was established by "calcification volume on the same patient calculated on a non-contrast image" — which itself would have been established by a ground truth methodology (likely expert manual segmentation) for the non-contrast images.

8. Sample Size for Training Set

• Sample Size: Not provided in the document.

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

• Ground Truth Establishment: Not provided in the document. However, for a deep learning model, it would typically involve expert manual annotation or segmentation of calcifications on a large dataset of CT images.

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