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The deep learning based Max Field-of-View (MaxFOV 2) is a CT image reconstruction method intended to produce images of the head and whole-body using Axial, Helical, and Cine acquisitions.

MaxFOV 2 is designed to extend the nominal display field of view (DFoV) for cases where patient size and positioning requirements result in a portion of the patient's body to be outside of the nominal DFoV.

These extended FoV images are intended for use in radiation therapy planning and are clinically useful for the simulation and planning of radiation therapy for the treatment of cancer for patients. They can also be used for visualization of patient anatomy for cases not involving therapy planning. MaxFOV 2 is intended for patients of all ages, especially bariatric patients.

The MaxFOV 2 is an enhanced Extended Field of View (EFOV) reconstruction option for GE's CT scanners. The MaxFOV 2 utilizes a new deep learning algorithm to extend the display field of view (DFOV) beyond the CT system's scan field of View (SFOV) of 50cm to up to 80cm depending on the bore size of the CT system. CT scanners use the EFOV reconstruction algorithms to visualize tissue truncated due to large patient habitus and/or off-center patient positioning. Same as the Wide View option on the predicate, the MaxFOV 2 is designed to enable a clinically useful visualization of the skin line and CT Number of human body parts located outside of the SFOV. EFOV images are especially useful for radiation therapy planning and they can also be used for visualization of patient anatomy outside of the SFOV for routine CT imaging. This DL enabled new MaxFOV2 EFOV reconstruction process offers improved performance over the existing WideView option on the predicate device.

The DL MaxFOV2 algorithm was designed and tested for GE's multiple CT scanner platforms of various bore sizes from 70cm to 80cm. These CT systems with the integrated MaxFOV 2 option remain compliant with the same standards as base CT systems.

This option is commercially marketed as MaxFOV2.

The provided text describes the MaxFOV 2, a CT image reconstruction method that utilizes deep learning to extend the display field of view. The document outlines the device's indications for use, technological characteristics, and the summary of non-clinical and clinical testing performed to support its substantial equivalence to a predicate device.

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

Acceptance Criteria and Reported Device Performance

The document doesn't explicitly state quantitative acceptance criteria in a table format with pass/fail thresholds. Instead, it describes various tests and a clinical reader study designed to demonstrate performance that is "consistent and acceptable," does not "raise new or different questions of safety and effectiveness," and supports "substantial equivalence and performance claims." The performance is evaluated relative to the predicate device, "Wide View (K023332)".

Assuming the core acceptance criteria revolve around image quality, accuracy of patient contour (skin line), and CT number accuracy in the extended FOV, the reported performance is qualitative but positive:

Study Details

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

• Sample Size for Test Set: 49 CT exams were used in the clinical reader study.
• Data Provenance: The exams were "acquired from different GE CT system platforms." The text does not specify the country of origin or whether the data was retrospective or prospective. It states the exams "represent typical and challenging RTP-relative scenarios where the MaxFOV2 will likely be used."

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: 5 external, clinical readers.
• Qualifications of Experts: The specific qualifications (e.g., years of experience, subspecialty) are not explicitly stated, beyond them being "external, clinical readers."

4. Adjudication Method for the Test Set

The text indicates that readers scored images using a 5-point Likert scale. It does not mention an explicit adjudication method (e.g., 2+1, 3+1 consensus) for establishing a single "ground truth" score per image. It seems the readers' individual scores were aggregated and analyzed to support the claims.

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

• Was it done?: A clinical reader study was performed, which is a type of MRMC study, though the primary goal seems to be demonstrating substantial equivalence rather than a direct comparison of human readers with and without AI assistance to quantify an "improvement effect size." The study evaluates the output of the AI model (the reconstructed images) through human reader assessment.
• Effect Size of Human Improvement with AI vs. without AI assistance: Not explicitly quantified in terms of human reader improvement. The study assesses the quality of images produced by MaxFOV2, which uses a deep learning component. The improvement is implied for the images produced by MaxFOV2 over the predicate device. "This DL enabled new MaxFOV2 EFOV reconstruction process offers improved performance over the existing WideView option on the predicate device." However, this statement refers to the device's performance, not specifically how human readers improve their diagnostic accuracy or efficiency when using the AI-assisted images compared to reading images generated without the AI.

6. Standalone Performance (Algorithm Only)

The text describes "engineering bench testing using phantoms." These tests (MaxFOV 2 Patient contour (Skin line) accuracy and CT Number accuracy, MaxFOV 2 IQ Performance Evaluation using a very large phantom, MaxFOV 2 Performance Evaluation Using an anthropomorphic phantom) appear to be standalone performance assessments of the algorithm and its output, independent of a human reader's interpretation in a diagnostic context. These tests would evaluate the algorithm's output directly against known phantom characteristics.

7. Type of Ground Truth Used for Test Set

• For Bench Testing: The ground truth for bench testing (e.g., image quality, skin line accuracy, CT number accuracy) would be established by the known physical properties and measurements of the phantoms used.
• For Clinical Reader Study: The "ground truth" for clinical acceptability in the reader study is based on the expert consensus/opinion of the 5 external clinical readers using a Likert scale. This is a subjective assessment of image quality and clinical utility rather than an objective "pathology" or "outcomes" ground truth. The study evaluates how well the generated images aid human interpretation.

8. Sample Size for the Training Set

The document does not explicitly state the sample size used for the training set of the deep learning algorithm ("CNN"). It only mentions that the MaxFOV2 "uses a CNN which is trained on multiple CT scanners."

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

The document does not provide details on how the ground truth for the training set was established. It simply states that the CNN was "trained on multiple CT scanners." For deep learning image reconstruction, the training might involve paired data (e.g., truncated vs. full FOV images, or images generated with existing algorithms as a reference), or simulated data, but the specific method of ground truth establishment is not described.

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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 physician as an aid in diagnosis.

The images delivered by the system can be used by trained staff as an aid in diagnosis, treatment preparation and radiation therapy planning.

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.

The Siemens SOMATOM Confidence is a Computed Tomography X- ray System which features one continuously rotating tube-detector system and functions according to the fan beam principle. The SOMATOM Confidence produces CT images in DICOM format, which can be used by trained staff for post-processing applications commercially distributed by Siemens 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 the post processing applications optionally.

The platform software for the SOMATOM Confidence, syngo CT VA62A (SOMARIS/7 VA62A), is a command-based program used for patient management, data management, X-ray scan control, image reconstruction, and image archive/evaluation. The SOMATOM Confidence will support the following modifications in comparison to the predicate devices:

• 1. New Marketing Name: SOMATOM Confidence (SOMATOM Confidence® RT Pro)
• 2. Modified Indication for Use Statement
• 3. New/Modified Hardware
  • Touch Panels ●
  • New Gantry and Patient Table Covers .
  • Stellar RT Detector .
• 4. Software version SOMARIS/7 VA62A
  • Data Exchange with external SW Client (Teamplay) ●
  • IT Hardening .
  • DirectDensity™ .
• 5. Update 510(k) Information

The SOMATOM Confidence will be offered in 20 and 64 slice configurations.

The provided text does not contain detailed acceptance criteria and the comprehensive study results to populate all the requested fields for a specific AI device. The document is a 510(k) summary for a CT scanner (SOMATOM Confidence® RT Pro), which is a general imaging device, not an AI-powered diagnostic tool with specific performance metrics like sensitivity, specificity, or AUC related to an AI algorithm.

However, I can extract the information related to the device itself and its general performance verification, as well as the reference to a clinical trial relevant to one of its indications for use (low-dose lung cancer screening).

Here's a breakdown of what can and cannot be answered from the provided text:

Information directly available or inferable about the SOMATOM Confidence® RT Pro (CT System):

• 1. A table of acceptance criteria and the reported device performance: The document states that "all of the software specifications have met the acceptance criteria" and that "the testing results support that the requirement specifications have met the acceptance criteria" for the DirectDensity™ feature. However, the specific acceptance criteria values (e.g., specific signal-to-noise ratios, spatial resolution, CT number accuracy) and their corresponding reported performance values for the device itself (the CT scanner) are not presented in a table format within this summary. It generally assures conformity to performance standards like ISO 14791, NEMA XR-29, IEC 61223-2-6, IEC 61223-3-5, IEC 62304, NEMA XR-25, and DICOM 3.1-3.20. For DirectDensity™, it mentions "image values proportional to relative electron density and perform as expected," but no quantitative criteria or performance figures are given.
• 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The document describes the CT system's performance, including features like DirectDensity™. This is essentially a "standalone" evaluation of the imaging device's capabilities. There is no mention of an AI diagnostic algorithm being tested in a standalone capacity within this text.
• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): For the testing of the CT system and its features such as DirectDensity™, phantom tests (using a Gammex 467 Tissue Characterization Phantom) and simulations were used.
• 8. The sample size for the training set: Not applicable for the CT scanner hardware/software itself. If this were an AI algorithm submission, this would be crucial.
• 9. How the ground truth for the training set was established: Not applicable for the CT scanner hardware/software itself.

Information related to the referenced "National Lung Screening Trial (NLST)" for the low-dose lung cancer screening indication:

• 2. Sample size used for the test set and the data provenance:
  • Sample Size: The NLST was "a randomized trial of screening with the use of low-dose CT compared to chest radiography." While the exact number of participants is not given in this document, the referenced paper (N Engl J Med 2011; 365:395-409) is the primary source for this information. It is a prospective trial.
  • Data Provenance: The trial was sponsored by the National Cancer Institute, implying multi-center US-based data.
• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not specified in this document, but would be detailed in the NLST publication. The interpretation task for CT in NLST was "to detect lung nodules of 4mm diameter or greater."
• 4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not specified in this document for the NLST.
• 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 NLST was a comparative effectiveness study comparing low-dose CT to chest radiography for lung cancer screening outcomes (mortality reduction). It was not specifically an MRMC study with AI assistance. It evaluated human readers interpreting images from two different modalities. The effect size reported by NLST was that screening with low-dose CT reduced lung cancer mortality compared to chest radiography. This document does not quantify that effect size but refers to the publication.

Summary Table:

Additional Requested Information:

• 2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
  • For CT System Testing: Not explicitly specified (phantom tests, simulations).
  • For NLST: Not explicitly stated in this document, but implied to be a large, prospective, multi-center US trial (National Cancer Institute sponsorship).
• 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)
  • For CT System Testing: Not applicable (phantom/simulations).
  • For NLST: Not specified in this document.
• 4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
  • For CT System Testing: Not applicable.
  • For NLST: Not specified in this 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
  • Not done with AI assistance. The NLST was a comparative effectiveness trial of two screening modalities (low-dose CT vs. chest radiography) interpreted by human readers. It did not involve AI assistance.
• 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
  • Yes, for the CT system itself. The performance of the SOMATOM Confidence CT system and its features (like DirectDensity™) were evaluated. This is not an AI diagnostic algorithm, but the imaging device's performance.
• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
  • For CT System Testing: Phantom tests (Gammex 467 Tissue Characterization Phantom) and simulations.
  • For NLST: Lung cancer mortality (outcomes data) was the primary endpoint. The "interpretation task with CT for this study was to detect lung nodules of 4mm diameter or greater" which would have likely been verified by pathology/follow-up.
• 8. The sample size for the training set
  • Not applicable; this is for a CT system, not an AI algorithm requiring a training set in the typical sense.
• 9. How the ground truth for the training set was established
  • Not applicable.

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