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The SCENARIA View system is indicated to acquire axial volumes of the whole body including the head. Images can be acquired in axial, helical, or dynamic modes. The SCENARIA View system can also be used for interventional needle guidance. Volume datasets acquired by a SCENARIA View system can be post-processed in the SCENARIA View system to provide additional information. Post-processing capabilities of the SCENARIA View software include multi-planar reconstruction (MPR), and volume rendering. Volume datasets acquired by a SCENARIA View system can be transferred to external devices via a DICOM standard interface.

The Low Dose CT Lung Cancer Screening Option for the SCENARIA View system is indicated for using low dose CT for lung cancer screening. The screening must be conducted with the established program criteria and protocols that have been approved and published by a governmental body, a professional medical society, and/or FUJIFILM Corporation.

The SCENARIA View system is intended for general populations.

The subject device SCENARIA View is a multi-slice CT system consists of a gantry, operator's workstation, patient table, high-frequency X-ray generator, and accessories. The system performance is similar to the predicate device.

The SCENARIA View system uses 128-slice CT technology, where the X-ray tube and detector assemblies are mounted on a frame that rotates continuously around the patient using slip ring technology. The solid-state detector assembly design collects up to 64 slices of data simultaneously. The X-ray sub-system features a high frequency generator, X-ray tube, and collimation system that produces a fan beam X-ray output. The system can operate in a helical (spiral) scan mode where the patient table moves during scanning. As the X-ray tube/detector assembly rotates around the patient, data is collected at multiple angles.

The collected data is then reconstructed into cross-sectional images by a high-speed reconstruction sub-system. The images are displayed on a Computer Workstation, stored, printed, and archived as required. The workstation is based on current PC technology using the Windows™ operating system.

Compared to the predicate device referenced within this submission, the subject devices support the following modifications:

1. New features

• AutoPose is an AI-based function that recognizes a specific body part in an image of localization scan and then automatically sets the scan range and the image reconstruction range.
• RemoteRecon is a function of setting image reconstruction parameters that runs on the external personal computer (hereinafter referred to as "PC") connected to the CT system.

2. Modified features

• The maximum load capacity of patient table type has been increased from 250kg to 300 kg.
• Motion corrected reconstruction is an image reconstruction feature that reduces motion artifacts. The feature has been modified to include applicability for chest examinations, which is a non-gated scan.
• AutoPositioning is a feature that assist in positioning the patient by camera images. The feature has been modified to include additional 12 body parts (Head and Neck, Neck, C-spine, Heart, Chest-Abdomen, Chest-Upper Abdomen, Abdomen-Pelvis, Abdomen, Pelvis, T-spine, L-spine, T-L-spine), in addition to the 2 body parts (Head, Chest) of the predicate device, with scanogram ranges displayed according to the selected protocol.

The provided FDA 510(k) Clearance Letter for SCENARIA View Phase 5.0 primarily focuses on demonstrating substantial equivalence to a predicate device (SCENARIA View 4.2). The document outlines non-clinical and some clinical tests, but it does not present a formal "acceptance criteria" table with specific quantitative metrics for the device performance of the new AI features (AutoPose, Body Still Shot) in the same way one might find for a novel AI/CADe device.

The "acceptance criteria" for this submission appear to be centered around workflow improvement and sufficient image quality when compared to manual or predicate methods, rather than hard quantitative performance targets. The study designs are more akin to usability studies and qualitative image reviews.

Here's an attempt to extract and interpret the information based on your requested structure, acknowledging the limitations of the provided text in terms of explicit acceptance criteria and standalone performance metrics for the AI components.

Acceptance Criteria and Device Performance for SCENARIA View Phase 5.0 (AI Components)

The provided document describes the acceptance criteria and study results for the new features AutoPose and Body Still Shot introduced in the SCENARIA View Phase 5.0 system. The acceptance criteria are largely qualitative, focusing on workflow improvement and sufficient image quality.

1. Table of Acceptance Criteria and Reported Device Performance

Note on "Acceptance Criteria": The document does not explicitly list quantitative acceptance criteria in a table format for the AI features. The criteria listed above are inferred from the Methods and Results sections of the non-clinical and clinical tests described. The primary goal was to demonstrate workflow efficiency (AutoPose) and qualitative image improvement (Body Still Shot).

2. Sample Sizes and Data Provenance

• AutoPose (Clinical Test):

  • Total Cases: 50 (Head), 50 (Neck), 52 (Chest), 54 (Heart), 52 (Abdomen), 52 (Abdomen-Pelvis), 50 (Chest-Abdomen), 24 (T-L-Spine), 50 (C-Spine), 50 (T-Spine), 50 (L-Spine).
    • Note: The table layout in the original document makes it unclear if Chest-Upper Abdomen had cases listed, but it's empty. Assuming 50 for Chest-Abdomen and 0 for Chest-Upper Abdomen as it's not specified.
  • Total Sum (if all distinct): 50 + 50 + 52 + 54 + 52 + 52 + 50 + 50 + 50 + 50 + 24 = 504 cases.
  • Data Provenance: Clinical sites in the USA.
  • Retrospective/Prospective: Not explicitly stated, but the nature of evaluating steps in a procedure suggests it was likely a prospective workflow evaluation with certified technologists.

• Body Still Shot (Clinical Test):

  • Total Cases: Not specified.
  • Data Provenance: Not specified (only mentions "Japanese M.D." reviewers).
  • Retrospective/Prospective: Not specified.

• Training Set Sample Size:

  • Not disclosed in the provided document. The document primarily details the validation/test set.

3. Number of Experts and Qualifications for Ground Truth

• AutoPose (Clinical Test):

  • Number of Experts: Not explicitly stated how many "certified radiological technologists" performed the evaluations, only that they were certified.
  • Qualifications: "certified radiological technologists." No specific years of experience or other details are provided.
  • Role in Ground Truth: Their assessment of the number of steps and the "expected position" for manual adjustment served as the comparison for AutoPose's performance.

• Body Still Shot (Clinical Test):

  • Number of Experts: Not explicitly stated how many "Japanese M.D." (Medical Doctors) reviewed the images, only that they were "Japanese M.D."
  • Qualifications: "Japanese M.D." No specific specialty (e.g., radiologist), years of experience, or other details are provided.
  • Role in Ground Truth: Their qualitative review ("evaluated to be able to obtain images of sufficient quality") served as the ground truth for image quality.

4. Adjudication Method for the Test Set

• AutoPose: Not explicitly stated. The results imply a direct comparison of workflow steps, but it's not mentioned if multiple technologists evaluated the same cases or how discrepancies were handled.
• Body Still Shot: Not explicitly stated how reviews were conducted if multiple M.D.s were involved (e.g., consensus, majority vote).

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

• Was it done?: No, a formal MRMC comparative effectiveness study demonstrating how human readers improve with AI vs. without AI assistance was not performed or reported for either AutoPose or Body Still Shot.
• Effect Size: Not applicable, as no such study was presented. The studies were focused on workflow efficiency (AutoPose) and qualitative image quality (Body Still Shot).

6. Standalone (Algorithm Only) Performance

• Was it done?: The document does not describe a standalone AI performance study (e.g., precision, recall, F1-score for AutoPose's pose recognition; or a quantitative image quality metric for Body Still Shot). The evaluation of AutoPose was focused on the workflow impact, and Body Still Shot on perceived image quality by human reviewers.

7. Type of Ground Truth Used

• AutoPose:
  • For Scan Range Setting: The "ground truth" or reference for the AutoPose evaluation was the manual scan range setting process and the expected optimal scan position as determined by certified radiological technologists. The metric was a reduction in the number of workflow steps.
• Body Still Shot:
  • For Image Quality: The ground truth for image quality was based on the qualitative assessment and review by "Japanese M.D." to determine "sufficient quality." This is essentially expert consensus on image usability.

8. Sample Size for the Training Set

• The document does not provide information on the sample size used for training the AI models (AutoPose and Body Still Shot).

9. How Ground Truth for the Training Set was Established

• The document does not provide information on how the ground truth for the training set was established for the AI models.

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