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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 Prime 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 that improves image quality and reduces image noise by employing Deep Convolutional Network methods for abdomen, pelvis, lung, cardiac, extremities, head and inner ear applications.

The Aquilion Prime SP (TSX-303B/8) V10.2 with AiCE-i is a 40-row CT System that is intended to acquire and display cross-sectional volumes of the whole body, including the head. An optional Dynamic Volume CT Upgrade Kit can be installed in order to expand the active detector rows to 80. This system is based upon the technology and materials of previously marketed Canon CT systems. In addition, the subject device incorporates the latest reconstruction technology, AiCE-i (Advanced intelligent Clear-IQ Engine - integrated), intended to reduce image noise and improve image quality by utilizing Deep Convolutional Network methods. These methods can more fully explore the statistical properties of the signal and noise. By learning to differentiate structure from noise, the algorithm produces fast, high quality CT reconstruction.

The Aquilion Prime SP (TSX-303B/8) V10.2 with AiCE-i is a CT x-ray system, and information on its acceptance criteria and the study proving it is provided below:

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

• 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 does not explicitly state a sample size for patient data. The testing primarily relies on phantom studies and a review of diagnostic images.

  • Data Provenance: Not specified for any potential patient data. The document mentions "Representative abdomen/pelvis, lung, cardiac, brain, extremities and inner ear diagnostic images" were obtained using the subject device, suggesting prospective acquisition for evaluation, but this is not definitively stated.

• 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 the assessment of diagnostic image quality:

  • Number of experts: One
  • Qualifications of experts: An "American Board Certified Radiologist." Further details (e.g., years of experience) are not provided.

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

  Based on the mention of a single American Board Certified Radiologist reviewing images, there was no multi-reader adjudication method (e.g., 2+1, 3+1) described for the diagnostic quality assessment. It appears to be a solitary expert's assessment.

• 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 multi-reader multi-case (MRMC) comparative effectiveness study focusing on human reader improvement with or without AI assistance is described in the provided text. The study focused on assessing image quality and specific performance metrics of the AI-integrated system (AiCE-i) through phantom studies and expert review of diagnostic images.

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

  Yes, standalone testing of the algorithm (AiCE-i) was a primary component of the study. This is evidenced by:

  • The extensive use of phantom studies to assess various image quality metrics (CNR, CT Number Accuracy, Uniformity, SSP, MTF, SD, NPS, LCD, etc.). These are objective measurements of the algorithm's output.
  • Direct claims stating quantitative dose reduction (82%) and noise reduction (21.4%) by the AiCE algorithm relative to other reconstruction methods.
  • Claims about improved low contrast detectability (13.8%) by the algorithm.
  • Analysis of NPS and kurtosis values to assess noise appearance/texture of the algorithm's output.

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

  The ground truth used for the evaluation can be categorized as follows:

  • Objective Phantom Measurements: For quantitative image quality metrics like dose reduction, noise reduction, CNR, MTF, NPS, and LCD. The "ground truth" here is the physical properties of the phantom and the established measurement methodologies.
  • Expert Radiologist Review: For confirming the "diagnostic quality" of the AiCE reconstructed images. The "ground truth" in this context is the qualitative assessment and professional judgment of the American Board Certified Radiologist.

• 8. The sample size for the training set

  The document does not provide details on the sample size or characteristics of the training data used for the Deep Convolutional Network methods employed by AiCE-i.

• 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 for the Deep Convolutional Network methods used by AiCE-i.

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