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Rapid Aneurysm Triage and Notification (ANRTN) is a radiological computer-assisted triage and notification software device for analysis of CT images of the head. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and prioritizing studies with suspected saccular aneurysms during routine patient care. Rapid ANRTN uses an artificial intelligence algorithm to analyze images and highlight studies with suspected saccular aneurysms in a standalone application for study list prioritization or triage in parallel to ongoing standard of care. The device generates compressed preview images that are meant for informational purposes only and not intended for diagnostic use. The device does not alter the original medical image and is not intended to be used as a diagnostic device. Analyzed images are available for review through the PACS, email and mobile application. When viewed the images are for informational purposes only and not for diagnostic use. The results of Rapid ANRTN, in conjunction with other clinical information and professional judgment, are to be used to assist with triage/prioritization of saccular aneurysm cases. Radiologists who read the original medical images are responsible for the diagnostic decision. Rapid ANRTN is limited to analysis of imaging data and should not be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis.

Rapid ANRT is limited to detecting saccular aneurysms at least 4mm in diameter in adults.

Rapid ANRTN software device is a radiological computer-assisted image processing software device. The Rapid ANRTN device is a CTA processing module which operates within the integrated Rapid Platform to determine the suspicion of head saccular aneurysm(s). The ANRTN software analyzes input CTA images that are provided in DICOM format and provides notification of suspected saccular aneurysm(s) and a non-diagnostic, compressed image for preview. Rapid ANRTN is an AI/ML image processing module which integrates within the Rapid Platform.

The provided text describes the acceptance criteria and the study that proves the device (Rapid Aneurysm Triage and Notification - Rapid ANRTN) meets these criteria.

Here's the breakdown of the requested information:

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

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

• Test Set Sample Size: 266 CTA cases (151 positive for aneurysm, 115 negative).
• Data Provenance:
  • Country of Origin: Not explicitly stated in the provided text.
  • Retrospective or Prospective: Not explicitly stated, but the mention of cases "obtained from Siemens, GE, Toshiba, and Philips scanners" and "698 (633 training, 65 validation) CTA cases from multiple sites" suggests a retrospective collection of existing imaging data.

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

• Number of Experts: 3 experts.
• Qualifications of Experts: Not explicitly stated beyond "experts." It is typically assumed these are trained medical professionals (e.g., radiologists) with relevant experience, but specific qualifications are not detailed in the provided text.

4. Adjudication method for the test set

• Adjudication Method: "Ground truth established by 3 experts." This implies a consensus-based approach, but the specific adjudication method (e.g., majority vote, specific tie-breaking rules, or if all 3 had to agree) is not explicitly detailed (e.g., 2+1, 3+1). It likely refers to a consensus reading among the three experts.

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: No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly stated or described. The study focused on the standalone performance of the algorithm. The device's intended use is to "assist hospital networks and trained radiologists in workflow triage," implying an assistive role to humans, but the provided data only shows the algorithm's performance, not human performance with and without assistance.

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

• Standalone Performance: Yes, a standalone performance validation was done. The text explicitly states: "Final device validation included standalone performance validation." and "This performance validation testing demonstrated the Rapid ANRTN device provides accurate representation of key processing parameters under a range of clinically relevant perturbations associated with the intended use of the software."

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

• Type of Ground Truth: Expert consensus. The text states, "ground truth established by 3 experts."

8. The sample size for the training set

• Training Set Sample Size: 633 CTA cases. (The broader algorithm development dataset included 698 total, split into 633 training and 65 validation cases, with the 266 cases being the final performance validation set).

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

• Ground Truth Establishment for Training Set: The text states, "Algorithm development was performed using 698 (633training, 65 validation) CTA cases from multiple sites." While it mentions the cases were "selected [to] covered a wide range of suspected saccular aneurysms," the specific method for establishing ground truth for the training set (e.g., expert review, clinical reports, or a combination) is not explicitly detailed in the provided document. It is implied, but not stated, that a similar expert review process would have been used as for the test set.

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