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Rapid ICH is a radiological computer aided triage and notification software in the analysis of non-enhanced head CT images. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communication of suspected positive findings of pathologies in head CT images, for IPH, IVH, SAH, and SDH Intracranial Hemorrhages (CH).

Rapid ICH uses an artificial intelligence algorithm to analyze images and highlight cases with detected ICH on a server or standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected ICH findings. Notifications include compressed preview images, which are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not is a a diagnostic device.

The results of Rapid ICH are intended to be used in conjunction and based on professional judgment, to assist with trage /prioritization of medical images. Notified radiologists are responsible for viewing full images per the standard of care.

Rapid ICH is a radiological computer-assisted triage and notification software device. The Rapid ICH module is a non-enhanced CT (NCCT) processing module which operates within the integrated Rapid Platform to provide triage and notification of suspected intracranial hemorrhage. The Rapid ICH module is an AI/ML module. The output of the module is a priority notification to clinicians indicating the suspicion of ICH based on positive findings. The Rapid ICH module uses the basic services supplied by the Rapid Platform including DICOM processing, job management, imaging module execution and imaging output including the notification and compressed image.

Here's a breakdown of the acceptance criteria and study details for the Rapid ICH device, based on the provided text:

Acceptance Criteria and Device Performance

The primary performance goals for Rapid ICH were defined by sensitivity and specificity thresholds.
Acceptance Criteria Table and Reported Device Performance:

Study Details

2. Sample Size and Data Provenance:

• Test Set Sample Size: 314 cases (148 ICH positive, 166 ICH negative).
• Data Provenance: Retrospective, multicenter, multinational study. Specific countries are not detailed, but "multinational" implies diverse geographical origins.

3. Number of Experts and Qualifications for Ground Truth:

• Number of Experts: Not explicitly stated how many individual experts established the ground truth. The document mentions "expert reader truthing of the data," suggesting one or more experts.
• Qualifications of Experts: The document states "trained radiologists" are intended users and mentions "expert reader truthing." However, specific qualifications such as years of experience, board certification, or subspecialty are not provided for the ground truth experts.

4. Adjudication Method for the Test Set:

• The document implies ground truth was established by "expert reader truthing of the data," but does not specify an adjudication method (e.g., 2+1, 3+1, consensus review process if multiple readers were involved).

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

• No, an MRMC comparative effectiveness study was NOT mentioned for evaluating human readers' improvement with AI assistance. The study focused on the standalone performance of the AI algorithm (accuracy) and the time-to-notification benefit.

6. Standalone Performance (Algorithm Only):

• Yes, a standalone performance study was done. The reported sensitivity, specificity, and AUC values directly reflect the algorithm's performance in identifying ICH presence. The study evaluated the software's performance in identifying abnormalities, and the "time to notification" indicates the speed of the algorithm's output.

7. Type of Ground Truth Used:

• Expert Consensus: The ground truth for the test set was established through "expert reader truthing of the data." This implies a clinical expert (radiologist) determined the presence or absence of ICH.

8. Sample Size for the Training Set:

• The document states that the "minor change causing this filing, is the use of additional data for training and validation," implying the training set for this iteration of the device included more data than the predicate. However, the specific sample size of the training set is not provided in the summary.

9. How the Ground Truth for the Training Set was Established:

• Similar to the test set, the document indicates that the device was trained and validated using "retrospective case data based on expert reader truthing of the data." This suggests the ground truth for the training set was also established by expert review/diagnosis by clinical experts.

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