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InferRead CT Stroke.AI is a radiological computer aided triage and notification software for use 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 suspected positive findings of intracranial hemorrhage (ICH).

InferRead CT Stroke.AI uses an artificial intelligence algorithm to analyze images and highlight cases with detected ICH on a standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is presented with a worklist with marked cases of suspected ICH findings. The device does not alter the original medical image, does not remove cases from queue, and is not intended to be used as a diagnostic device. If the clinician does not view the case, or if a case is not flagged, cases remain to be processed per the standard of care.

The results of InferRead CT Stroke.AI are intended to be used in conjunction with other patient information and based on professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care.

InferRead CT Stroke.AI is a radiological computer-assisted triage and notification software device. The software device is a computer program with a deep learning algorithm running on Ubuntu operating system. The device can be deployed as an onsite server in the hospital and the user interacts with the software from a client workstation. The device can be broken down into 4 modules, the NeoViewer, Docking Toolbox, RePACS, and DLServer.

The Docking Toolbox module receives DICOM series and inspects the series against a list of requirements. Series that pass the requirements are sent into the system for prediction for intracranial hemorrhage. Series are processed in a first-out order. When hemorrhage is detected, the system marks the case in the work list prompting the user to conduct preemptive triage and prioritization.

When the user refreshes the page, cases with suspected findings will be marked with an indicator. Cases are identified, such as by Name and Patient ID. A preview is available but is not intended for primary diagnosis and a radiologist must review the case per their standard process. The suspected cases assist in triaging intracranial hemorrhage cases sooner than standard of care practice alone.

Here's a breakdown of the acceptance criteria and study proving the device meets them, based on the provided FDA 510(k) summary for InferRead CT Stroke.AI:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria were implicitly defined by the null hypothesis and target performance goals for sensitivity and specificity. The study aimed to demonstrate statistically significant performance above an 80% threshold for both metrics.

Additional Performance Metrics Reported:

• Area Under the Receiver Operating Characteristic Curve (AUC): 0.962
• InferRead Time-to-Notification: 1.07 ± 0.57 minutes (mean ± SD)
• Standard of Care Time-to-Open-Exam: 75.4 ± 192.7 minutes (mean ± SD)

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

• Sample Size: 369 non-contrast brain CT scans (studies).
• Data Provenance: Obtained from three hospitals in the U.S. The study was retrospective.
• Case Distribution: Approximately equal numbers of positive (51.5% with ICH) and negative (48.5% without ICH) cases.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

• The document states that the ground truth was established by "trained neuro-radiologists."
• It does not specify the exact number of neuro-radiologists or their specific years of experience.

4. Adjudication Method for the Test Set

• The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1). It only states that the ground truth was "established by trained neuro-radiologists." This implies some form of consensus reading or a single expert's definitive diagnosis, but the process is not detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• No, an MRMC comparative effectiveness study was not explicitly described in terms of human readers improving with AI vs. without AI assistance.
• The study did compare the "InferRead time-to-notification" with the "standard of care time-to-open-exam," which suggests a comparison of workflow efficiency with the AI system's notification versus traditional worklist review.
  • Effect Size (Time-to-Notification): InferRead CT Stroke.AI achieved a notification time of 1.07 ± 0.57 minutes, significantly faster than the standard of care time-to-open-exam of 75.4 ± 192.7 minutes (P

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