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DeepCT is a notification-only, parallel workflow tool for use by hospital networks and trained clinicians to identify and communicate images of specific patients to a specialist, independent of standard of care workflow. DeepCT uses an artificial intelligence algorithm to analyze images for findings suggestive of a pre-specified clinical condition and to notify an appropriate medical specialist of these findings in parallel to standard of care image interpretation. Identification of suspected findings is not for diagnostic use beyond notification. Specifically, the device analyzes non-contrast CT images of the brain acquired in the acute setting and sends notifications to a specialist that a suspected ICH (intracranial hemorrhage) has been identified and recommends review of those images. Notified clinicians are responsible for viewing non-contrast CT images of the brain on a diagnostic viewer and engaging in appropriate patient evaluation and relevant discussion with a treating specialist before making care-related decisions or requests. DeepCT 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.

This software is used to analyze the head computed tomography image of a patient suspected of having intracranial hemorrhage and/or hematoma (hereinafter referred to as "ICH"). Provide a "present" situation (with ICH) notification, send a text message to the user.

DeepCT (Ver. 4.1.4) is a software-only device that uses two components: (1) Image Forwarding Software and (2) Image Processing and Analysis Server.

(1) The Image Forwarding Software is configured by the hospital to be used on a computer and is responsible for transmitting a copy of DICOM files from the local through a secured channel to the Image Processing and Analysis Server.

When the Image Forwarding Software receives the interpretation result from the Image Processing and Analysis Server, it shows the result on the screen. If there is a suggestive of ICH, the Image Forwarding Software sends a notification to the specialist identifying the study of interest. While the software informs the notification process, no other diagnostic information is generated from the software or available to the user beyond the notification.

(2) The Image Processing and Analysis Server is responsible for receiving, assembling, processing, analyzing and storing DICOM images. This component includes the algorithm that is responsible for identifying and quantifying image characteristics that are consistent with a ICH and transmit the result back to the Image Forwarding Software.

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

1. Acceptance Criteria and Reported Device Performance

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

• Sample Size: 260 cases
• Data Provenance: Retrospective, multicenter, multinational.
  • Countries: 5 clinical sites (2 US and 3 OUS - Outside US). Specific countries are not mentioned beyond "US" and "OUS".
  • Distribution: 130 cases from US sites and 130 cases from OUS sites.
  • Case Balance: Approximately an equal number of positive (images with ICH) and negative (images without ICH) cases.

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

The document does not specify the number of experts used to establish ground truth for the test set or their qualifications. It only states that the study evaluated "the software's performance in identifying non-contrast CT head images containing ICH findings," implying an established ground truth, but details are absent.

4. Adjudication Method for the Test Set

The document does not specify the adjudication method used for the test set.

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

No. The document describes a standalone study evaluating the algorithm's performance against a pre-established ground truth. It does not mention a comparative effectiveness study involving human readers with and without AI assistance.

6. Standalone Performance Study

Yes. The study described in the "Performance Testing" section is a standalone study of the algorithm's performance. It evaluates DeepCT's sensitivity, specificity, and processing time in identifying ICH without human-in-the-loop performance measurement.

7. Type of Ground Truth Used

The document implies a ground truth based on the presence or absence of "ICH findings" in the images, but it does not explicitly state the method used to establish this ground truth (e.g., expert consensus, pathology, outcomes data).

8. Sample Size for the Training Set

Radiology records were collected from 21,603 patients who underwent head CT scans between 2007 and 2017. This dataset was used for DeepCT development and deployment. It is implied this was the training set, or at least a significant portion of it.

9. How the Ground Truth for the Training Set Was Established

The document states: "The Tri-Service General Hospital Institutional Review Board, Kaohsiung Veterans General Hospital Institutional Review Board and National Taiwan University Hospital Research Ethics Committee all approved and consented the use of the retrospective image data for DeepCT development and deployment without relevant ethical concern."

While Institutional Review Board (IRB) approval is mentioned for the use of the data, the document does not explicitly describe how the ground truth labels (i.e., presence or absence of ICH) were established for this large training dataset. It only refers to "radiology records" and the "retrospective image data." It's highly probable that these labels were derived from radiologists' interpretations in the original radiology reports, but this is not definitively stated.

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