(114 days)
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.
| Metric | Acceptance Criteria (Lower Bound 95% CI) | Reported Device Performance (Value with 95% CI) |
|---|---|---|
| Sensitivity | > 80% | 0.916 (95% CI: 0.867-0.951) |
| Specificity | > 80% | 0.922 (95% CI: 0.872-0.957) |
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
§ 892.2080 Radiological computer aided triage and notification software.
(a)
Identification. Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (
e.g., improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use;
(ii) A detailed description of the intended user and user training that addresses appropriate use protocols for the device;
(iii) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.