(205 days)
RADIFY® Triage is a radiological computer-assisted triage and notification software that analyzes adult chest X-ray images for the presence of pre-specified suspected critical findings (pleural effusion and/or pneumothorax).
RADIFY® Triage uses an artificial intelligence algorithm to analyze images for features suggestive of critical findings and provides case-level output available in the PACS for worklist prioritization or triage.
As a passive notification for prioritization-only software tool within the standard of care workflow, RADIFY® Triage does not send a proactive alert directly to the appropriately trained medical specialists. The product is not intended to direct attention to specific portions of an image. Its results are not intended to be used on a stand-alone basis for clinical decision-making. The device does not remove the cases from the queue and does not flag the condition as being absent.
RADIFY® Triage is a radiological computer-assisted prioritization software that utilizes Albased image analysis algorithms to identify pre-specified critical findings (pleural effusion and/or pneumothorax) on frontal (AP and PA) views chest X-ray images and flag the images in the PACS to enable worklist prioritization by the appropriately trained medical specialists who are qualified to interpret chest radiographs. The software does not alter the order or remove cases from the reading queue.
The algorithm was trained on datasets from US and non-USA sources. This training dataset consisted of 93.7% of the data from South Africa, and 6.3% of the data from the USA. The input for RADIFY® Triage is a frontal chest x-ray (AP and PA view) in digital imaging and communications in medicine (DICOM) format.
Chest X-rays are sent to RADIFY® Triage via PACS (Picture Archiving and Communication System (PACS) and processed by the device for analysis. Following receipt of chest x-rays, the software device automatically analyses each image to detect features suggestive of pneumothorax and/or pleural effusion. Chest x-rays without the suspicious findings are placed in the worklist for routine review, which is the standard of care. RADIFY® Triage does not provide any proactive alerts and is not intended to direct attention to specific portions of the image. The results are not intended to be used on a standalone basis for clinical decision-making nor is it intended to rule out the target conditions or otherwise preclude clinical assessment of x-ray cases.
Here's a breakdown of the acceptance criteria and the study details for the Radify® Triage device, based on the provided document:
1. Acceptance Criteria and Reported Device Performance
| Condition | Acceptance Criteria (ROC AUC) | Reported Device Performance (ROC AUC) | Reported Device Sensitivity | Reported Device Specificity |
|---|---|---|---|---|
| Pleural Effusion | > 0.95 | 0.9761 (95% CI: [0.9736, 0.9786]) | 94.39% (95% CI: [93.26, 95.51]) | 96.42% (95% CI: [95.29, 98.00]) |
| Pneumothorax | > 0.95 | 0.9743 (95% CI: [0.9712, 0.9774]) | 94.81% (95% CI: [93.90, 95.73]) | 97.91% (95% CI: [97.00, 98.83]) |
| Overall | N/A (implied by individual) | 0.9762 (95% CI: [0.9743, 0.9781]) | 94.26% (95% CI: [93.53, 94.99]) | 97.27% (95% CI: [96.54, 98.00]) |
| Notification Time | (Implicitly comparable to predicate) | Average of 3 seconds | N/A | N/A |
Note: The document explicitly states the acceptance criteria for performance as "Device shows > 95% AUC".
2. Sample Size and Data Provenance for the Test Set
- Test Set Sample Size:
- Pneumothorax: 2188 scans (1229 with pneumothorax + 959 without pneumothorax).
- Pleural Effusion: 1229 scans (392 with pleural effusion + 837 without pleural effusion).
- Shared Cases: 88 scans had both pleural effusion and pneumothorax co-existing.
- Data Provenance: Retrospective, obtained from three hospitals across the US: one large urban hospital in New York City and three different private clinics in urban and suburban areas in Texas state.
3. Number, Qualifications, and Adjudication Method of Experts for Test Set Ground Truth
- Number of Experts: 3
- Qualifications of Experts: Board-certified ABR (USA) radiologists with a minimum of 11 years of experience.
- Adjudication Method: Not explicitly stated, but the phrase "The ground truth was established by 3 board-certified ABR (USA) radiologists" implies a consensus-based approach, likely a majority vote or discussion to reach agreement. It does not specify 2+1 or 3+1, but suggests a similar process.
4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- Was an MRMC study done? No, the document describes a standalone (algorithm only) performance evaluation against a radiologist-established ground truth. It does not mention a study to compare human reader performance with and without AI assistance.
- Effect size of human readers improving with AI vs. without AI assistance: Not applicable as an MRMC comparative effectiveness study was not performed or reported.
5. Standalone Performance Study
- Was a standalone study done? Yes, the document details the performance of the RADIFY® Triage algorithm alone, analyzing chest X-ray images for pneumothorax and pleural effusion. The reported metrics (AUC, sensitivity, specificity) are for the algorithm's performance in detecting these conditions compared to the established ground truth.
6. Type of Ground Truth Used
- Type of Ground Truth: Expert consensus, established by 3 board-certified ABR (USA) radiologists.
7. Sample Size for the Training Set
- Training Set Sample Size: Not explicitly stated as a total number of images, but the composition is given: "The algorithm was trained on datasets from US and non-USA sources. This training dataset consisted of 93.7% of the data from South Africa, and 6.3% of the data from the USA."
8. How Ground Truth for the Training Set Was Established
- How Ground Truth Was Established: Not explicitly detailed for the training set. The document only states that the algorithm was trained on datasets and then evaluated on a separate, independent test set where the ground truth was established by the 3 expert radiologists. It's common practice for training data ground truth to be established through similar expert review processes, but this specific detail is not provided for the training data in the given text.
§ 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.