(84 days)
Rapid PE Triage and Notification (PETN) is a radiological computer aided triage and notification software indicated for use in the analysis of CTPA images. The device is intended to assist hospital networks and trained clinicians in workflow triage by flagging and communication of suspected positive findings of central pulmonary embolism (PE) pathology in adults. The software is only intended to be used on single-energy exams.
Rapid PETN uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings 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 findings. Notifications include compressed preview images that 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 intended to be used as a diagnostic device.
The results of Rapid PETN are intended to be used in conjunction with other patient information and based on ther professional judgment, to assist with trage/proritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care. Rapid PETN is validated for use on GE, Siemens and Toshiba scanners.
Rapid PETN is a radiological computer-assisted triage and notification software device. The Rapid PETN module is a contrast enhanced CTPA processing module which operates within the integrated Rapid Platform to provide triage and notification of suspected Central Pulmonary Emboli (PE). The PETN module is an AI/ML module. The output of the module is a priority notification to clinicians indicating the suspicion of central PE based on positive findings. The Rapid PETN 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 summary of the acceptance criteria and study details for iSchemaView Inc.'s Rapid PE Triage and Notification (PETN) device, based on the provided text:
1. Acceptance Criteria and Reported Device Performance
The acceptance criteria were defined by the primary endpoint of the standalone performance validation study.
| Acceptance Criteria (Primary Endpoint) | Reported Device Performance |
|---|---|
| Sensitivity ≥ 0.96 (presumably lower bound of CI) | 0.96 (95% CI: 0.92 - 0.98) |
| Specificity ≥ 0.89 (presumably lower bound of CI) | 0.89 (95% CI: 0.83 - 0.93) |
| Processing Time (Secondary Endpoint) | 2.64 minutes (2.34-4.80 min) |
2. Sample Size and Data Provenance
- Test Set (Final Performance Validation): 306 CTPA cases.
- Data Provenance: The text does not explicitly state the country of origin. It mentions "multiple sites" for the development data. The study appears to be retrospective as it uses existing CTPA cases.
3. Number of Experts and their Qualifications for Ground Truth
- Number of Experts: 3 experts
- Qualifications: The document does not explicitly state the qualifications (e.g., radiologist with specific experience) of these experts.
4. Adjudication Method for the Test Set
- The ground truth was established by "3 experts using a 2:3 confirmation." This indicates a consensus-based approach where at least two out of three experts had to agree for a particular finding to be considered ground truth.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned. The study focused on the standalone performance of the AI algorithm for triage and notification, not on how human readers' performance improved with AI assistance.
6. Standalone Performance (Algorithm Only)
- Yes, a standalone (algorithm only without human-in-the-loop performance) study was conducted. The "Final device validation included standalone performance validation."
7. Type of Ground Truth Used
- The ground truth was established by expert consensus (3 experts using a 2:3 confirmation).
8. Sample Size for the Training Set
- Algorithm Development (Training and Development Validation): 600 CTPA cases (300 Positive, 300 Negative).
- Training Cases: 480 cases (240 Positive, 240 Negative).
- Additionally: An extra 276 negative cases were included to further assess specificity. It's not explicitly stated if these were solely for development validation or also incorporated into a later training phase, but their purpose was for model assessment.
9. How the Ground Truth for the Training Set Was Established
- The document implies that the ground truth for the 600 cases used in algorithm development (training and initial validation) was established in a similar manner to the final validation, but it doesn't explicitly detail the method (e.g., number of experts, adjudication) for the training set itself. Given the context of medical device development, it is highly probable that ground truth for the training set was also established by expert review, likely with a consensus process, to ensure high-quality labels for model training.
§ 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.