(261 days)
The red dot™ software platform is a software workflow tool designed to aid the clinical assessment of adult Chest X-Ray cases with features suggestive of Pneumothorax in the medical care environment. red dot™ analyzes cases using an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. red dot™ is not intended to direct attention to specific portions of an image or to anomalies other than Pneumothorax. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out Pneumothorax or otherwise preclude clinical assessment of X-Ray cases.
Behold.ai red dot™ is a radiological computer-assisted triage and notification software system. The software automatically analyzes PA/AP chest x-rays and alerts the PACS/RIS workstation once images with features suggestive of pneumothorax are identified.
Through the use of the red dot™ device, a radiologist is able to review studies with features suggestive of pneumothorax earlier than in standard of care workflow.
In summary, the red dot™ device provides a passive notification through the PACS/workstation to the radiologists indicating the existence of a case that may potentially benefit from prioritization. It doesn't output an image and therefore it does not mark, highlight, or direct users' attention to a specific location on the original chest X ray.
The device aim is to aid in prioritization and triage of radiological medical images only.
The main components of the red dot™ device are described below.
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Image input, validation and anonymization
After a chest x-ray has been performed, a copy of the study is received and processed by the red dot™ device. Following receipt of a study, the validation feature ensures the image is valid (i.e. has readable pixels) and the anonymization feature removes or anonymizes Personally Identifiable Information (PII) such as Patient Name, Patient Birthdate, and Patient Address. -
red dot™ Image Analysis Algorithm
This component of the device is primarily comprised of the visual recognition algorithm that is responsible for detecting images with potential abnormalities. Once a study has been validated, the algorithm analyzes the frontal chest x-ray for detection of suspected findings suggestive of pneumothorax. -
PACS Integration Feature
The results of a successful study analysis is provided to an integration engine in a standard JSON message containing sufficient information to allow the integration engine to notify the PACS/workstation for prioritization through the worklist interface.
Here's a breakdown of the acceptance criteria and the study proving the red dot™ device meets them, based on the provided document:
Acceptance Criteria and Device Performance
1. Table of Acceptance Criteria and Reported Device Performance
| Metric | Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|---|
| AUROC | > 0.95 (as stated for "prespecified performance goals") | 0.975 (95% CI: [0.966 - 0.984]) |
| Sensitivity | Lower bound of 95% CI > 80% | 94.65% (95% CI: [91.46 - 96.91]) |
| Specificity | Lower bound of 95% CI > 80% | 87.95% (95% CI: [85.04 - 90.46]) |
| Accuracy | Not explicitly stated as an acceptance criterion bound beyond the above, but reported. | 90.20% (95% CI: [88.06 - 92.08]) |
| Processing Time (red-dot™) | Substantially equivalent to predicate (Zebra HealthPNX: 22.1 seconds) | 13.8 seconds (Mean, 95% CI: [13.0 - 14.5]) |
| Notification Transit Time | Implied to be part of overall timing comparison with predicate | 15.5 seconds (Average from 3 live customer sites) |
| Total red dot™ Performance Time | Substantially equivalent to predicate (Zebra HealthPNX: 22.1 seconds) | 29.3 seconds |
Note on Acceptance Criteria: The document explicitly states that the AUROC was above 0.95 and "all lower bounds of the 95% confidence intervals exceeded 80% and achieved the prespecified performance goals in the study" for the classification metrics (AUROC, Sensitivity, Specificity). For the timing, the acceptance criterion is defined as being "substantially equivalent" to the predicate.
Study Details Proving Device Meets Acceptance Criteria
2. Sample Size Used for the Test Set and Data Provenance
- Test Set Sample Size: 888 CXR images.
- Data Provenance: Retrospective, anonymous study.
- Country of Origin: United States (n=738 cases from 2 clinical sites) and United Kingdom (n=150 cases from 2 clinical sites).
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
- Number of Experts: At least two ABR certified radiologists reviewed each CXR image. A third reader was involved in the event of disagreement/discrepancy.
- Qualifications of Experts: All readers were "ABR certified radiologists" and "received training related to imaging findings defining each condition per protocol prior to the review."
4. Adjudication Method for the Test Set
- Adjudication Method: "The ground truth was determined by two readers with a third reader in the event of disagreement/discrepancy." Ground truth for a condition was defined as agreement between two readers. This is a 2+1 adjudication method.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
- MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study was not reported as having been done to directly compare human readers with and without AI assistance. The study described is a standalone performance validation of the AI algorithm against a consensus ground truth.
6. If a Standalone Performance Study Was Done
- Standalone Study: Yes, a standalone (algorithm only without human-in-the-loop performance) study was explicitly done. The reported metrics (AUROC, Accuracy, Sensitivity, Specificity) are for the red dot™ algorithm's performance in detecting pneumothorax.
7. The Type of Ground Truth Used
- Type of Ground Truth: Expert consensus. Specifically, "agreement between two readers" from ABR certified radiologists, with a third radiologist for discrepancy resolution.
8. The Sample Size for the Training Set
- Training Set Sample Size: The document does not specify the sample size for the training set. It only describes the test set.
9. How the Ground Truth for the Training Set Was Established
- Training Set Ground Truth Establishment: The document does not provide details on how the ground truth for the training set was established. It only focuses on the data used for the performance evaluation (test set).
§ 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.