(233 days)
HALO is a notification only cloud-based image processing software artificial intelligence algorithms to analyze patient imaging data in parallel to the standard of care imaging interpretation. Its intended use is to identify suggestive imaging patterns of a pre-specified clinical condition and to directly notify an appropriate medical specialist.
HALO's indication is to facilitate the evaluation of the brain vasculature on patients suspected of stroke by processing and analyzing contrast enhanced CT angiograms of the brain acquired in an acute setting. After completion of the data analysis. HALO sends a notification if a pattern suggestive for a suspected intracranial Large Vessel Occlusion (LVO) of the anterior circulation (ICA, M1 or M2) has been identified in an image.
The intended users of HALO are defined as appropriate medical specialists that are involved in the diagnosis and care of stroke patients at emergency department where stroke patients are administered. They include physicians such as neurologists, and/or other emergency department physicians.
HALO's output should not be used for primary diagnosis or clinical decisions; the final diagnosis is always decided upon by the medical specialist. HALO is indicated for CT scanners from GE Healthcare.
HALO is a notification only, cloud-based clinical support tool which identifies image features and communicates the analysis results to a specialist in parallel to the standard of care workflow.
HALO is designed to process CT angiograms of the brain and facilitate evaluation of these images using artificial intelligence to detect patterns suggestive of an intracranial large vessel occlusion (LVO) of the anterior circulation.
A copy of the original CTA images is sent to HALO cloud servers for automatic image processing. After analyzing the images, HALO sends a notification regarding a suspected finding to a specialist, recommending review of these images. The specialist can review the results remotely in a compatible DICOM web viewer.
Here's a summary of the acceptance criteria and study details for the HALO device, based on the provided FDA 510(k) summary:
HALO Device Acceptance Criteria and Study Details
1. Table of Acceptance Criteria and Reported Device Performance
| Metric | Acceptance Criteria (Implicit) | Reported Device Performance (HALO) |
|---|---|---|
| Sensitivity | Sufficiently high for LVO detection (comparable to predicate) | 91.1% (95% CI, 86.0%-94.8%) |
| Specificity | Sufficiently high for LVO detection (comparable to predicate) | 87.0% (95% CI, 81.2%-91.5%) |
| AUC | High (indicative of good discriminative power) | 0.97 |
| Notification Time | Fast enough for acute stroke setting (comparable to predicate) | Median: 4 minutes 31 seconds. Range: 3:47 to 7:12 |
| Substantial Equivalence | Equivalent to predicate device ContaCT in terms of indications for use, technological characteristics, and safety and effectiveness. | Concluded to be substantially equivalent. |
2. Sample Size and Data Provenance for Test Set
- Sample Size: 348 CTA scans were initially collected, with 364 patients included for further analysis after exclusion. It's unclear if the "348 CTA scans" and "364 patients" refer to the same dataset or if some patients had multiple scans or if there was an expansion of the dataset. Assuming 364 cases (patients with at least one scan) were used.
- Data Provenance: Retrospective evaluation in a consecutive patient cohort. Data was collected from US comprehensive stroke centers.
3. Number and Qualifications of Experts for Ground Truth
- Number of Experts: 3 neuro radiologists.
- Qualifications: "Neuro radiologists" implies specialized training and experience in interpreting neurological imaging, which is appropriate for stroke diagnosis. Specific years of experience are not mentioned.
4. Adjudication Method for Test Set
The adjudication method is not explicitly stated. It says "Ground truth was established by an expert panel consisting of 3 neuro radiologists," which suggests a consensus-based approach, but the specific rule (e.g., majority vote, unanimous agreement, review by a lead expert if disagreement) is not provided.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No, an MRMC comparative effectiveness study involving human readers with and without AI assistance was not explicitly mentioned or conducted as detailed in the summary. The study focused on the standalone performance of the HALO algorithm.
6. Standalone Performance (Algorithm Only)
Yes, a standalone study was performed. The clinical study retrospectively evaluated the performance of the HALO clinical decision support algorithm for LVO detection using the collected CTA scans. The reported sensitivity, specificity, AUC, and notification time are all measures of the algorithm's standalone performance.
7. Type of Ground Truth Used
Expert Consensus. The ground truth for the test set was established by an expert panel consisting of 3 neuro radiologists.
8. Sample Size for Training Set
The sample size for the training set is not explicitly mentioned in the provided document. The document only covers the evaluation of the algorithm.
9. How Ground Truth for Training Set was Established
How the ground truth was established for the training set is not explicitly mentioned in the provided document. ("database of images" is stated for the core algorithm, but not how ground truth was applied to them).
§ 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.