(133 days)
Annalise Enterprise is a device designed to be used in the medical care environment to aid in triage and prioritization of studies with features suggestive of the following finding: mass effect . The device analyzes studies using an artificial intelligence algorithm to identify the finding. It makes study-level output available to an order and imaging management system for worklist prioritization or triage. The device is not intended to direct attention to specific portions of an image and only provides notification for the suspected finding. Its results are not intended: to be used on a standalone basis for clinical decision making ● ● to rule out specific findings, or otherwise preclude clinical assessment of CTB studies
Annalise Enterprise CTB Triage Trauma is a software workflow tool which uses an artificial intelligence (AI) algorithm to identify suspected findings on non-contrast brain CT studies in the medical care environment. The findings identified by the device include mass effect. Radiological findings are identified by the device using an AI algorithm - a convolutional neural network trained using deep-learning techniques. Images used to train the algorithm were sourced from datasets that included a range of equipment manufacturers and models. The performance of the device's AI algorithm was validated in a standalone performance evaluation, in which the case-level output from the device was compared with a reference standard ('ground truth'). This was determined by two ground truthers, with a third truther used in the event of disagreement. All truthers were US board-certified neuroradiologists. The device interfaces with image and order management systems (such as PACS/RIS) to obtain noncontrast brain CT studies for processing by the AI algorithm. Following processing, if any of the radiological findings of interest are identified in a non-contrast brain CT study, the device provides a notification to the image and order management system for prioritization of that study in the worklist. This enables users to review the studies containing features suggestive of these radiological findings earlier than in the standard clinical workflow. It is important to note that the device will never decrease a study's existing priority in the worklist. This ensures that worklist items will never have their priorities downgraded based on AI results. The device workflow is performed parallel to and in conjunction with the standard clinical workflow for interpretation of non-contrast brain CTs. The device is intended to aid in prioritization and triage of radiological medical images only.
Here is a summary of the acceptance criteria and study proving device performance, based on the provided text:
Device Name: Annalise Enterprise CTB Triage Trauma
Manufacturer: Annalise-AI Pty Ltd.
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state "acceptance criteria" for sensitivity and specificity in a separate table. Instead, it provides the results of the standalone performance study in a table, implying these values meet the unstated criteria for demonstrating safety and effectiveness. The comparison section further states that these results are "substantially equivalent to those of the predicate device."
| Finding | Slice Thickness Range | Operating Point | Sensitivity % (Se) (95% CI) | Specificity % (Sp) (95% CI) |
|---|---|---|---|---|
| Mass Effect | ≤1.5mm | 0.160195 | 97.0 (95.3, 98.4) | 88.7 (83.5, 94.0) |
| ≤1.5mm | 0.221484 | 96.6 (94.9, 98.2) | 89.5 (84.2, 94.0) | |
| Mass Effect | >1.5mm & ≤5.0mm | 0.120944 | 96.8 (95.3, 98.1) | 89.3 (84.5, 93.5) |
| 0.160195 | 95.3 (93.6, 97.0) | 92.9 (88.7, 96.4) |
Additional Performance Metric (Triage Effectiveness):
- Triage Turn-around Time: 81.6 seconds (95% CI: 80.3 - 82.9)
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size:
- For slice thickness ≤1.5mm: 626 cases (493 positive, 133 negative for mass effect)
- For slice thickness >1.5mm & ≤5.0mm: 762 cases (594 positive, 168 negative for mass effect)
- Data Provenance: Retrospective, anonymized study. Collected consecutively from five US hospital network sites.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
- Number of Experts: At least two neuroradiologists for initial review, with a third neuroradiologist for adjudication in case of disagreement.
- Qualifications: All experts were US board-certified neuroradiologists, ABR-certified, and protocol-trained.
4. Adjudication Method for the Test Set
- Method: Consensus determined by two ground truthers. If there was a disagreement between the initial two ground truthers, a third ground truther was used to reach consensus (2+1 method).
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
- An MRMC study was not explicitly described as being performed for AI-assisted human reader performance improvement.
- The performance assessment focused on standalone performance of the AI algorithm and a triage effectiveness (turn-around time) study, which was an internal bench study. The document states the AI "enables users to review the studies containing features suggestive of these radiological findings earlier than in the standard clinical workflow," implying an effect on human workflow, but doesn't quantify reader improvement in an MRMC setting.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
- Yes, a standalone performance evaluation was done. The sections under "Performance Testing" describe comparing the device's case-level output with a reference standard (ground truth). The results in the table above (Sensitivity, Specificity) are from this standalone performance evaluation.
7. The Type of Ground Truth Used
- Type: Expert consensus (from multiple board-certified neuroradiologists).
8. The Sample Size for the Training Set
- The document states, "The test dataset used during the standalone performance evaluation was newly acquired and independent from the training dataset used in model development."
- The sample size of the training set is not explicitly provided in the given text. It only mentions that "Images used to train the algorithm were sourced from datasets that included a range of equipment manufacturers and models."
9. How the Ground Truth for the Training Set Was Established
- The document does not explicitly describe how the ground truth for the training set was established. It only details the ground truth establishment for the test set used in the standalone performance evaluation.
§ 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.