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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:

· obstructive hydrocephalus

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 a specific finding, or otherwise preclude clinical assessment of CTB studies

Intended modality: Annalise Enterprise identifies the suspected finding in non-contrast brain CT studies.

Intended user:

The device is intended to be used by trained clinicians who, as part of their scope of practice, are qualified to interpret brain CT studies.

Intended patient population: The intended population is patients who are 22 years or older.

Annalise Enterprise CTB Triage - OH 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 obstructive hydrocephalus.

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 including Toshiba, GE Medical Systems, Siemens. Philips, and Canon Medical Systems. This dataset, which contained over 200.000 CT brain imaging studies, was annotated by qualified and trained radiologists.

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.

The provided text describes the acceptance criteria and the study that proves the device meets those criteria for the Annalise Enterprise CTB Triage - OH device.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the device's performance are implicitly defined by the reported sensitivity and specificity values in the pivotal standalone performance study. The device is intended to establish effective triage for "obstructive hydrocephalus." At various operating points, the device demonstrates high sensitivity and specificity.

In addition to the sensitivity and specificity, the device demonstrated a triage turn-around time of 81.6 (95% CI: 80.3 - 82.9) seconds, which was considered substantially equivalent to the predicate device.

2. Sample Size Used for the Test Set and Data Provenance

The test set for the standalone performance evaluation was:

• Sample Size:
  • 175 cases for slice thickness ≤1.5mm (75 positive, 100 negative)
  • 191 cases for slice thickness >1.5mm & ≤5.0mm (84 positive, 107 negative)
• Data Provenance: Retrospective, anonymized cases collected consecutively from five US hospital network sites, including both community hospitals and academic medical centers. The dataset was newly acquired and independent from the training dataset.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• Number of Experts: At least two neuroradiologists, with a third neuroradiologist used in the event of disagreement.
• Qualifications: US board-certified neuroradiologists, ABR-certified and protocol-trained (for the ground truth determination process).

4. Adjudication Method for the Test Set

The adjudication method used was 2+1 consensus. Ground truth was determined by two ground truthers, and a third ground truther was used in the event of disagreement.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and its Effect Size

No MRMC study comparing human readers with AI vs. without AI assistance was explicitly mentioned. The study focused on the standalone performance of the algorithm and its triage effectiveness (turn-around time) as an aid to prioritize worklists. The statement "[The device] enables users to review the studies containing features suggestive of these radiological findings earlier than in the standard clinical workflow" implies an improvement in workflow, but a direct MRMC study quantifying effect size on human reader performance was not provided in this document.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done

Yes, a standalone performance evaluation was conducted. The performance data presented in the table above (sensitivity and specificity) are results from this standalone evaluation.

7. The Type of Ground Truth Used

The ground truth used was expert consensus, specifically from US board-certified neuroradiologists using a 2+1 adjudication method.

8. The Sample Size for the Training Set

The training dataset contained over 200,000 CT brain imaging studies.

9. How the Ground Truth for the Training Set was Established

The images used to train the algorithm were sourced from datasets that were annotated by qualified and trained radiologists.

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