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Accipiolx is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage in the acute care environment. Accipiolx 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.

Accipiolx is not intended to direct attention to specific portions of an image or to anomalies other than acute intracranial hemorrhage. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT cases.

Accipiolx is a software device designed to be installed within healthcare facility radiology networks to identify and prioritize non-contrast head CT (NCCT) scans based on algorithmically-identified findings of acute intracranial hemorrhage (alCH). The device, which utilizes deep learning technologies, facilitates prioritization of CT scans containing findings of alCH. Accipiolx receives CT scans identified by the Accipio Agent or other compatible Medical Image Communications Device (MICD), processes them using algorithmic methods involving execution of multiple computational steps to identify suspected presence of alCH, and generates a results file to be transferred by the Accipio Agent or a similar MICD device for output to a PACS system or workstation for worklist prioritization.

Accipiolx works in parallel to and in conjunction with the standard care of workflow. After a CT scan has been performed, a copy of the study is automatically retrieved and processed by Accipiolx. The device performs identification and classification of objects consistent with alCH, and provides a case-level indicator which facilitates prioritization of cases with potential acute hemorrhagic findings for urgent review.

Here's a breakdown of the acceptance criteria and study details for the MaxQ AI Accipiolx device, based on the provided text:

1. Table of Acceptance Criteria & Reported Device Performance

Note: While specific numerical acceptance criteria (e.g., "Sensitivity must be >= 95%") are not explicitly listed in the document for each metric, the text states that "These results exceeded the predefined performance goals." This implies that the reported performance values were at or above the company's internal targets.

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

• Sample Size for Test Set: 360 newly tested cases.
• Data Provenance: Retrospective study. Cases were collected from multiple sites across 17 US states.

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

The document does not specify the number of experts or their qualifications used to establish the ground truth for the test set. It only mentions that performance was validated by comparing results to "predefined performance goals."

4. Adjudication Method for the Test Set

The document does not describe an explicit adjudication method (e.g., 2+1, 3+1, none) for the test set's ground truth.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, the document does not mention an MRMC comparative effectiveness study where human readers improve with AI vs. without AI assistance. The study focuses on the standalone performance of the AI algorithm.

6. If a Standalone (Algorithm Only) Performance Study Was Done

Yes, a standalone (algorithm only) performance study was done. The reported metrics (Sensitivity, Specificity, Processing Time, NPV, PPV) are for the Accipiolx device's performance in identifying acute intracranial hemorrhage directly from the CT scans.

7. The Type of Ground Truth Used

The document implicitly suggests the ground truth was established by clinical assessment, as the device's output "prioritizes cases with potential acute hemorrhagic findings for urgent review." While not explicitly stated as "expert consensus," the context of a "workflow tool" aiding "clinical assessment" implies a human expert review of the cases to establish the presence or absence of ICH for comparison against the algorithm's output. The comparison of device performance to "predefined performance goals" further supports this.

8. The Sample Size for the Training Set

The document does not specify the exact sample size for the training set. It only states that the device was "developed using training CT cases collected from multiple institutions and CT manufacturers."

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 mentions a "training process" that "included pilot development, optimization of object and feature identification, algorithmic training and selection/optimization of thresholds." This strongly implies that the training data was meticulously labeled for the presence of acute intracranial hemorrhage, likely by medical experts, to enable the deep learning algorithm to learn patterns associated with ICH.

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