BriefCase, RIB Fractures Triage (RibFx)

BriefCase for RibFx Triage is an artificial intelligence-powered software designed to expedite the identification of acute rib fractures on chest CT scans by providing rapid notifications to radiologists, thereby optimizing workflow prioritization. Its methodology involves DICOM image analysis using a deep learning algorithm, which has demonstrated high sensitivity and specificity in detecting target pathologies and significantly reducing the time to radiological review for critical findings.

BriefCase for RibFx Triage is a radiological computer-assisted software designed to aid medical specialists in workflow triage by identifying and flagging suspected acute rib fractures in chest CT scans. The system utilizes an artificial intelligence algorithm to analyze DICOM images acquired via a hospital server and processed on a cloud server. Suspected cases of three or more acute rib fractures trigger notifications on a dedicated worklist application on the radiologist's desktop. These notifications, which include patient identifiers and the suspected pathology, also offer a compressed, non-diagnostic preview image to prompt the radiologist to prioritize review of the original images in the PACS, operating in parallel with the standard of care without altering images or the reading queue. The software's performance was evaluated in a multicenter study involving 279 chest CT cases. It demonstrated high accuracy in identifying three or more acute rib fractures, achieving an Area Under the Curve (AUC) of 0.976, a sensitivity of 96.7%, and a specificity of 90.4%. A key clinical benefit highlighted was the potential for significant time savings in radiological workflow. For true positive cases, the software's mean time-to-notification was 4.2 minutes, compared to a standard of care mean time-to-exam-open of 89.4 minutes, suggesting an average potential time saving of over an hour in initiating review for critical cases. The Negative Predictive Value (NPV) was 99.6%, while the Positive Predictive Value (PPV) was 52.9%.

AI Regulatory Concerns

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• Q1. How does the algorithm's performance (sensitivity, specificity, AUC) vary with different CT scanner models, acquisition parameters (e.g., slice thickness variations within the 0.5-5.0mm range), or image noise levels typically encountered in clinical practice?; Given the PPV of 52.9%, what is the anticipated impact on radiologist workload due to false positive notifications, and are there strategies to mitigate this, such as adjustable notification thresholds?; The study demonstrated an average time saving of 85.2 minutes for true positive cases; what is the distribution of this time saving, and how does it vary across different hospital settings (e.g., academic vs. community, ED vs. inpatient)?; How does the 'compressed, low-quality, grayscale, unannotated image' preview feature influence the radiologist's initial assessment and decision to prioritize, and is there a risk of misinterpretation even if labeled 'not for diagnostic use'?; What are the specific exclusion criteria for 'technically inadequate' studies (e.g., motion artifacts, severe metal artifacts), and how robust is the software in identifying and handling such studies before they lead to erroneous notifications?