BriefCase-Quantification

The BriefCase-Quantification software is designed to assist medical specialists by employing an AI-driven, deep-learning algorithm to automatically measure the maximum axial diameter of the abdominal aorta from contrast-enhanced CT DICOM images. Its methodology relies on cloud-based processing and provides an adjunctive quantitative assessment intended to streamline radiological workflow, with performance validated by a mean absolute error of 1.95 mm in a multicenter study.

BriefCase-Quantification by Aidoc Medical, Ltd. is a cloud-based radiological image management and processing software designed to assist medical specialists in analyzing CT exams with contrast of the abdominal aorta in adults (18+). Its primary function is to provide an automated measurement of the maximum abdominal aortic axial diameter for normal and aneurysmal aortas, excluding post-operative cases. The software utilizes an artificial intelligence, deep-learning algorithm that processes filtered DICOM images chronologically. After AI processing, the system generates a preview image annotated with the diameter measurement, which is then sent to an image review software and PACS for user review, emphasizing that these measurements are unofficial and require confirmation by a radiologist. The core science of BriefCase-Quantification lies in its AI-driven algorithm that identifies and measures the abdominal aorta from CT scan series. The device's performance was validated in a retrospective, blinded, multicenter study involving 160 cases from six US clinical sites. The study compared the software's measurements against a ground truth established by three board-certified radiologists. The primary endpoint was successfully achieved, with the software demonstrating a mean absolute error of 1.95 mm (95% CI: 1.59 mm, 2.32 mm) compared to the ground truth. This performance was deemed comparable to a reference device, supporting its utility as an aid in quantitative image analysis, though not as a standalone diagnostic tool.

AI Regulatory Concerns

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Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

• Q1. How does the algorithm differentiate between normal and aneurysmal abdominal aortas, and what is its sensitivity and specificity for detecting aneurysms based on the measured diameter?; What were the specific criteria for case selection in the pivotal study, particularly regarding the range of aortic diameters and the prevalence of aneurysmal versus normal aortas?; Given the mean absolute error of 1.95 mm, what is the clinical impact of this potential measurement discrepancy on patient management, especially for aortas near critical diameter thresholds for intervention?; How does the software's performance vary across different CT scanner manufacturers (Siemens, GE, Philips, Toshiba) and image reconstruction techniques beyond what was detailed in the pivotal study summary?; Beyond maximum axial diameter, does the BriefCase-Quantification software offer or plan to offer any other morphological assessments of the abdominal aorta, such as length, tortuosity, or thrombus burden?