BriefCase

Aidoc's BriefCase is an artificial intelligence-driven software designed to analyze adult frontal chest X-rays, using a deep-learning algorithm to identify and notify clinicians about suspected vertical malposition of endotracheal tubes relative to the carina to expedite triage. The system processes DICOM images via cloud-based analysis and demonstrated high sensitivity (86.81%) and specificity (92.02%) in a multicenter study, significantly reducing the time-to-notification for potentially critical ETT placements compared to standard care.

Aidoc Medical's BriefCase is a radiological computer-aided triage and notification software designed for the analysis of frontal chest X-ray (CXR) images in adults and transitional adolescents aged 18 and older. Its primary purpose is to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communicating suspected positive cases of vertically malpositioned endotracheal tubes (ETT) in relation to the carina, specifically when the ETT distal tip is assessed as being more than 5 cm above, less than 2 cm above, or below the carina. The system utilizes an artificial intelligence algorithm to process DICOM images, operating through distinct modules for image acquisition (Aidoc Hospital Server), cloud-based image processing (Aidoc Cloud Server), and workflow integration with notifications (Aidoc Desktop Application), all functioning in parallel to standard care without altering original images. The device's efficacy was established in a retrospective, multicenter pivotal study involving 921 CXR cases, where its performance was compared against a ground truth determined by a majority vote of three senior board-certified radiologists. BriefCase achieved a sensitivity of 86.81% and a specificity of 92.02% in identifying ETT malposition. A significant finding was the reduction in the mean time-to-notification to 4.93 minutes, substantially faster than the standard care's mean time-to-exam-open of 157.0 minutes. The device has limitations: it assesses only vertical ETT position, does not account for patient positioning, cannot detect esophageal intubation, fails if the carina is poorly visualized, and may generate false alerts for correctly positioned double lumen ETTs due to its inability to discriminate ETT types.

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's performance vary with different X-ray scanner manufacturers or image quality variations not extensively covered in the pivotal study?; What is the specific false positive rate and clinical impact for correctly positioned double lumen ETTs, and are there algorithmic adjustments planned to address this limitation?; To what extent does the presence of significant co-existing thoracic pathologies (e.g., large pleural effusions, severe atelectasis) affect the algorithm's ability to accurately identify the carina and ETT tip, and what is the failure analysis rate in such complex cases?; Could the approximately 5-minute processing and notification time be further optimized, and what are the primary bottlenecks in the image acquisition-to-notification pipeline (e.g., de-identification, cloud upload, analysis)?; Beyond time-to-notification, are there any data on how the use of BriefCase impacts downstream clinical outcomes or resource utilization, such as rates of re-intubation or length of ICU stay?