BraveCX

BraveCX is an artificial intelligence-powered software designed to enhance radiological workflows by rapidly analyzing adult chest X-rays for signs of pleural effusion and pneumothorax. Its deep learning algorithm provides passive notifications to PACS/workstations, enabling prioritization of potentially critical cases for review by clinicians, without making standalone diagnostic claims.

BraveCX is a radiological computer-assisted triage and notification software designed to analyze adult chest X-ray images, encompassing both Anteroposterior (AP) and Posteroanterior (PA) views, for suspected critical findings, specifically pleural effusion and pneumothorax. It utilizes a Deep Learning Artificial Intelligence (AI) algorithm to identify features suggestive of these conditions from DICOM images. The software integrates into existing clinical workflows by receiving these images, analyzing them, and providing a case-level output, denoted as a "flag" or "(blank)", to Picture Archiving and Communication Systems (PACS) or workstations. This output facilitates worklist prioritization, enabling healthcare professionals, such as radiologists or other appropriately trained clinicians, to group potentially critical exams for earlier review. BraveCX operates as a passive notification tool, meaning it does not send direct, proactive alerts to specialists, and its results are intended to aid in prioritization rather than serve as a standalone basis for clinical decision-making. The performance of BraveCX was rigorously evaluated through both internal testing and extensive external independent testing utilizing large, diverse datasets such as MIMIC-CXR, NIH-CXR, and CheXpert, which represent the US population, with ground truths established by board-certified radiologists. In the external validation studies, for pleural effusion (based on 2,509 images, 867 positive), BraveCX achieved a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 0.988, a sensitivity of 92.62%, and a specificity of 98.11%. For pneumothorax (based on 3,245 images, 2,114 positive), the software demonstrated a ROC AUC of 0.972, a sensitivity of 93.38%, and a specificity of 97.27%. These performance metrics were found to be robust and unaffected by variables such as chest X-ray view position, patient sex, age quartiles, BMI quartiles, and various ethnicities. Furthermore, BraveCX demonstrated efficient processing, with a time-to-notification to the worklist ranging from approximately 4.8 to 10.4 seconds for simultaneous prediction of both conditions.

AI Regulatory Concerns

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• Q1. How does the BraveCX algorithm's performance vary with different X-ray equipment manufacturers and image acquisition protocols not extensively represented in the training/testing datasets?; What is the real-world impact on radiologist reading times and diagnostic accuracy for pleural effusion and pneumothorax when BraveCX is integrated into a clinical workflow compared to standard care without AI triage?; Given the passive notification system, what are the optimal strategies for integrating BraveCX flags into diverse PACS/workstation environments to ensure effective worklist prioritization without causing alert fatigue or workflow disruption?; Could the underlying AI model of BraveCX be adapted or retrained to identify other critical thoracic pathologies with similar accuracy, and what would be the data requirements for such an expansion?; How does the algorithm handle edge cases or images with multiple concurrent pathologies, and does the presence of other findings affect its accuracy for detecting pleural effusion or pneumothorax?