Annalise Enterprise CXR Triage Pneumothorax

Annalise Enterprise CXR Triage Pneumothorax is an AI-driven software employing a deep learning convolutional neural network to identify pneumothorax and tension pneumothorax on adult chest X-rays, aiming to improve clinical workflow through worklist prioritization. The system is designed as an assistive tool for trained clinicians, demonstrating high accuracy with an AUC of 0.979 for pneumothorax and efficient triage capabilities.

Annalise Enterprise CXR Triage Pneumothorax is a software tool designed to assist clinicians in the medical care environment by analyzing adult chest X-ray (CXR) images for features suggestive of pneumothorax and tension pneumothorax. It utilizes an artificial intelligence algorithm, specifically a convolutional neural network developed using deep learning techniques trained on over 1,500,000 CXR images from diverse sources. The device integrates with Picture Archiving and Communication Systems (PACS) or Radiology Information Systems (RIS) to make case-level outputs available, enabling worklist prioritization or triage. It is intended for use by trained clinicians qualified to interpret CXRs and is not meant for standalone diagnostic decisions, to rule out these conditions, or to identify anomalies other than pneumothorax and tension pneumothorax; it also does not decrease a study's existing priority. The device's performance was evaluated through standalone accuracy and triage effectiveness assessments. In a retrospective study using 949 unique patient CXR cases (413 positive for pneumothorax, including 123 tension pneumothorax), the AI algorithm demonstrated an Area Under the Curve (AUC) of 0.979 for pneumothorax and 0.988 for tension pneumothorax. At its default 'balanced' operating point, it achieved 93.9% sensitivity and 92.2% specificity for pneumothorax, and 94.3% sensitivity and 95.8% specificity for tension pneumothorax. Additional operating points optimizing for sensitivity or specificity also met performance requirements. A triage effectiveness study on 621 positive cases showed an average triage turn-around time of 20.57 seconds, supporting its utility in expediting the review of critical cases.

AI Regulatory Concerns

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• Q1. How does the algorithm's performance vary with subtle or atypical presentations of pneumothorax versus classic presentations?; What were the specific characteristics of the 123 tension pneumothorax cases within the test dataset, and did performance differ for this critical subset compared to simple pneumothorax?; Given the training data was sourced from three continents, how was dataset shift or variability in imaging protocols across these sources managed during model development and validation?; What is the expected impact on radiologist workload and time-to-diagnosis in a real-world clinical setting beyond the reported bench study triage turn-around time?; How does the device differentiate between true pneumothorax and mimics, such as skin folds or bullous lung disease, and what was the false positive rate for such mimics in the test set?