Point-of-care AI-assisted stepwise ultrasound pneumothorax diagnosis

• Published in: Physics in medicine & biology Vol. 68; no. 20; pp. 205013 - 205026
• Main Authors: Kim, Kyungsang, Macruz, Fabiola, Wu, Dufan, Bridge, Christopher, McKinney, Suzannah, Al Saud, Ahad Alhassan, Sharaf, Elshaimaa, Sesic, Ivana, Pely, Adam, Danset, Paul, Duffy, Tom, Dhatt, Davin, Buch, Varun, Liteplo, Andrew, Li, Quanzheng
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 21.10.2023
• Subjects: lung sliding classification; pneumothorax; stepwise deep learning; ultrasound; lung sliding classification; stepwise deep learning; ultrasound; pneumothorax
• ISSN: 0031-9155; 1361-6560
• DOI: 10.1088/1361-6560/acfb70

. Ultrasound is extensively utilized as a convenient and cost-effective method in emergency situations. Unfortunately, the limited availability of skilled clinicians in emergency hinders the wider adoption of point-of-care ultrasound. To overcome this challenge, this paper aims to aid less experienced healthcare providers in emergency lung ultrasound scans. . To assist healthcare providers, it is important to have a comprehensive model that can automatically guide the entire process of lung ultrasound based on the clinician's workflow. In this paper, we propose a framework for diagnosing pneumothorax using artificial intelligence (AI) assistance. Specifically, the proposed framework for lung ultrasound scan follows the steps taken by skilled physicians. It begins with finding the appropriate transducer position on the chest to locate the pleural line accurately in B-mode. The next step involves acquiring temporal M-mode data to determine the presence of lung sliding, a crucial indicator for pneumothorax. To mimic the sequential process of clinicians, two DL models were developed. The first model focuses on quality assurance (QA) and regression of the pleural line region-of-interest, while the second model classifies lung sliding. To achieve the inference on a mobile device, a size of EfficientNet-Lite0 model was further reduced to have fewer than 3 million parameters. . The results showed that both the QA and lung sliding classification models achieved over 95% in area under the receiver operating characteristic (AUC), while the ROI performance reached 89% in the dice similarity coefficient. The entire stepwise pipeline was simulated using retrospective data, yielding an AUC of 89%. . The step-wise AI framework for the pneumothorax diagnosis with QA offers an intelligible guide for each clinical workflow, which achieved significantly high precision and real-time inferences.