Prospective Clinical Evaluation of a Deep Learning Algorithm for Guided Point-of-Care Ultrasonography Screening of Abdominal Aortic Aneurysms

Abstract

AbstractBackgroundAbdominal Aortic Aneurysm (AAA) is a critical condition that can lead to fatal consequences if not detected and treated early. Despite the high prevalence in smokers and guideline recommendation for screening, AAA often remains undetected due to availability of diagnostic ultrasound examinations. This prospective clinical trial aimed to investigate the use of a Deep Learning (DL) algorithm to guide AAA screening.MethodsThis prospective, comparative diagnostic study was conducted at the Kaohsiung Chang Gung Memorial Hospital. We developed and deployed an object detection-based DL algorithm providing real-time guidance for novice users performing AAA screening using point of care ultrasound. 10 registered nurses with no prior ultrasonography experience were recruited and performed at least 15 scans on patients over 65 years old to acquire abdominal aorta videos. These scans were compared with those of physicians using the same ultrasound hardware but without DL guidance.ResultsA total of 184 patients (median [IQR] age of 72 [67-79], and 105 (57.1%) male) completed this study. The DL-guided novices achieved adequate scan quality in 87.5% (95% CI: 82.7 - 92.3%) of patients, comparable to the 91.3% (95% CI: 87.2-95.4%) rate of physician scans (p=0.310). This performance did not vary by BMI. The DL model predicted AAA with an AUC of 0.975, showing 100% sensitivity and 94.3% specificity. The DL model predicted the maximal width of abdominal aorta with mean absolute error of 2.8mm compared to physician measurements. 3 AAA with maximal width of aorta > 3cm were found in this study cohort.ConclusionDL-guided POCUS is an effective tool for AAA screening, providing comparable performance to experienced physicians. The use of this DL system could democratize AAA screening and improve access, thereby aiding in early disease detection and treatment.Clinical PerspectiveWhat is NewOur study presents a deep learning (DL) guidance system that enables novice users to perform Abdominal Aortic Aneurysm (AAA) screening with POCUS, yielding image quality comparable to experienced physicians.The DL algorithm accurately identifies AAA from scans conducted by novice users, maintains consistent performance across patients with varying BMIs, and demonstrates increased scan efficiency with repeated use.Clinical ImplicationsDL-guided POCUS can potentially expand AAA screening capabilities to non-specialist settings and increase throughput for screening at risk individuals.The implementation of our DL model for AAA screening could enhance early detection, particularly in underserved areas, but also optimize clinical workflows by decreasing diagnostic wait times and increasing ultrasound utilization efficiency.