Diagnosing pulmonary embolism (PE) is still challenging due to other conditions that can mimic its appearance, leading to incomplete or delayed management and several inter-observer variabilities. This study evaluated the performance and clinical utility of an artificial intelligence (AI)-based application designed to assist clinicians in the detection of PE on CT pulmonary angiography (CTPA). CTPAs from 230 US cities acquired on 57 scanner models from 6 different vendors were retrospectively collected. Three US board certified expert radiologists defined the ground truth by majority agreement. The same cases were analyzed by CINA-PE, an AI-driven algorithm capable of detecting and highlighting suspected PE locations. The algorithm's performance at a per-case and per-finding level was evaluated. Furthermore, cases with PE not mentioned in the clinical report but correctly detected by the algorithm were analyzed. A total of 1204 CTPAs (mean age 62.1 years ± 16.6[SD], 44.4 % female, 14.9 % positive) were included in the study. Per-case sensitivity and specificity were 93.9 % (95%CI: 89.3 %–96.9 %) and 94.8 % (95%CI: 93.3 %–96.1 %), respectively. Per-finding positive predictive value was 89.5 % (95%CI: 86.7 %–91.9 %). Among the 196 positive cases, 29 (15.6 %) were not mentioned in the clinical report. The algorithm detected 22/29 (76 %) of these cases, leading to a reduction in the miss rate from 15.6 % to 3.8 % (7/186). The AI-based application may improve diagnostic accuracy in detecting PE and enhance patient outcomes through timely intervention. Integrating AI tools in clinical workflows can reduce missed or delayed diagnoses, and positively impact healthcare delivery and patient care.

中文翻译：

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用于肺栓塞检测的人工智能工具的性能和临床实用性


诊断肺栓塞（PE）仍然具有挑战性，因为其他情况可以模仿其外观，导致治疗不完整或延迟以及观察者之间的一些差异。本研究评估了基于人工智能 (AI) 的应用程序的性能和临床实用性，该应用程序旨在帮助临床医生通过 CT 肺血管造影 (CTPA) 检测 PE。回顾性收集了来自 230 个美国城市的来自 6 个不同供应商的 57 款扫描仪型号的 CTPA。三名美国委员会认证的专家放射科医生以多数同意的方式定义了基本事实。 CINA-PE 分析了相同的病例，CINA-PE 是一种人工智能驱动的算法，能够检测和突出显示可疑的 PE 位置。评估了算法在每个案例和每个发现级别的性能。此外，还分析了临床报告中未提及但算法正确检测到的 PE 病例。该研究共纳入 1204 名 CTPA（平均年龄 62.1 岁 ± 16.6[SD]，44.4% 女性，14.9% 阳性）。每个病例的敏感性和特异性分别为 93.9% (95%CI: 89.3%–96.9%) 和 94.8% (95%CI: 93.3%–96.1%)。每次发现的阳性预测值为 89.5%（95%CI：86.7%–91.9%）。在196例阳性病例中，有29例（15.6%）在临床报告中未提及。该算法检测到了 22/29 (76%) 的案例，从而将漏检率从 15.6% 降低到 3.8% (7/186)。基于人工智能的应用程序可以提高PE检测的诊断准确性，并通过及时干预改善患者的治疗效果。将人工智能工具集成到临床工作流程中可以减少漏诊或延迟诊断，并对医疗保健服务和患者护理产生积极影响。