Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010, Bern, Switzerland.
Department of Emergency Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Sci Rep. 2024 Sep 28;14(1):22447. doi: 10.1038/s41598-024-73435-3.
The study aimed to evaluate the impact of AI assistance on pulmonary nodule detection rates among radiology residents and senior radiologists, along with assessing the effectiveness of two different commercialy available AI software systems in improving detection rates and LungRADS classification in chest CT. The study cohort included 198 participants with 221 pulmonary nodules. Residents' mean detection rate increased significantly from 64 to 77% with AI assist, while seniors' detection rate remained largely unchanged (85% vs. 86%). Residents showed significant improvement in segmental nodule localization with AI assistance, seniors did not. Software 2 slightly outperformed software 1 in increasing detection rates (67-77% vs. 80-86%), but neither significantly affected LungRADS classification. The study suggests that clinical experience mitigates the need for additional AI software, with the combination of CAD with residents being the most beneficial approach. Both software systems performed similarly, with software 2 showing a slightly higher but non-significant increase in detection rates.
本研究旨在评估人工智能辅助对放射科住院医师和资深放射科医生肺部结节检测率的影响,并评估两种不同商业化的人工智能软件系统在提高胸部 CT 检测率和 LungRADS 分类方面的有效性。研究队列包括 198 名参与者和 221 个肺结节。人工智能辅助使住院医师的平均检测率从 64%显著提高到 77%,而资深医生的检测率基本保持不变(85%对 86%)。人工智能辅助显著提高了住院医师对结节节段定位的准确性,而资深医生则没有。软件 2 在提高检测率方面略优于软件 1(67%-77%对 80%-86%),但均未显著影响 LungRADS 分类。本研究表明,临床经验减轻了对额外人工智能软件的需求,将 CAD 与住院医师结合使用是最有益的方法。两种软件系统的性能相似,软件 2 显示出略高但无统计学意义的检测率提高。