• Ken Kurisaki, Akihiko Soyama, Shin Hamauzu, Masahiko Yamada, Shun Yamaguchi, Kunihito Matsuguma, Enzo Kerkhof, Toru Fukuda, Ryo Toya, and Susumu Eguchi.
• From the Department of Surgery, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki City, Japan (Kurisaki, Soyama, Yamaguchi, Matsuguma, Eguchi).
• J. Am. Coll. Surg. 2024 May 1; 238 (5): 856860856-860.

BackgroundWe previously reported the successful development of a computer-aided diagnosis (CAD) system for preventing retained surgical sponges with deep learning using training data, including composite and simulated radiographs. In this study, we evaluated the efficacy of the CAD system in a clinical setting.Study DesignA total of 1,053 postoperative radiographs obtained from patients 20 years of age or older who underwent surgery were evaluated. We implemented a foreign object detection application software on the portable radiographic device used in the operating room to detect retained surgical sponges. The results of the CAD system diagnosis were prospectively collected.ResultsAmong the 1,053 images, the CAD system detected possible retained surgical items in 150 images. Specificity was 85.8%, which is similar to the data obtained during the development of the software.ConclusionsThe validation of a CAD system using deep learning in a clinical setting showed similar efficacy as during the development of the system. These results suggest that the CAD system can contribute to the establishment of a more effective protocol than the current standard practice for preventing the retention of surgical items.Copyright © 2024 by the American College of Surgeons. Published by Wolters Kluwer Health, Inc. All rights reserved.

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