Journal of the American College of Surgeons · 2024
Clinical Validation of Computer-Aided Diagnosis Software for Preventing Retained Surgical Sponges.
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Kurisaki Ken, Soyama Akihiko, Hamauzu Shin, Yamada Masahiko, Yamaguchi Shun, Matsuguma Kunihito, Kerkhof Enzo, Fukuda Toru, Toya Ryo, Eguchi Susumu
Аннотация
BACKGROUND: We 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 DESIGN: A 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.
RESULTS: Among 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.
CONCLUSIONS: The 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.
STUDY DESIGN: A 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.
RESULTS: Among 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.
CONCLUSIONS: The 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.