TY - JOUR
T1 - Clinical Validation of Computer-Aided Diagnosis Software for Preventing Retained Surgical Sponges
AU - Kurisaki, Ken
AU - Soyama, Akihiko
AU - Hamauzu, Shin
AU - Yamada, Masahiko
AU - Yamaguchi, Shun
AU - Matsuguma, Kunihito
AU - Kerkhof, Enzo
AU - Fukuda, Toru
AU - Toya, Ryo
AU - Eguchi, Susumu
N1 - Publisher Copyright:
© 2024 by the American College of Surgeons. Published by Wolters Kluwer Health, Inc. All rights reserved.
PY - 2024/5/1
Y1 - 2024/5/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85190903223&partnerID=8YFLogxK
U2 - 10.1097/XCS.0000000000001012
DO - 10.1097/XCS.0000000000001012
M3 - Article
C2 - 38258847
AN - SCOPUS:85190903223
SN - 1072-7515
VL - 238
SP - 856
EP - 860
JO - Journal of the American College of Surgeons
JF - Journal of the American College of Surgeons
IS - 5
ER -