Preoperative Prediction of Prolonged Operative Time in Laparoscopic Ovarian Cystectomy Using Convolutional Neural Network-Extracted Ultrasound Image Features.
Authors
Affiliations (4)
Affiliations (4)
- Department of Artificial Intelligence, Jeju National University, 102 Jejudaehak-ro, Jeju-si, Jeju Special Self-Governing Province, 63243, Republic of Korea.
- Department of Obstetrics and Gynecology, Seoul National University Hospital, 101 Daehak-ro Jongno-gu, Seoul, Republic of Korea.
- Department of Obstetrics and Gynecology, Jeju National University Hospital Jeju National University College of Medicine Aran 13gil 15 (Ara-1Dong), Jeju City, 63241, Jeju Self-Governing Province, Republic of Korea.
- Department of Obstetrics and Gynecology, Jeju National University Hospital Jeju National University College of Medicine Aran 13gil 15 (Ara-1Dong), Jeju City, 63241, Jeju Self-Governing Province, Republic of Korea. Electronic address: [email protected].
Abstract
The aim of this study was to identify clinical and imaging factors associated with prolonged operative time and to explore whether CNN-derived ultrasound features provide incremental predictive value beyond conventional clinical variables. Retrospective cohort study SETTING: Single academic medical center PARTICIPANTS: A total of 247 patients who underwent laparoscopic ovarian cystectomy for presumed benign ovarian tumors were included in the study. Eligible participants were 18 years or older, had preoperative ultrasound images and complete operative records, and were initially planned for ovarian cystectomy. Patients were excluded if they were scheduled for oophorectomy, or if other major procedures such as myomectomy or hysterectomy were planned concurrently. We used two predictive models: a logistic regression model based on preoperative clinical variables (e.g., CA-125 levels, bilaterality of ovarian cysts, and robotic surgery) to predict prolonged operative time, defined as the upper quartile (25%) of operative duration in our cohort, and a second logistic regression model combining these variables with CNN-derived features from preoperative ultrasound images to enhance predictive accuracy. Multivariable analysis revealed that robotic surgery, bilaterality of ovarian cysts, CA-125 levels, and two CNN-derived imaging features were independently associated with prolonged operative time. Incorporation of CNN-derived ultrasound features increased the AUC from 0.889 to 0.920, although the difference did not reach statistical significance. The combined model, incorporating clinical and CNN-extracted imaging features, demonstrates the feasibility of predicting prolonged operative time in laparoscopic ovarian cystectomy. This approach may support preoperative risk stratification and serve as a foundation for future studies exploring its role in surgical scheduling and resource planning, pending external validation. CNN (Convolutional Neural Network): A type of deep learning model that processes image data by extracting hierarchical spatial features.