TY - JOUR
T1 - Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections
AU - Lippenberger, Florian
AU - Ziegelmayer, Sebastian
AU - Berlet, Maximilian
AU - Feussner, Hubertus
AU - Makowski, Marcus
AU - Neumann, Philipp Alexander
AU - Graf, Markus
AU - Kaissis, Georgios
AU - Wilhelm, Dirk
AU - Braren, Rickmer
AU - Reischl, Stefan
N1 - Publisher Copyright:
© 2024, The Author(s).
PY - 2024/12
Y1 - 2024/12
N2 - Purpose: Sigmoid diverticulitis is a disease with a high socioeconomic burden, accounting for a high number of left-sided colonic resections worldwide. Modern surgical scheduling relies on accurate prediction of operation times to enhance patient care and optimize healthcare resources. This study aims to develop a predictive model for surgery duration in laparoscopic sigmoid resections, based on preoperative CT biometric and demographic patient data. Methods: This retrospective single-center cohort study included 85 patients who underwent laparoscopic sigmoid resection for diverticular disease. Potentially relevant procedure-specific anatomical parameters recommended by a surgical expert were measured in preoperative CT imaging. After random split into training and test set (75% / 25%) multiclass logistic regression was performed and a Random Forest classifier was trained on CT imaging parameters, patient age, and sex in the training cohort to predict categorized surgery duration. The models were evaluated in the test cohort using established performance metrics including receiver operating characteristics area under the curve (AUROC). Results: The Random Forest model achieved a good average AUROC of 0.78. It allowed a very good prediction of long (AUROC = 0.89; specificity 0.71; sensitivity 1.0) and short (AUROC = 0.81; specificity 0.77; sensitivity 0.56) procedures. It clearly outperformed the multiclass logistic regression model (AUROC: average = 0.33; short = 0.31; long = 0.22). Conclusion: A Random Forest classifier trained on demographic and CT imaging biometric patient data could predict procedure duration outliers of laparoscopic sigmoid resections. Pending validation in a multicenter study, this approach could potentially improve procedure scheduling in visceral surgery and be scaled to other procedures.
AB - Purpose: Sigmoid diverticulitis is a disease with a high socioeconomic burden, accounting for a high number of left-sided colonic resections worldwide. Modern surgical scheduling relies on accurate prediction of operation times to enhance patient care and optimize healthcare resources. This study aims to develop a predictive model for surgery duration in laparoscopic sigmoid resections, based on preoperative CT biometric and demographic patient data. Methods: This retrospective single-center cohort study included 85 patients who underwent laparoscopic sigmoid resection for diverticular disease. Potentially relevant procedure-specific anatomical parameters recommended by a surgical expert were measured in preoperative CT imaging. After random split into training and test set (75% / 25%) multiclass logistic regression was performed and a Random Forest classifier was trained on CT imaging parameters, patient age, and sex in the training cohort to predict categorized surgery duration. The models were evaluated in the test cohort using established performance metrics including receiver operating characteristics area under the curve (AUROC). Results: The Random Forest model achieved a good average AUROC of 0.78. It allowed a very good prediction of long (AUROC = 0.89; specificity 0.71; sensitivity 1.0) and short (AUROC = 0.81; specificity 0.77; sensitivity 0.56) procedures. It clearly outperformed the multiclass logistic regression model (AUROC: average = 0.33; short = 0.31; long = 0.22). Conclusion: A Random Forest classifier trained on demographic and CT imaging biometric patient data could predict procedure duration outliers of laparoscopic sigmoid resections. Pending validation in a multicenter study, this approach could potentially improve procedure scheduling in visceral surgery and be scaled to other procedures.
KW - Computed tomography
KW - Diverticulitis
KW - Laparoscopic surgery
KW - Machine learning
KW - Random Forest
KW - Surgery scheduling
UR - http://www.scopus.com/inward/record.url?scp=85183046025&partnerID=8YFLogxK
U2 - 10.1007/s00384-024-04593-z
DO - 10.1007/s00384-024-04593-z
M3 - Article
AN - SCOPUS:85183046025
SN - 0179-1958
VL - 39
JO - International Journal of Colorectal Disease
JF - International Journal of Colorectal Disease
IS - 1
M1 - 21
ER -