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International Journal of Colorectal Disease

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Open Access 01-12-2024 | Diverticulitis | RESEARCH

Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections

Authors: Florian Lippenberger, Sebastian Ziegelmayer, Maximilian Berlet, Hubertus Feussner, Marcus Makowski, Philipp-Alexander Neumann, Markus Graf, Georgios Kaissis, Dirk Wilhelm, Rickmer Braren, Stefan Reischl

Published in: International Journal of Colorectal Disease | Issue 1/2024

Abstract

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.

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Title: Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections
Authors: Florian Lippenberger
Sebastian Ziegelmayer
Maximilian Berlet
Hubertus Feussner
Marcus Makowski
Philipp-Alexander Neumann
Markus Graf
Georgios Kaissis
Dirk Wilhelm
Rickmer Braren
Stefan Reischl
Publication date: 01-12-2024
Publisher: Springer Berlin Heidelberg
Keyword: Diverticulitis
Published in: International Journal of Colorectal Disease / Issue 1/2024
Print ISSN: 0179-1958
Electronic ISSN: 1432-1262
DOI: https://doi.org/10.1007/s00384-024-04593-z

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections

Abstract

Purpose

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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