Prediction model of laparoendoscopic single-site surgery in gynecology using machine learning algorithm

Jun Ma; Jiani Yang; Shanshan Cheng; Yue Jin; Nan Zhang; Chao Wang; Yu Wang

doi:10.5114/wiitm.2021.106081

eISSN: 2299-0054
ISSN: 1895-4588

Videosurgery and Other Miniinvasive Techniques

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Gynaecology

abstract:

Original paper

Prediction model of laparoendoscopic single-site surgery in gynecology using machine learning algorithm

Jun Ma

¹

,

Jiani Yang

¹

,

Shanshan Cheng

¹

,

Yue Jin

¹

,

Nan Zhang

¹

,

Chao Wang

¹

,

Yu Wang

¹

Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China

Videosurgery Miniinv 2021; 16 (3): 587–596

DOI: https://doi.org/10.5114/wiitm.2021.106081

Online publish date: 2021/05/14

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Introduction
Minimally invasive surgery has been widely used in gynecology. The laparoendoscopic single-site surgery (LESS) risk prediction model can provide evidence-based references for preoperative surgical procedure selection. To determine whether the patients are suitable for LESS and to provide guidance for the clinical operation plan, we aimed to compare the clinical outcomes of LESS and conventional laparoscopic surgery (CLS) in gynecology. We constructed a LESS risk prediction model and predicted surgical conditions for the preoperative evaluation system.

Material and methods
A retrospective analysis was carried out among patients undergoing LESS (n = 1019) and CLS (n = 1055). Various clinical indicators were compared. Multiple machine model algorithms were evaluated. The optimal results were chosen as the model to form the risk prediction model.

Results
The LESS group showed advantages in the postoperative 12/24 h visual analog scale and Vancouver scar score compared with the CLS group (p < 0.05). The comparisons in other clinical indicators between the two groups showed that each group had advantages and the difference was statistically significant (p < 0.05), including operative time, estimated blood loss, and hospital stay. We evaluated the predictive value for various models using AUC values of 0.77, 0.77, 0.76, and 0.67 for XGBoost, random forest, GBDT, and logistic regression, respectively. The decision tree model was shown to be the optimal model.

Conclusions
LESS can reduce postoperative pain, shorten hospital stay and make scars acceptable. The risk prediction model based on a machine learning algorithm has manifested a high degree of accuracy and can satisfy the doctors’ demand for individualized preoperative evaluation and surgical safety in LESS.

keywords:

Prediction model of laparoendoscopic single-site surgery in gynecology using machine learning algorithm

Jun Ma 1 , Jiani Yang 1 , Shanshan Cheng 1 , Yue Jin 1 , Nan Zhang 1 , Chao Wang 1 , Yu Wang 1

laparoendoscopic single-site surgery, prediction model, conventional laparoscopic surgery, machine learning, adverse outcomes

Jun Ma

¹

,

Jiani Yang

¹

,

Shanshan Cheng

¹

,

Yue Jin

¹

,

Nan Zhang

¹

,

Chao Wang

¹

,

Yu Wang

¹