ArticleWideochirurgia i inne techniki maloinwazyjne = Videosurgery and other miniinvasive techniques2021
Prediction model of laparoendoscopic single-site surgery in gynecology using machine learning algorithm.
Article in Wideochirurgia i inne techniki maloinwazyjne = Videosurgery and other miniinvasive techniques, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Article
- Short- and long-term outcomes as well as anal function of transanal natural orifice specimen extraction surgery versus conventional laparoscopic surgery for sigmoid colon or rectal cancer resection: a retrospective study with over 5-year follow-up.Wideochirurgia i inne techniki maloinwazyjne = Videosurgery and other miniinvasive techniques · 2022Article
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Authors and funding
7 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
introductionMinimally 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.
aimTo 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
methodsA 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.
resultsThe 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.
conclusionsLESS 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.
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