Predictive Capacities of a Machine Learning Decision Tree Model Created to Analyse Feasibility of an Open or Robotic Kidney Transplant

IF 2.3 3区医学 Q2 SURGERY International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2024-12-23 DOI:10.1002/rcs.70035

Alessandro Martinino, Ojus Khanolkar, Erdem Koyuncu, Egor Petrochenkov, Giulia Bencini, Joanna Olazar, Pierpaolo Di Cocco, Jorge Almario-Alvarez, Mario Spaggiari, Enrico Benedetti, Ivo Tzvetanov

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Abstract

Background

Machine learning has emerged as a potent tool in healthcare. A decision tree model was built to improve the decision-making process when determining the optimal choice between an open or robotic surgical approach for kidney transplant.

Methods

822 patients (OKT) and 169 (RKT) underwent kidney transplantation at our centre during the study period. A decision tree model was built in a two-step process consisting of: (1) Creating the model on the training data and (2) testing the predictive capabilities of the model using the test data.

Results

Our model correctly predicted an OKT in 148 patients out of 161 test cases who received an OKT (accuracy 91%) and predicted an RKT in 19 out of 25 test cases of patients receiving an RKT (accuracy 76%).

Conclusion

Our model represents the inaugural data-driven model that furnishes concrete insights for the discernment between employing robotic and open surgery techniques.

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用于分析开放或机器人肾移植可行性的机器学习决策树模型的预测能力。

背景：机器学习已经成为医疗保健领域的有力工具。建立了一种决策树模型，以改善在开放式或机器人手术方式之间做出最佳选择的决策过程。方法：822例（OKT）和169例（RKT）患者在研究期间在我中心接受了肾移植。建立决策树模型分为两个步骤：(1)在训练数据上创建模型；(2)使用测试数据测试模型的预测能力。结果：我们的模型在161例接受OKT的患者中正确预测了148例患者的OKT（准确率91%），并在25例接受RKT的患者中预测了19例患者的RKT（准确率76%）。结论：我们的模型代表了首个数据驱动模型，为使用机器人和开放手术技术之间的区别提供了具体的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Medical Robotics and Computer Assisted Surgery 医学-外科

CiteScore

4.50

自引率

12.00%

发文量

131

审稿时长

6-12 weeks

期刊介绍： The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.