Carlotta Nedbal, Sairam Adithya, Shilpa Gite, Nithesh Naik, Stephen Griffin, Bhaskar K Somani
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Fifteen ML classification algorithms were used to investigate correlations between preoperative characteristics and postoperative outcomes: primary stone-free status (SFS, defined as stone fragments <2 mm at the end of the procedure confirmed endoscopically and no evidence of stone fragments >2 mm at Xray kidney-ureters-bladder (XR KUB) or ultrasound kidney-ureters-bladder (US KUB) at 3 months follow-up) and complications. For the task of complication and stone status, an ensemble model was made out of Bagging classifier, Extra Trees classifier, and linear discriminant analysis. Also, a multitask neural network was constructed for the simultaneous prediction of all postoperative characteristics. Finally, explainable artificial intelligence techniques were used to explain the prediction made by the best models. <b><i>Results:</i></b> The ensemble model produced the highest accuracy (90%) in predicting SFS, finding correlation with overall stone size (-0.205), presence of multiple stones (-0.127), and preoperative stenting (-0.102). Complications were predicted by Synthetic Minority Oversampling Technique (SMOTE) oversampled dataset (93.3% accuracy) with relation to preoperative positive urine culture (-0.060) and SFS (0.003). Training ML for the multitask model, accuracies of 83.3% and 80% were respectively reached. <b><i>Conclusion:</i></b> ML has a great potential of assisting health care research, with possibilities to investigate dataset at a higher level. With the aid of this intelligent tool, urologists can implement their practice and develop new strategies for outcome prediction and patient counseling and informed shared decision-making. Our model reached an excellent accuracy in predicting SFS and complications in the pediatric population, leading the way to the validation of patient-specific predictive tools.</p>","PeriodicalId":15723,"journal":{"name":"Journal of endourology","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Machine Learning Predictive Model for Ureteroscopy Lasertripsy Outcomes in a Pediatric Population-Results from a Large Endourology Tertiary Center.\",\"authors\":\"Carlotta Nedbal, Sairam Adithya, Shilpa Gite, Nithesh Naik, Stephen Griffin, Bhaskar K Somani\",\"doi\":\"10.1089/end.2024.0120\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b><i>Introduction:</i></b> We aimed to develop machine learning (ML) algorithms for the automated prediction of postoperative ureteroscopy outcomes for pediatric kidney stones based on preoperative characteristics. <b><i>Materials and Methods:</i></b> Data from pediatric patients who underwent ureteroscopy for stone treatment by a single experienced surgeon, between 2010 and 2023 in Southampton General Hospital, were retrospectively collected. Fifteen ML classification algorithms were used to investigate correlations between preoperative characteristics and postoperative outcomes: primary stone-free status (SFS, defined as stone fragments <2 mm at the end of the procedure confirmed endoscopically and no evidence of stone fragments >2 mm at Xray kidney-ureters-bladder (XR KUB) or ultrasound kidney-ureters-bladder (US KUB) at 3 months follow-up) and complications. For the task of complication and stone status, an ensemble model was made out of Bagging classifier, Extra Trees classifier, and linear discriminant analysis. Also, a multitask neural network was constructed for the simultaneous prediction of all postoperative characteristics. Finally, explainable artificial intelligence techniques were used to explain the prediction made by the best models. <b><i>Results:</i></b> The ensemble model produced the highest accuracy (90%) in predicting SFS, finding correlation with overall stone size (-0.205), presence of multiple stones (-0.127), and preoperative stenting (-0.102). Complications were predicted by Synthetic Minority Oversampling Technique (SMOTE) oversampled dataset (93.3% accuracy) with relation to preoperative positive urine culture (-0.060) and SFS (0.003). Training ML for the multitask model, accuracies of 83.3% and 80% were respectively reached. <b><i>Conclusion:</i></b> ML has a great potential of assisting health care research, with possibilities to investigate dataset at a higher level. With the aid of this intelligent tool, urologists can implement their practice and develop new strategies for outcome prediction and patient counseling and informed shared decision-making. 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引用次数: 0
摘要
简介:我们旨在开发机器学习(ML)算法,根据术前特征自动预测小儿肾结石输尿管镜检查术后结果:我们的目标是开发机器学习(ML)算法,根据术前特征自动预测小儿肾结石输尿管镜术后结果:回顾性收集了2010年至2023年间在南安普顿综合医院由一名经验丰富的外科医生进行输尿管镜检查治疗结石的儿科患者的数据。15 种 ML 分类算法用于研究术前特征与术后结果之间的相关性:原发性无结石状态(SFS,定义为 XR KUB 或 US KUB 随访 3 个月时结石碎片为 2 毫米)和并发症。针对并发症和结石状态的任务,采用了袋式分类器、额外树分类器和 LDA 组成的集合模型。此外,还构建了一个多任务神经网络,用于同时预测所有术后特征。最后,使用可解释人工智能技术来解释最佳模型的预测结果:集合模型预测 SFS 的准确率最高(90%),发现与结石总大小(-0.205)、是否存在多发性结石(-0.127)和术前支架植入(-0.102)相关。SMOTE超采样数据集预测并发症的准确率为93.3%,与术前尿培养阳性(-0.060)和SFS(0.003)相关。多任务模型的 ML 训练准确率分别达到 83.3% 和 80%:结论:ML 在协助医疗保健研究方面潜力巨大,可以在更高层次上对数据集进行研究。借助这一智能工具,泌尿科医生可以实施他们的实践,并为结果预测、患者咨询和知情共同决策制定新策略。我们的模型在预测儿科人群的 SFS 和并发症方面达到了极高的准确度,为验证针对特定患者的预测工具开辟了道路。
A Machine Learning Predictive Model for Ureteroscopy Lasertripsy Outcomes in a Pediatric Population-Results from a Large Endourology Tertiary Center.
Introduction: We aimed to develop machine learning (ML) algorithms for the automated prediction of postoperative ureteroscopy outcomes for pediatric kidney stones based on preoperative characteristics. Materials and Methods: Data from pediatric patients who underwent ureteroscopy for stone treatment by a single experienced surgeon, between 2010 and 2023 in Southampton General Hospital, were retrospectively collected. Fifteen ML classification algorithms were used to investigate correlations between preoperative characteristics and postoperative outcomes: primary stone-free status (SFS, defined as stone fragments <2 mm at the end of the procedure confirmed endoscopically and no evidence of stone fragments >2 mm at Xray kidney-ureters-bladder (XR KUB) or ultrasound kidney-ureters-bladder (US KUB) at 3 months follow-up) and complications. For the task of complication and stone status, an ensemble model was made out of Bagging classifier, Extra Trees classifier, and linear discriminant analysis. Also, a multitask neural network was constructed for the simultaneous prediction of all postoperative characteristics. Finally, explainable artificial intelligence techniques were used to explain the prediction made by the best models. Results: The ensemble model produced the highest accuracy (90%) in predicting SFS, finding correlation with overall stone size (-0.205), presence of multiple stones (-0.127), and preoperative stenting (-0.102). Complications were predicted by Synthetic Minority Oversampling Technique (SMOTE) oversampled dataset (93.3% accuracy) with relation to preoperative positive urine culture (-0.060) and SFS (0.003). Training ML for the multitask model, accuracies of 83.3% and 80% were respectively reached. Conclusion: ML has a great potential of assisting health care research, with possibilities to investigate dataset at a higher level. With the aid of this intelligent tool, urologists can implement their practice and develop new strategies for outcome prediction and patient counseling and informed shared decision-making. Our model reached an excellent accuracy in predicting SFS and complications in the pediatric population, leading the way to the validation of patient-specific predictive tools.
期刊介绍:
Journal of Endourology, JE Case Reports, and Videourology are the leading peer-reviewed journal, case reports publication, and innovative videojournal companion covering all aspects of minimally invasive urology research, applications, and clinical outcomes.
The leading journal of minimally invasive urology for over 30 years, Journal of Endourology is the essential publication for practicing surgeons who want to keep up with the latest surgical technologies in endoscopic, laparoscopic, robotic, and image-guided procedures as they apply to benign and malignant diseases of the genitourinary tract. This flagship journal includes the companion videojournal Videourology™ with every subscription. While Journal of Endourology remains focused on publishing rigorously peer reviewed articles, Videourology accepts original videos containing material that has not been reported elsewhere, except in the form of an abstract or a conference presentation.
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