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首页> 外文期刊>JMIR Medical Informatics >Automated Generation of Personalized Shock Wave Lithotripsy Protocols: Treatment Planning Using Deep Learning
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Automated Generation of Personalized Shock Wave Lithotripsy Protocols: Treatment Planning Using Deep Learning

机译：自动化的个性化冲击波Lithotripsy协议：使用深度学习的治疗计划

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Background Though shock wave lithotripsy (SWL) has developed to be one of the most common treatment approaches for nephrolithiasis in recent decades, its treatment planning is often a trial-and-error process based on physicians’ subjective judgement. Physicians’ inexperience with this modality can lead to low-quality treatment and unnecessary risks to patients. Objective To improve the quality and consistency of shock wave lithotripsy treatment, we aimed to develop a deep learning model for generating the next treatment step by previous steps and preoperative patient characteristics and to produce personalized SWL treatment plans in a step-by-step protocol based on the deep learning model. Methods We developed a deep learning model to generate the optimal power level, shock rate, and number of shocks in the next step, given previous treatment steps encoded by long short-term memory neural networks and preoperative patient characteristics. We constructed a next-step data set (N=8583) from top practices of renal SWL treatments recorded in the International Stone Registry. Then, we trained the deep learning model and baseline models (linear regression, logistic regression, random forest, and support vector machine) with 90% of the samples and validated them with the remaining samples. Results The deep learning models for generating the next treatment steps outperformed the baseline models (accuracy = 98.8%, F1 = 98.0% for power levels; accuracy = 98.1%, F1 = 96.0% for shock rates; root mean squared error = 207, mean absolute error = 121 for numbers of shocks). The hypothesis testing showed no significant difference between steps generated by our model and the top practices ( P =.480 for power levels; P =.782 for shock rates; P =.727 for numbers of shocks). Conclusions The high performance of our deep learning approach shows its treatment planning capability on par with top physicians. To the best of our knowledge, our framework is the first effort to implement automated planning of SWL treatment via deep learning. It is a promising technique in assisting treatment planning and physician training at low cost.
机译：背景技术虽然冲击波Lithotripsy（SWL）已被发展成为近几十年来成为肾血清腺最常见的治疗方法之一，但其治疗规划通常是基于医生主观判断的试用和错误过程。这种模态的医生缺乏经验可能导致低质量的治疗和对患者的不必要的风险。目的提高冲击波碎石型治疗的质量和一致性，我们旨在开发一种深入学习模型，用于通过先前的步骤和术前患者特征产生下一个治疗方法，并在基于逐步的协议中产生个性化SWL治疗计划论深层学习模式。方法我们开发了一个深入学习模型，以产生下一步中的最佳功率水平，冲击率和冲击次数，给定由长短期内存神经网络和术前患者特征编码的先前处理步骤。我们从国际石册中记录的肾版SWL处理的顶级实践构建了一个下一步数据集（n = 8583）。然后，我们培训了具有90％的样本的深度学习模型和基线模型（线性回归，逻辑回归，随机森林，支持向量机），并用其余样本验证它们。结果生成下一个处理步骤的深度学习模型优于基线模型（精度= 98.8％，功率水平的98.0％;精度= 98.1％，休克速率为96.0％;根均匀误差= 207，意味着绝对误差= 121用于冲击的数量）。假设检测显示我们的模型和顶部实践生成的步骤之间没有显着差异（用于功率水平的P = .480; P = .782用于冲击率; P = .727用于震动的数量）。结论我们的深度学习方法的高性能表明其与顶部医生的策略策划能力。据我们所知，我们的框架是通过深入学习实施SWL治疗自动化规划的首要努力。这是一项有希望的技术，可在低成本辅助治疗规划和医生培训。

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