Automated Generation of Personalized Shock Wave Lithotripsy Protocols: Treatment Planning Using Deep Learning
文献类型：期刊论文
| 作者 | Chen, Zhipeng1; Zeng, Daniel D.2 ; Seltzer, Ryan G. N.3; Hamilton, Blake D.4
|
| 刊名 | JMIR MEDICAL INFORMATICS
 |
| 出版日期 | 2021-05-01 |
| 卷号 | 9期号:5页码:11 |
| 关键词 | nephrolithiasis extracorporeal shock wave therapy lithotripsy treatment planning deep learning artificial intelligence |
| DOI | 10.2196/24721 |
| 通讯作者 | Chen, Zhipeng(zhipengchen@saidi.org.cn) |
| 英文摘要 | 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. |
| WOS关键词 | STONE DISTANCE ; KIDNEY ; INJURY ; FRAGMENTATION ; ATTENUATION ; REDUCTION ; PREDICTOR ; ALGORITHM ; CALCULUS ; EFFICACY |
| WOS研究方向 | Medical Informatics |
| 语种 | 英语 |
| WOS记录号 | WOS:000656664300007 |
| 出版者 | JMIR PUBLICATIONS, INC |
| 源URL | [http://ir.ia.ac.cn/handle/173211/45301]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
| 通讯作者 | Chen, Zhipeng |
| 作者单位 | 1.Shenzhen Artificial Intelligence & Data Sci Inst, Shenzhen, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 3.Translat Analyt & Stat, Tucson, AZ USA 4.Univ Utah, Sch Med, Salt Lake City, UT USA |
| 推荐引用方式 GB/T 7714 | Chen, Zhipeng,Zeng, Daniel D.,Seltzer, Ryan G. N.,et al. Automated Generation of Personalized Shock Wave Lithotripsy Protocols: Treatment Planning Using Deep Learning[J]. JMIR MEDICAL INFORMATICS,2021,9(5):11. |
| APA | Chen, Zhipeng,Zeng, Daniel D.,Seltzer, Ryan G. N.,&Hamilton, Blake D..(2021).Automated Generation of Personalized Shock Wave Lithotripsy Protocols: Treatment Planning Using Deep Learning.JMIR MEDICAL INFORMATICS,9(5),11. |
| MLA | Chen, Zhipeng,et al."Automated Generation of Personalized Shock Wave Lithotripsy Protocols: Treatment Planning Using Deep Learning".JMIR MEDICAL INFORMATICS 9.5(2021):11. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。