使用机器学习量化膀外逆流
Saidul Kabir1, J L Pippi Salle2, Muhammad E H Chowdhury3
1Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000, Bangladesh.
Journal of pediatric urology
|November 18, 2023
概括
一个机器学习模型准确地评分了尿管回流 (VCUG) 图像的尿管回流 (VUR). 这种客观的方法有助于确定VUR患者的临床过程和治疗.
科学领域:
- 放射学 放射学是指放射学
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 排空囊回形图 (VCUG) 的分级对于管理囊回流 (VUR) 至关重要.
- 目前的VCUG图像评估是主观的,导致VUR评估中的潜在不一致性.
- 需要对VUR进行客观分级,以规范患者护理.
研究的目的:
- 开发和验证用于客观VCUG图像分级的监督机器学习模型.
- 通过放射图像自动评估VUR严重程度.
- 为了提高VUR诊断的准确性和一致性.
主要方法:
- 一组113张VCUG图像的数据集进行了策划.
- 专家儿科放射科医生和泌尿科医生对VUR的严重程度进行了分级,以确定基本事实.
- 提取了九个图像特征,并用于训练六个具有交叉验证的机器学习模型.
主要成果:
- 机器学习模型,特别是支持矢量机 (SVM) 和多层感知器 (MLP),实现了高精度.
- 使用排名最高的功能,MLP的F1分数达到91.14%,SVM的90.27%.
- 一个扭曲的囊模式被确定为高度VUR的关键预测因素.
结论:
- 机器学习为从VCUG图像中客观VUR分级提供了一个有希望的途径.
- 开发的模型在评估VUR严重程度方面表现出很高的准确性.
- 未来对机器学习协议的改进可以进一步完善客观VUR分级.
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