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机器学习算法用于识别与抗发作药物vigabatrin相关的视野损失 - 一个概念证明

John M Wild¹, Philip E M Smith², Carlo Knupp³

¹College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK wildjm@cardiff.ac.uk.

The British journal of ophthalmology

|March 18, 2025

概括

机器学习算法准确地检测到维加巴素诱导的视野损失 (VAVFL),有助于临床解释. 这些工具改善了视野缺陷的患者的患者管理.

科学领域:

眼科医生眼科眼科
人工智能的人工智能
药物监督药物监督药物监督

背景情况:

维加巴特林是一种抗发作药物,可能导致视野丧失 (VAVFL).
解释视觉场可能具有挑战性,特别是在认知障碍患者中.
为了有效的患者管理,需要对VAVFL进行客观的鉴定.

研究的目的:

开发和评估用于客观检测VAVFL的机器学习算法.
评估这些算法的在临床环境中的实用性.

主要方法:

基于欧洲药物管理局批准的VAVFL检测协议,开发了两个机器学习算法.
算法将患者的视野与VAVFL的参考模式进行了比较.
使用对称化技术来提高信号与噪声的比率并提高准确性.

主要成果:

算法与黄金标准的临床解释有很好的一致性.
在FF135和C30-2T测试中都实现了高灵敏度和特异性.
在周边学习,疲劳或同时出现同名损失的情况下,对称证明是有益的.

结论:

机器学习算法为VAVFL检测提供了直接可解释的结果.
这些算法可以帮助在社区 (神经) 眼科设置中的患者管理.

关键词:

视野视野视野视野视野

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