深度学习预测了英国生物银行基底成像的流行和发病帕金森病
Charlie Tran1, Kai Shen1, Kang Liu2
1Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA.
Scientific reports
|February 13, 2024
概括
视网膜底部成像显示了早期帕金森病 (PD) 检测的前景. 机器学习模型在从眼部扫描中识别PD时达到68%的准确性,有助于早期诊断和干预.
科学领域:
- 神经学 神经学
- 眼科医生 眼科 眼科
- 人工智能的人工智能
背景情况:
- 帕金森病 (PD) 是一种快速发展的神经系统疾病,具有诊断挑战.
- 目前对PD的诊断方法昂贵,而且无法广泛使用.
- 早期发现PD对于及时干预至关重要,特别是考虑到其潜伏性发作.
研究的目的:
- 评估机器学习 (ML) 和深度学习 (DL) 技术,使用视网膜底图像对帕金森病进行分类.
- 探索视网膜底部成像作为帕金森病潜在的非侵入性查工具.
- 评估ML/DL模型在区分PD患者与健康对照中的准确性.
主要方法:
- 系统评估传统的ML和DL算法.
- 使用英国生物银行视网膜底部成像数据对帕金森病的分类.
- 帕金森病患者的年龄和性别与健康受试者的匹配.
主要成果:
- 帕金森病患者与健康人群的区别是68%的准确度.
- 这种诊断准确性在流行和发病的帕金森病中是一致的.
- 使用视觉归属图和模型稳定性指标,增强了可解释性.
结论:
- 视网膜底部成像,一个"大脑的窗口",是帕金森病的可行的查方式.
- 在 fundus 图像上使用 ML/DL 的自动诊断可以改善帕金森病的检测.
- 通过视网膜成像进行早期和准确的查可以为帕金森病患者提供及时的医疗干预.
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