对机器学习技术进行了广泛的分析,以识别青光眼
R Vinod Kumar1, N Sharmila Banu2
1Research Scholar, Department of Computer Science and Engineering, School of Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, 506371, India. vinodasdhoni@gmail.com.
International ophthalmology
|November 7, 2025
概括
机器学习 (ML) 显示出对诊断青光眼的前景,这是导致失明的主要原因. 通过多模式数据和可解释性来增强ML模型,这是对临床使用在青光眼检测中的关键.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 玻璃眼是全球不可逆转的失明的主要原因.
- 早期发现青光眼对于预防视力丧失至关重要.
- 机器学习 (ML) 算法越来越多地用于青光眼的诊断.
研究的目的:
- 审查最近的ML算法对青光眼诊断的进展.
- 分析这些ML模型的性能,局限性和临床应用.
- 确定未来的研究方向,以改善绿眼的检测中的ML.
主要方法:
- 2019年至2024年间发表的30篇论文的系统审查.
- 分析ML算法,成像方式 (基金图像,OCT) 和数据整合策略.
- 评估模型性能,概括性和可解释性.
主要成果:
- 将成像数据 (基金,OCT) 与ML模型相结合,显示出有希望的诊断结果.
- 持续存在的挑战包括不良的多模式数据集成,有限的概括性和缺乏可解释性.
- 从2019年到2023年,对绿眼病诊断的ML的研究活动显著增加.
结论:
- 多模式技术,可解释的模型和强大的数据集对于准确的青光眼诊断至关重要.
- 标准化框架和多样化的数据集被推用于临床接受ML工具.
- 目前和未来,ML对青光眼的检测和管理具有显著的潜力.
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