开发可解释的AI工具，辅助医生诊断新生血管性黄斑变性。
OCTA-Based Biomarker Characterization in nAMD
- 基于OCTA图像提取病变区域和血管密度等生物标志物
- 构建3D可视化模型，直观展示异常血管分布
- 采用可解释的机器学习模型，诊断准确率达68%
本研究旨在提升眼科医生对新生血管性年龄相关性黄斑变性（nAMD）的诊断决策能力。我们开发了三项工具：(1) 利用图像处理技术提取mCNV面积、血管密度等生物标志物；(2) 生成neovascularization的3D可视化图像，以更清晰呈现病变区域；(3) 应用三种白盒机器学习算法（决策树、支持向量机、DL-Learner）构成集成模型进行nAMD诊断。模型在训练数据上达到100%准确率，在测试集上为68%。主要优势在于所有模型均为白盒，确保可解释性和透明性，使临床医生能更好理解诊断逻辑。
原文摘要 · Abstract (English)
We aim to enhance ophthalmologists' decision-making when diagnosing the Neovascular Age-Related Macular Degeneration (nAMD). We developed three tools to analyze Optical Coherence Tomography Angiography images: (1) extracting biomarkers such as mCNV area and vessel density using image processing; (2) generating a 3D visualization of the neovascularization for a better view of the affected regions; and (3) applying an ensemble of three white box machine learning algorithms (decision tree, support vector machines and DL-Learner) for nAMD diagnosis. The learned expressions reached 100% accuracy for the training data and 68% accuracy in testing. The main advantage is that all the learned models white-box, which ensures explainability and transparency, allowing clinicians to better understand the decision-making process.
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