Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD
Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD
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DOI：
10.1167/tvst.9.2.25
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发表时间：
2020-01-01
影响因子：
3
通讯作者：
Smith, R. Theodore
中科院分区：
文献类型：
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作者：
Bhuiyan, Alauddin;Wong, Tien Yin;Smith, R. Theodore
目的：建立和验证基于人工智能（AI）的AMD筛查模型，并预测1 - 2年内晚期干性和湿性AMD的进展。方法：使用年龄相关性眼病研究（AREDS）数据集对我们的预测模型进行训练和验证。对营养性AMD治疗-2 （NAT-2）研究进行外部验证。第一步：对来自4139名AREDS研究参与者的116,875张彩色眼底照片进行了深度学习筛选，并对其进行了训练和验证，将其分为无AMD、早期AMD、中级AMD和晚期AMD，并进一步按照AREDS 12级严重程度量表对其进行分层。第二步：通过逻辑模型树机器学习技术将所得的AMD评分与社会人口学临床数据和其他自动提取的影像学数据相结合，预测1或2年内进展为晚期AMD的风险，并对923名2年内进展的AREDS参与者、901名1年内进展的参与者和2840名2年内未进展的参与者进行培训和验证。对于那些发现有进展到晚期AMD风险的人，我们进一步预测了晚期AMD进展的类型（干性或湿性）。结果：对于早期/无AMD与中期/晚期（即转诊级别）AMD的识别，我们达到了99.2%的准确率。2年晚期AMD（任何）的预测模型准确率为86.36%，晚期干性AMD为66.88%，晚期湿性AMD为67.15%。对于NAT-2数据集，2年后AMD预测准确率为84%。结论：经过验证的基于彩色眼底照片的AMD筛查和晚期AMD风险预测模型现已准备好进行临床试验和潜在的远程医疗部署。翻译相关性：无创、高度准确、快速的人工智能方法筛查转诊级别AMD并预测AMD晚期进展，为我们对这种普遍致盲疾病的护理提供了重大的潜在改进。
Purpose: To build and validate artificial intelligence (AI)-based models for AMD screening and for predicting late dry and wet AMD progression within 1 and 2 years.Methods: The dataset of the Age-related Eye Disease Study (AREDS) was used to train and validate our prediction model. External validation was performed on the Nutritional AMD Treatment-2 (NAT-2) study.First Step: An ensemble of deep learning screening methods was trained and validated on 116,875 color fundus photos from 4139 participants in the AREDS study to classify them as no, early, intermediate, or advanced AMD and further stratified them along the AREDS 12 level severity scale. Second step: the resulting AMD scores were combined with sociodemographic clinical data and other automatically extracted imaging data by a logistic model tree machine learning technique to predict risk for progression to late AMD within 1 or 2 years, with training and validation performed on 923 AREDS participants who progressed within 2 years, 901 who progressed within 1 year, and 2840 who did not progress within 2 years. For those found at risk of progression to late AMD, we further predicted the type (dry or wet) of the progression of late AMD.Results: For identification of early/none vs. intermediate/late (i.e., referral level) AMD, we achieved 99.2% accuracy. The prediction model for a 2-year incident late AMD (any) achieved 86.36% accuracy, with 66.88% for late dry and 67.15% for late wet AMD. For the NAT-2 dataset, the 2-year late AMD prediction accuracy was 84%.Conclusions: Validated color fundus photo-based models for AMD screening and risk prediction for late AMD are now ready for clinical testing and potential telemedical deployment.Translational Relevance: Noninvasive, highly accurate, and fast AI methods to screen for referral level AMD and to predict late AMD progression offer significant potential improvements in our care of this prevalent blinding disease.