Bhuiyan Alauddin, Wong Tien Yin, Ting Daniel Shu Wei, Govindaiah Arun, Souied Eric H, Smith R Theodore
iHealthScreen Inc., New York, NY, USA.
New York University, New York, NY, USA.
Transl Vis Sci Technol. 2020 Apr 24;9(2):25. doi: 10.1167/tvst.9.2.25. eCollection 2020 Apr.
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.
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.
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.
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%.
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.
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.
构建并验证基于人工智能（AI）的模型，用于年龄相关性黄斑变性（AMD）筛查以及预测1年和2年内晚期干性和湿性AMD的进展情况。
使用年龄相关性眼病研究（AREDS）数据集来训练和验证我们的预测模型。在营养性AMD治疗-2（NAT-2）研究中进行外部验证。
在来自AREDS研究的4139名参与者的116,875张彩色眼底照片上训练并验证一组深度学习筛查方法，将其分类为无、早期、中期或晚期AMD，并进一步按照AREDS 12级严重程度量表进行分层。第二步：通过逻辑模型树机器学习技术，将所得的AMD评分与社会人口统计学临床数据及其他自动提取的影像数据相结合，以预测1年或2年内进展为晚期AMD的风险，在923名2年内病情进展的AREDS参与者、901名1年内病情进展的参与者以及2840名2年内未病情进展的参与者中进行训练和验证。对于那些被发现有进展为晚期AMD风险的患者，我们进一步预测晚期AMD进展的类型（干性或湿性）。
对于识别早期/无AMD与中期/晚期（即转诊水平）AMD，我们达到了99.2%的准确率。2年新发晚期AMD（任何类型）的预测模型准确率达到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年晚期AMD预测准确率为84%。
经过验证的基于彩色眼底照片的模型，用于AMD筛查和晚期AMD风险预测，现已准备好进行临床试验和潜在的远程医疗部署。
用于筛查转诊水平AMD和预测晚期AMD进展的无创、高精度且快速的AI方法，为我们对这种常见致盲疾病的治疗带来了显著的潜在改善。
Zotero 插件
