Table.

Studies on Ocular Diseases Using Artificial Intelligence Techniques With EHR Data

Authors	Aim	Disease	Algorithm Type	Specific Techniques	Performance	Conclusions
Lin et al.20	Disease detection	Myopia	Supervised machine learning	Random forest	95% CI for predicting onset of high myopia. 3 years onset prediction (AUC: 94%–98.5%), 5 years (85.6%–90.1%), 8 years (80.1%–83.7%)	Machine learning with EHR data can accurately predict myopia onset
Lee et al.21	Improve diagnostic accuracy	AMD	Deep learning	Convolutional neural networks	For each patient, AUC (97.45%), accuracy (93.54%), sensitivity (92.64%), and specificity (93.69%)	Linked OCT images to EMR data can improve the accuracy of a deep learning model when used to distinguish AMD from normal OCT images
Baxter et al.22	Risk assessment	Open-angle glaucoma	Supervised machine learningDeep learning	Logistic regression, random forests,ANNs	AUC of logistic model (67%), random forest (65%), ANNs (65%)	Existing systemic data in the EHR can identify POAG patients at risk of progression to surgical intervention
Chaganti et al.16	Identify risk factors and improve diagnostic accuracy	Glaucoma, intrinsic optic nerve disease, optic nerve edema, orbital inflammation, and thyroid eye disease	Supervised machine learning	Random forest	AUC of classifiers: glaucoma (88%), intrinsic optic neuritis (76%), optic nerve edema (78%), orbital inflammation (77%), thyroid eye disease (85%)	EMR phenotype (from pyPheWAS) can improve the predictive performance of a random forest classifier with imaging biomarkers
Apostolova et al.23	Patient identification	Open globe injury	Supervised machine learning & Text-mining	SVM NLP–Word embeddings	Text classification: precision (92.50%), recall (89.83%)	Free-form text with machine learning methods can used to identify open globe injury
Saleh et al.18	Risk assessment	DR	Supervised machine learning	FRF, DRSA	Performance of FRF: Accuracy (80.29%), sensitivity (80.67%), specificity (80.18%) Performance of DRSA: Accuracy (77.32 %), sensitivity (76.89 %), specificity (77.43%) of DRSA.	Ensemble classifiers (RFR and DRSA) can be applied for diabetic retinopathy risk assessment. The 2-step aggregation procedure is recommended
Rohm et al.24	Predict progression	AMD	Supervised machine learning	AdaBoost, Gradient Boosting, Random Forests, Extremely Randomized trees, LASSO	Accuracy of logMAR VA prediction after VEGF injections.3 months: MAE (0.14), RMSE (0.18)12 months: MAE (0.16), RMSE (0.2)	EHR data of patients with neovascular AMD can be used to predict visual acuity by using machine learning models
Yoo and Park25	Risk assessment	DR	Supervised machine learning	Ridge, elastic net, and LASSO	In external validation, LASSO predicted DR: AUC (82%), accuracy (75.2%), sensitivity (72.1%), and specificity (76.0%)	LASSO with EHR data can be used to predict DR risk among diabetic patients
Fraccaro et al.17	Improve diagnostic accuracy	AMD	Supervised machine learning	Logistic regression, decision trees, SVM, random forests, and AdaBoost	AUC of random forest, logistic regression, and AdaBoost (92%); SVM, decision trees (90%)	Machine learning algorithms using clinical EHR data can be used to improve diagnostic accuracy of AMD
Sramka et al.26	Improve surgical outcome	Cataracts	Supervised machine learningDeep learning	SVM-RMMLNN-EM	Both SVM-RM and MLNN-EM achieved significantly better results than the Barrett Universal II formula in the ±0.50 D PE category	SVM-RM and MLNN-EM with EHR data can be used to improve clinical IOL calculations and improve cataract surgery refractive outcomes
Peissig et al.27	Patient identification	Cataracts	Text-mining	NLP	The multimodal model shows results including sensitivity (84.6%), specificity (98.7%), PPV (95.6%), and NPV (95.1%)	A multimodal strategy incorporating optical character recognition and natural language processing can increase the number of cataracts cases identified
Gaskin et al.15	Identify and predict risks of cataract surgery complications	Cataract	Supervised machine learning Text-mining	Bootstrapped LASSO, random forest NLP	Based on the LASSO model, younger age (<60 years old), prior anterior vitrectomy or refractive surgery, history of AMD, and complex cataract surgery were risk factors associated with postoperative complicationsThe random forest model shows high NPV > 95% and moderate sensitivity (67%) and AUC (65%)	Bootstrapped LASSO can be used to identify risk factors of postoperative complications of cataract surgeryRandom forest shows good reliability for predicting cataract surgery complications
Skevofilakas et al.28	Risk assessment	DR	Deep learningSupervised machine learning	FNN and iHWNNCART	AUC of hybrid DSS (98%), iHWNN (97%), FNN (88%), and CART (86%).	Hybrid DSS trained on imaging and related EHR data can estimate the risk of a type 1 diabetic patient developing diabetic retinopathy

AMD, age-related macular degeneration; ANN, artificial neural network; AUC, area under the curve; CART, classification and regression tree; CI, confidence interval; DR, diabetic retinopathy; DRSA, dominance-based rough set approach; DSS, decision support system; EHR, electronic medical record; EMR, electronic medical record; FNN, feed forward neural network; FRF, fuzzy random forest; iHWNN, improved hybrid wavelet neural network; IOL, intraocular lens; LogMAR, logarithm of the minimum angle of resolution; LASSO, least absolute shrinkage and selection operator; MAE, mean absolute error; MLNN-EM, multilayer neural network ensemble model; NLP, natural language processing; NPV, negative predictive value; OCT, optical coherence tomography; POAG, primary open-angle glaucoma; RFR, random forest regression; RMSE, root mean squared error; SVM, support vector machine; SVM-RM, support vector machine regression model; VA, visual acuity; VEGF, vascular endothelial growth factor.