Key Points Question Which applications of artificial intelligence (AI) in biomedical research could generate the most translational value (ie, ability to lead to development that has measurable value for human health)? Findings In this cohort study using bibliometric analysis, 2 citation-based metrics were used as proxies for translational impact, and 75 unique medical applications of AI were identified from a set of 16 629 National Institutes of Health awards related to AI, using natural language processing. Varied applications were found to have high translatability, but bias against applications in basic biochemical analysis was evident. Meaning These findings suggest that National Institutes of Health award data applications of AI in biomedical research are not equivalent: some demonstrate greater potential for translational impact than others.
Purpose To apply machine learning models for predicting the number of pro re nata (PRN) injections of antivascular endothelial growth factor (anti-VEGF) for neovascular age-related macular degeneration (nAMD) in two years in the Comparison of AMD (age-related macular degeneration) Treatments Trials. Methods The data from 493 eligible participants randomized to PRN treatment of ranibizumab or bevacizumab were used for training (n = 393) machine learning models including support-vector machine (SVM), random forest, and extreme gradient boosting (XGBoost) models. Model performances of prediction using clinical and image data from baseline, weeks 4, 8, and 12 were evaluated by the area under the receiver operating characteristic curve (AUC) for predicting few (≤8) or many (≥19) injections, by R 2 and mean absolute error (MAE) for predicting the total number of injections in two years. The best model was selected for final validation on a test dataset (n = 100). Results Using training data up to week 12, the models achieved AUCs of 0.79–0.82 and 0.79–0.81 for predicting few and many injections, respectively, with R 2 of 0.34–0.36 (MAE = 4.45–4.58 injections) for predicting total injections in two years from cross-validation. In final validation on the test dataset, the SVM model had AUCs of 0.77 and 0.82 for predicting few and many injections, respectively, with R 2 of 0.44 (MAE = 3.92 injections). Important features included fluid in optical coherence tomography, lesion characteristics, and treatment trajectory in the first three months. Conclusions Machine learning models using loading dose phase data have the potential to predict two-year anti-VEGF demand for nAMD and quantify feature importance for these predictions. Translational Relevance Prediction of anti-VEGF injections using machine learning models from readily available data, after further validation on independent datasets, has the potential to help optimize treatment protocols and outcomes for nAMD patients in an individualized manner.
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