Differentiation of Active Corneal Infections From Healed Scars Using Deep Learning.
OBJECTIVE: To develop and evaluate an automated, portable algorithm to differentiate active corneal ulcers from healed scars using only external photographs.DESIGN: A convolutional neural network was trained and tested using photographs of corneal ulcers and scars.SUBJECTS: De-identified photographs of corneal ulcers were obtained from the Steroids for Corneal Ulcers Trial (SCUT), Mycotic Ulcer Treatment Trial (MUTT), and Byers Eye Institute at Stanford University.METHODS: Photographs of corneal ulcers (n=1313) and scars (n=1132) from the SCUT and MUTT trials were used to train a convolutional neural network (CNN). The CNN was tested on two different patient populations from eye clinics in India (n=200) and the Byers Eye Institute at Stanford University (n=101). Accuracy was evaluated against gold standard clinical classifications. Feature importances for the trained model were visualized using Gradient-weighted Class Activation Mapping (Grad-CAM).MAIN OUTCOME MEASURE: Accuracy of the CNN was assessed via F1 score. Area under the receiver operating characteristic curve (ROC) was used to measure the precision-recall trade-off.RESULTS: The CNN correctly classified 115/123 active ulcers and 65/77 scars in corneal ulcer patients from India (F1 score: 92.0% (95% CI: 88.2 - 95.8%), sensitivity: 93.5% (95% CI: 89.1 - 97.9%), specificity: 84.42% (95% CI: 79.42 - 89.42%), ROC (AUC=0.9731)). The CNN correctly classified 43/55 active ulcers and 42/46 scars in corneal ulcer patients from Northern California (F1 score: 84.3% (95% CI: 77.2 - 91.4%), sensitivity: 78.2% (95% CI: 67.3 - 89.1%), specificity: 91.3% (95% CI: 85.8 - 96.8%), ROC (AUC=0.9474)). The CNN visualizations correlated with clinically relevant features such as corneal infiltrate, hypopyon, and conjunctival injection.CONCLUSION: The CNN classified corneal ulcers and scars with high accuracy and generalizes to patient populations outside of its training data. The CNN focuses on clinically relevant features when it makes a diagnosis. The CNN demonstrates potential as an inexpensive diagnostic approach that may aid triage in communities with limited access to eye care.
View details for DOI 10.1016/j.ophtha.2021.07.033
View details for PubMedID 34352302
Distributed deep learning networks among institutions for medical imaging
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
2018; 25 (8): 945–54
Deep learning has become a promising approach for automated support for clinical diagnosis. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In this study, we propose methods of distributing deep learning models as an attractive alternative to sharing patient data.We simulate the distribution of deep learning models across 4 institutions using various training heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The training heuristics investigated include ensembling single institution models, single weight transfer, and cyclical weight transfer. We evaluated these approaches for image classification in 3 independent image collections (retinal fundus photos, mammography, and ImageNet).We find that cyclical weight transfer resulted in a performance that was comparable to that of centrally hosted patient data. We also found that there is an improvement in the performance of cyclical weight transfer heuristic with a high frequency of weight transfer.We show that distributing deep learning models is an effective alternative to sharing patient data. This finding has implications for any collaborative deep learning study.
View details for PubMedID 29617797
View details for PubMedCentralID PMC6077811