BMC infectious diseases · 2026
Scaling trachoma surveillance in endemic areas using machine learning.
СтатьяНазвание и аннотация: Перевод в очереди
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Kulohoma Benard W, Wesonga Colette S A
Аннотация
BACKGROUND: Trachoma remains a leading infectious cause of blindness in endemic regions despite progress towards global elimination. Accurate and scalable diagnosis remains challenging in areas with limited ophthalmic expertise. We developed and evaluated a reproducible transfer learning model using eyelid image-based data.
METHODS: We retrospectively analysed anonymised inner eyelid photographs collected from trachoma prevalence surveys conducted in Ethiopia, Tanzania, Australia, Solomon Islands, Colombia, the Gambia, and Guatemala (n = 572 images). Images were categorized as trachoma (n = 251) or not trachoma (n = 321) based on consensus grades from the Tropical Data certified graders. Data were processed and analysed in R (version 4.4.1) using the keras and tensorflow packages interfaced with Python (version 3.10) through reticulate. A ResNet50 convolutional neural network pretrained on ImageNet was fine-tuned for binary classification. The model was trained for up to 30 epochs with early stopping and adaptive learning-rate reduction. Performance on a held-out validation set (n = 62, 12%) was evaluated using accuracy, sensitivity, specificity, and Cohen's κ.
RESULTS: The ResNet50 model achieved an overall accuracy of 85.4% (95% CI 76.3-92%) on the validation set. The model achieved a sensitivity of 80.9% for detecting trachoma and a specificity of 90.5% for correctly identifying non-trachoma eyes. Agreement between predictions and grader labels was substantial (κ = 0.71, 95% CI 0.58-0.84).
CONCLUSION: Our findings demonstrate the feasibility of using a reproducible R-based deep learning pipeline for automated trachoma classification in large-scale surveys. These findings demonstrate the feasibility of automated trachoma classification using a transfer-learning framework. Larger datasets will be required before operational deployment in surveillance programmes.
CLINICAL TRIAL NUMBER: Not applicable.
METHODS: We retrospectively analysed anonymised inner eyelid photographs collected from trachoma prevalence surveys conducted in Ethiopia, Tanzania, Australia, Solomon Islands, Colombia, the Gambia, and Guatemala (n = 572 images). Images were categorized as trachoma (n = 251) or not trachoma (n = 321) based on consensus grades from the Tropical Data certified graders. Data were processed and analysed in R (version 4.4.1) using the keras and tensorflow packages interfaced with Python (version 3.10) through reticulate. A ResNet50 convolutional neural network pretrained on ImageNet was fine-tuned for binary classification. The model was trained for up to 30 epochs with early stopping and adaptive learning-rate reduction. Performance on a held-out validation set (n = 62, 12%) was evaluated using accuracy, sensitivity, specificity, and Cohen's κ.
RESULTS: The ResNet50 model achieved an overall accuracy of 85.4% (95% CI 76.3-92%) on the validation set. The model achieved a sensitivity of 80.9% for detecting trachoma and a specificity of 90.5% for correctly identifying non-trachoma eyes. Agreement between predictions and grader labels was substantial (κ = 0.71, 95% CI 0.58-0.84).
CONCLUSION: Our findings demonstrate the feasibility of using a reproducible R-based deep learning pipeline for automated trachoma classification in large-scale surveys. These findings demonstrate the feasibility of automated trachoma classification using a transfer-learning framework. Larger datasets will be required before operational deployment in surveillance programmes.
CLINICAL TRIAL NUMBER: Not applicable.