Research Information System
The Journal of clinical endocrinology and metabolism, vol.111, no.3, 2026 (SCI-Expanded, Scopus)
CONTEXT: Acromegaly is a rare and progressive disorder often diagnosed late due to its insidious onset and gradually evolving facial features. Early detection remains a critical unmet need to reduce disease-associated morbidity and mortality. OBJECTIVE: This study aimed to develop and evaluate machine learning models that can identify acromegaly-specific facial features using prediagnostic photographs, potentially enabling earlier diagnosis. METHODS: A total of 489 facial photographs from 92 patients with acromegaly and 254 images from 88 controls were analyzed. A 2-stage pipeline was implemented: (1) deep feature extraction using a pretrained VGG-Face model followed by support vector machine (SVM) classification, and (2) an interpretable model using 5 landmark-based facial measurements. Separate data sets were created using prediagnosis, postdiagnosis, and combined images to evaluate model performance. RESULTS: The best classification results were obtained from the prediagnosis data set (mean 7.47 years before diagnosis), with an area under the curve (AUC) of 0.982 and accuracy of 91.5%. Interpretability analyses highlighted maxillary, nasal, and orbital regions as key facial zones. The interpretable model, using facial ratios, achieved moderate accuracy (AUC = 0.776) while providing clinical insight into contributing features such as face width-to-height ratio and philtrum height. CONCLUSION: Our findings demonstrate that acromegaly-related facial features can be detected years before clinical diagnosis using machine learning. By combining high-performance deep models with interpretable approaches, this study supports the potential for artificial intelligence-based facial screening tools to aid in early detection of acromegaly.