Facial Analysis in Acromegaly Using Machine Learning: Toward Earlier Diagnosis

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.

This study aimed to develop and evaluate machine learning models that can identify acromegaly-specific facial features using prediagnostic photographs, potentially enabling earlier diagnosis.

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.

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.

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.