Automatic Detection for Acromegaly Using Hand Photographs: A Deep-Learning Approach

Machine learning assisted diagnosis of acromegaly from facial photographs has been proved feasible in recent years. According to our previous research, facial and limb changes exist in patients with acromegaly at early stage. We aimed to facilitate the process of early self-screening for acromegaly from hand photographs by using a deep-learning approach. In this study, a dataset containing hand photographs of 635 acromegaly patients and 192 normal people were used to train a Deep Convolution Neural Network (DCNN). We augmented these images with tailed raw data. The prediction is performed in an end-to-end paradigm without manual pre-processing, from the input photograph to the final prediction. The trained models were evaluated on a separate dataset to validate the effectiveness. Different kinds of advanced DCNN architecture were explored in this novel task and they showed significant performance compared with the results from human doctors specialized in pituitary adenoma. We further used heat-map to provide visual explanations to illustrate how the DCNN diagnosed the acromegaly. The final result of our experiment showed a sensitivity of 0.983, a specificity of 0.920, a PPV of 0.966, a NPV of 0.958 and a F1-score of 0.974. In our method, the sensitivity was higher than doctors' predictions, which indicates that our method could effectively help people detect acromegaly by themselves. Furthermore, our algorithm paid more attention to fingers and joints on which human doctors focused. This is the first study to investigate whether it is possible to detect acromegaly by machine learning from hand photographs and compare the result with human doctors specialized in pituitary adenoma. This study provided an easy-to-use tool for early self-screening of acromegaly for people without medical knowledge, so that acromegaly patients can get more timely treatment.