Automatic acromegaly detection using deep learning on hand images: a multicenter observational study.
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Ohmachi Yuka, Nishio Mizuho, Abe Ichiro, Kobayashi Kunihisa, Iida Tomoko, Shirakawa Manabu, Nagata Yuichi, Takeuchi Kazuhito, Taguchi Akira, Kinoshita Yasuyuki, Fukuhara Noriaki, Nishioka Hiroshi, Tahara Shigeyuki, Fujio Shingo, Ogura Takafumi, Kurosaki Masamichi, Hada Yurika, Susa Shinji, Otsuka Yuki, Otsuka Fumio, Ishida Ikuhiro, Kameda Hiraku, Oyama Kenichi, Yamada Shozo, Kobatake Masaki, Oi-Yo Yuka, Fujii Genki, Tomofuji Seiji, Sasaki Yuriko, Bando Hironori, Yamamoto Masaaki, Iguchi Genzo, Motomura Yuma, Tsujimoto Yasutaka, Yamamoto Naoki, Suzuki Masaki, Urai Shin, Takahashi Michiko, Murakami Takamichi, Ogawa Wataru, Fukuoka Hidenori
Аннотация
OBJECTIVE: To develop a privacy-conscious deep learning model for detecting acromegaly using hand images.
METHODS: This nationwide multicenter study enrolled 716 patients (317 with acromegaly and 399 controls) and 11 480 images from 15 Japanese pituitary centers. The inclusion criteria were age ≥18 years and care received at the participating facilities. Hand images focusing on the dorsal and fist sign, excluding the palm/fingerprint regions, were used to develop the model. The data were split into training/validation (12 centers) and test (3 centers) datasets. A ResNet-50-based model was trained using PyTorch with data augmentation and 5-fold cross-validation. For each patient, the predictions were averaged over 4 images. The performance of the model was compared with that of endocrinologists.
RESULTS: The model achieved a sensitivity of 0.89, specificity of 0.91, positive predictive value of 0.88, negative predictive value of 0.93, F1-score of 0.89, and an area under the receiver operating characteristic curve of 0.96, outperforming specialists (F1-score range: 0.43-0.63).
CONCLUSION: This study highlights the utility of dorsal hand and fist sign as diagnostic clues for acromegaly, which the AI model captured more accurately than endocrinologists. Using this privacy-conscious feature, this model can be deployed in public settings like health checkups. Further validation using larger datasets, including healthy individuals and diverse diseases, is necessary.