News Research Diagnostics & Imaging Research and Evidence

Deep learning model uses hand images to improve acromegaly detection

"A significant step forward in diagnostic accuracy and in promoting equitable health care delivery.” 

April 13, 2026 By Matthew Solan 2 min read
Share

5 Key Takeaways

  • 1

    A deep learning model effectively identified acromegaly using hand images, preserving patient privacy by excluding facial and fingerprint features.

  • 2

    The model achieved 89% sensitivity and 91% specificity, outperforming board-certified endocrinologists in acromegaly detection.

  • 3

    The study involved 716 patients, including 317 with acromegaly, recruited from 15 Japanese pituitary centers.

  • 4

    Subgroup analyses showed consistent model performance across age, remission status, and sex, indicating robust diagnostic capability.

  • 5

    Limitations include potential generalizability issues due to the study's focus on Japanese patients and incomplete biochemical confirmation.

AACE Endocrine AI is published by Conexiant under a license arrangement with the American Association of Clinical Endocrinology, Inc. (AACE®). The ideas and opinions expressed in AACE Endocrine AI do not necessarily reflect those of Conexiant or AACE. For more information, see Policies.

Related Content