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Automatic acromegaly detection using deep learning on hand images: a multicenter observational study.

Yuka Ohmachi1, Mizuho Nishio2, Ichiro Abe3

  • 1Division of Diabetes and Endocrinology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe 650-0017, Hyogo, Japan.

The Journal of Clinical Endocrinology and Metabolism
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

A new artificial intelligence (AI) model accurately detects acromegaly using hand images, outperforming human specialists. This privacy-conscious tool shows promise for early acromegaly diagnosis in public health screenings.

Keywords:
acromegalyartificial intelligencedeep learningearly detectionhand images

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Area of Science:

  • Endocrinology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Acromegaly diagnosis and intervention present clinical challenges, necessitating novel diagnostic tools.
  • Existing artificial intelligence (AI) models for acromegaly detection face limitations due to privacy concerns.
  • Developing privacy-conscious AI for acromegaly detection is crucial for timely patient management.

Purpose of the Study:

  • To develop and evaluate a privacy-conscious deep learning model for acromegaly detection using hand images.
  • To assess the model's performance in identifying acromegaly based on specific hand features.

Main Methods:

  • A nationwide multicenter study involved 716 patients (317 acromegaly, 399 controls) and 11,480 hand images from 15 Japanese centers.
  • A ResNet-50 deep learning model was trained on dorsal hand and fist sign images, excluding palm/fingerprint regions.
  • Model performance was evaluated using data augmentation, 5-fold cross-validation, and comparison against endocrinologists' assessments.

Main Results:

  • The AI model achieved high diagnostic accuracy, with a sensitivity of 0.89 and specificity of 0.91.
  • The model demonstrated superior performance compared to endocrinologists, achieving an F1-score of 0.89 versus 0.43-0.63.
  • An area under the receiver operating characteristic curve (AUC) of 0.96 indicates excellent discriminative ability.

Conclusions:

  • Dorsal hand and fist signs are valuable diagnostic clues for acromegaly, effectively captured by the AI model.
  • The privacy-conscious AI model shows potential for deployment in public health settings, such as health checkups.
  • Further validation with larger, diverse datasets is recommended to confirm the model's generalizability.