Objective:

To review the applications of machine learning (ML) in non-diabetic endocrine disorders, with a particular emphasis on thyroid-related research.

Approach:

Key Findings:

• 68% of studies focused on thyroid diseases, 20% on pituitary disorders, 7% on adrenal disorders, and 5% on parathyroid diseases.
• ML showed high diagnostic performance in thyroid nodule evaluation, malignancy prediction, and lymph node metastasis detection, with some models achieving accuracy comparable to expert radiologists.
• Pituitary ML models demonstrated effective differentiation of cystic adenomas and predicted treatment responses.
• Adrenal ML studies achieved high accuracy in differentiating tumor types and improving screening processes.
• Parathyroid ML applications enhanced detection accuracy and surgical outcomes.

Interpretation:

ML applications in endocrinology are promising, particularly in thyroid-related research, but face challenges in validation and clinical integration, necessitating strong interdisciplinary collaboration.

Limitations:

• Lack of model transparency and data imbalance.
• Small sample sizes and reliance on retrospective designs, limiting generalizability.
• Infrequent external validation and standardized reporting.
• Research imbalance favoring thyroid diseases over rarer endocrine disorders.

Conclusion:

High-quality, well-designed ML research is needed in endocrinology, with interdisciplinary collaboration essential for successful integration into clinical practice.

Sources:

• Endocrine Journal
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