An overview of deep learning applications in precocious puberty and thyroid dysfunction

Area of Science:

• Endocrinology
• Artificial Intelligence
• Medical Diagnostics

Background:

• Deep learning methods have gained significant traction in endocrinology over the past decade.
• Endocrine disorders, including those related to hormone production and timing, present complex diagnostic challenges.

Purpose of the Study:

• To summarize current deep learning applications in endocrinology research.
• To provide an accessible overview of deep learning for a broader audience.
• To focus on research trends in thyroid dysfunction classification and precocious puberty diagnosis.

Main Methods:

• Introduction to deep learning and common architectures.
• Review of research trends and applications in specific endocrine disorders.
• Analysis of strengths, weaknesses, and solutions for deep learning approaches.
• Discussion of practical considerations for model selection and interpretation.

Main Results:

• Deep learning shows promise in classifying thyroid dysfunction.
• Deep learning aids in the diagnosis of precocious puberty.
• Identified strengths and weaknesses of various deep learning models in endocrinology.

Conclusions:

• Deep learning offers powerful tools for advancing endocrine disorder research and diagnosis.
• Further research is needed to address challenges and optimize deep learning model implementation.
• Practical guidance is provided for developing and understanding deep learning models in this field.