An overview of deep learning applications in precocious puberty and thyroid dysfunction - Razzaq, Misbah; Clément, Frédérique; Yvinec, Romain (Frontiers in Endocrinology, 2022)
In the last decade, deep learning (DL) methods have garnered a great deal of attention in endocrinology research. In this article, we provide a summary of current DL applications in endocrine disorders caused by either precocious onset of adult hormone or abnormal amount of hormone production. To give access to the broader audience, we start with a gentle introduction to DL and its most commonly used architectures, and then we focus on the research trends of DL applications in thyroid dysfunction classification and precocious puberty diagnosis. We highlight the strengths and weaknesses of various approaches and discuss potential solutions to different challenges. We also go through the practical considerations useful for choosing (and building) the DL model, as well as for understanding the thought process behind different decisions made by these models. Finally, we give concluding remarks and future directions.