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

Abstract

In the last decade, deep learning methods have garnered a great deal of attention in endocrinology research. In this article, we provide a summary of current deep learning 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 deep learning and its most commonly used architectures, and then we focus on the research trends of deep learning 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 deep learning model, as well as for understanding the thought process behind different decisions made by these models. Finally, we give concluding remarks and future directions.

Machine learning in TCM with natural products and molecules: current status and future perspectives

“…Deep learning has gained increasing importance for effective processing of large amounts of data and identifying patterns or functions hidden deep inside biological data. It has rapidly developed and successfully applied in many fields, including image recognition, robotics, speech recognition, and life sciences [ 5 ]. Deep learning uses different structural network models for different types of data and different application situations, and the primary models in deep learning include the convolutional neural network (CNN), Elman recurrent neural network (RNN), long short-term memory (LSTM), and generative adversarial network (GAN).…”