Artificial Intelligence Mechanism to Predict the Effect of Bone Mineral Densıty in Endocrıne Diseases—A Review

Abstract

As a rigid human organ, bone supports the human body and helps in the formation of shapes; it also protects many important parts of the human body. A healthy bone is crucial for humans for three main reasons: It promotes mobility, protects internal organs and blood cells formulation, and stores nutrients. As a result, researchers discovered stable bone mineral density (BMD) as an indicator for bone health. As BMD plays a key role in maintaining bone health, many clinical studies are proposed to perform the prediction of BMD and fracture risk level based on the gender and age descriptions. BMD is not only related with bone metabolism (BM), and it also correlates with Endocrine Diseases (EDs). Various hormone imbalance and biominerals are estimated to measure the deficiency of endocrine disorder, which mainly causes Diabetes Mellitus type 1, type 2 (T1DM, T2DM), hyperthyroidism, and hypothyroidism. Factors associated with bone mineral density and endocrine disorder are recognized with various proven medical studies. Therefore, the relationship between BMD and ED is linked and categorized for performing further analysis. Comparison of different prediction and classification algorithms used in the correlation of BMD and ED is discussed with the help of Artificial Intelligence Techniques (AITs). The above discussion on AIT is summarized with a dataset (image, biological data) related to BMD and ED. Henceforth, the evaluation metrics of different models are categorized and compared to show the requirement of AIT in the prediction of bone health.