Advances in Sensor-Based Detection and Machine Learning for Osteoporosis: A Comprehensive Survey of Diagnostic Techniques

Abstract

Abstract: One of the biggest challenges in the field of healthcare is the early identification of bone diseases. The increasing demand for diagnostic services often overwhelms current approaches, leading to increased healthcare costs, delayed identification, and patient discontent. To solve this problem, an Osteoporosis Early Detection System incorporating Biosensors and Machine Learning via the Internet of Things (IoT) is desperately needed. Osteoporosis difficulties and increased health risks are similar to congestion challenges that they might arise from delayed diagnosis. The scarcity of adequate technologies for early osteoporosis detection hinders effective healthcare delivery, especially in areas where the condition is more prevalent or during screenings that need quick identification. In order to enable prompt identification and notify medical professionals for preventive action, certain healthcare institutions have started including biosensors that can track changes in bone density. The development of these technologies could revolutionize the early diagnosis and treatment of osteoporosis and lessen the effects of the condition on patient’s health outcomes.