Study of Gall Bladder Stones as a New Piezoelectric Sensor Material Using Machine Learning

Gall bladder stones, traditionally considered pathological deposits, exhibit unique crystalline structures that may possess intrinsic piezoelectric properties. This study explores the potential of gall bladder stones as novel piezoelectric sensor materials. Using advanced characterization techniques, the structural and electro-mechanical properties of gall bladder stones were analyzed. Furthermore, machine learning algorithms were employed to model and predict the piezoelectric behavior based on compositional and morphological features extracted from the samples. The results demonstrate that gall bladder stones exhibit measurable piezoelectric responses comparable to conventional materials, suggesting their viability for sensor applications. This interdisciplinary approach combining materials science and machine learning offers a promising pathway for developing cost-effective and biocompatible piezoelectric sensors, opening new avenues in biomedical engineering and sensor technology.