AI-Powered Android App for X-ray-Based Detection of Bone and Chest Conditions in Physiotherapy

Abstract

Physiotherapists face difficulties in detecting the exact problem of their patients. De tecting the exact problem will make it easier for physiotherapists to proceed with their treatment. This study proposes the creation of a new Android application that incorporates an AI diagnostic ca pability into physiotherapy practice. The system is based on the MobileNetV1 convolutional neural network (CNN) architecture to detect bone fractures and chest secretions (e.g., pneumonia) from X-ray images. Through a REST-API backend architecture, real-time predictions are produced using pre-trained TensorFlow deep learning models. For fractures, the model achieved an accuracy score of 96%, while the chest secretion detection model attained 94% accuracy on publicly available datasets. The system provides physiotherapy practitioners with actionable diagnostic clues by establishing a translation path way from AI to physiotherapy research, especially in low-resource settings.