The last results reached within the project GAP (Image-Guided experimental and computational Analysis of fractured Patients), have been recently published in the article “The synergy of synchrotron imaging and convolutional neural networks towards the detection of human micro-scale bone architecture and damage” on Science Direct.
The project GAP was developed by a team of ASP students of 16th Cycle, attempting to face the critical issue of early diagnosis of bone fractures.
The study of bone damage processes at different scales is, in fact, essential for understanding fracture mechanisms, which are mostly induced by a trauma or a pathology (such as osteoporosis). Early diagnosis is critical for reducing the burden of bone fractures on the health care system and the economy. This problem is deeply rooted in our society and it will grow more and more because of the increase in average age; in fact, according to data from the Italian Ministry of Health, 40% of the total Italian population, mostly after the age of 65, will have a fracture of the femur, vertebrae or wrist.
GAP team attempts to face this crucial challenge through a Convolutional Neural Network able to analyze bone micro-architecture features in high-resolution synchrotron data.