Computer methods and programs in biomedicine · 2026
Fragility Fracture Risk Prediction through the integration of Artificial Intelligence and Radiofrequency Echographic Multi Spectrometry (REMS) Technology.
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Peluso Giusy, Conversano Francesco, Bellone Mauro, Lombardi Fiorella Anna, Pisani Paola, Epicoco Italo, Cafaro Massimo, Casciaro Sergio
Аннотация
BACKGROUND AND OBJECTIVE: Bone fragility is a major clinical issue and a leading cause of disability, especially in aging populations. Traditional diagnostics rely on bone mineral density, which does not fully capture structural and micro-architectural factors influencing fracture risk. This study investigates Deep Learning applied to ultrasound Radiofrequency Echographic Multi Spectrometry (REMS) data to improve fracture risk identification and stratification.
METHODS: A retrospective multicenter dataset of 3510 participants was analyzed. A Convolutional Neural Network based on AlexNet was trained in a supervised manner to classify subjects as non-fractured, at-risk, or fractured. Performance was evaluated via 5-fold cross-validation, with an ensemble of the best model from each fold to enhance robustness and generalization. A continuous Fragility Index (0-100) was derived from softmax probabilities to quantify risk and stratify patients into low-, medium-, and high-risk categories.
RESULTS: The model showed consistent performance at both frame and patient levels. For binary classification of at-risk versus not-at-risk individuals, the ensemble achieved approximately 80% sensitivity and 80% specificity, balancing detection and false positives. The Fragility Index offered a continuous, interpretable measure of fracture susceptibility, enabling effective risk stratification. Weak correlation with bone mineral density suggests that the model captures structural features beyond mineral content.
CONCLUSIONS: Deep Learning analysis of ultrasound Radio-Frequency signals provides a non-ionizing, portable approach for assessing bone fragility. This method may support early identification of at-risk individuals and enable personalized prevention and management strategies in osteoporosis care.
METHODS: A retrospective multicenter dataset of 3510 participants was analyzed. A Convolutional Neural Network based on AlexNet was trained in a supervised manner to classify subjects as non-fractured, at-risk, or fractured. Performance was evaluated via 5-fold cross-validation, with an ensemble of the best model from each fold to enhance robustness and generalization. A continuous Fragility Index (0-100) was derived from softmax probabilities to quantify risk and stratify patients into low-, medium-, and high-risk categories.
RESULTS: The model showed consistent performance at both frame and patient levels. For binary classification of at-risk versus not-at-risk individuals, the ensemble achieved approximately 80% sensitivity and 80% specificity, balancing detection and false positives. The Fragility Index offered a continuous, interpretable measure of fracture susceptibility, enabling effective risk stratification. Weak correlation with bone mineral density suggests that the model captures structural features beyond mineral content.
CONCLUSIONS: Deep Learning analysis of ultrasound Radio-Frequency signals provides a non-ionizing, portable approach for assessing bone fragility. This method may support early identification of at-risk individuals and enable personalized prevention and management strategies in osteoporosis care.