Label-free rapid diagnosis of jaw osteonecrosis via the intersection of Raman spectroscopy and deep learning

Objectives

To establish a precise and efficient diagnostic framework for distinguishing medication-related osteonecrosis of the jaw, radiation-induced osteonecrosis of the jaw, and normal bone tissue, thus enhancing clinical decision-making and enabling targeted therapeutic interventions.

Methods

Raman spectroscopy was applied to investigate bone mineral composition, organic matrix content, and crystallinity in ninety bone tissue samples (30 MRONJ, 30 ORN, 30 control). Each mandible underwent 10 randomized spectral acquisitions, yielding 900 spectra across 200–2200 cm⁻¹. The raw spectral data were preprocessed using Labspec6 software (Horiba Scientific). Principal component analysis (PCA) and linear discriminant analysis (LDA) were employed for feature extraction and classification. Additionally, a ResNet18 deep learning architecture was employed to enhance diagnostic accuracy. The model's performance was evaluated using precision, recall, and the area under the receiver operating characteristic curve to ensure robustness.

Results

The PCA-LDA integration achieved 90.3 % accuracy in differentiating MRONJ, ORN, and healthy bone， with leave-one-out cross-validation confirming 89.1 % classification robustness. Furthermore, the ResNet18 deep learning model outperformed traditional classification methods, achieving 0.926 ± 0.024 accuracy, 0.924 ± 0.026 precision, 0.926 ± 0.024 recall, and 0.985 ± 0.007 AUROC on the validation set.

Significance

These findings underscore the significant potential of combining Raman spectroscopy with advanced deep learning techniques as a rapid, noninvasive, and highly reliable diagnostic tool. This approach not only enhances the ability to differentiate between MRONJ and ORN but also offers substantial implications for improving patient management and therapeutic outcomes in clinical practice.