Intelligently Quantifying the Entire Irregular Dental Structure

Abstract

Quantitative analysis of irregular anatomical structures is crucial in oral medicine, but clinicians often typically measure only several representative indicators within the structure as references. Deep learning semantic segmentation offers the potential for entire quantitative analysis. However, challenges persist, including segmentation difficulties due to unclear boundaries and acquiring measurement landmarks for clinical needs in entire quantitative analysis. Taking the palatal alveolar bone as an example, we proposed an artificial intelligence measurement tool for the entire quantitative analysis of irregular dental structures. To expand the applicability, we have included lightweight networks with fewer parameters and lower computational demands. Our approach finally used the lightweight model LU-Net, addressing segmentation challenges caused by unclear boundaries through a compensation module. Additional enamel segmentation was conducted to establish a measurement coordinate system. Ultimately, we presented the entire quantitative information within the structure in a manner that meets clinical needs. The tool achieved excellent segmentation results, manifested by high Dice coefficients (0.934 and 0.949), intersection over union (0.888 and 0.907), and area under the curve (0.943 and 0.949) for palatal alveolar bone and enamel in the test set. In subsequent measurements, the tool visualizes the quantitative information within the target structure by scatter plots. When comparing the measurements against representative indicators, the tool’s measurement results show no statistically significant difference from the ground truth, with small mean absolute error, root mean squared error, and errors interval. Bland-Altman plots and intraclass correlation coefficients indicate the satisfactory agreement compared with manual measurements. We proposed a novel intelligent approach to address the entire quantitative analysis of irregular image structures in the clinical setting. This contributes to enabling clinicians to swiftly and comprehensively grasp structural features, facilitating the design of more personalized treatment plans for different patients, enhancing clinical efficiency and treatment success rates in turn.