OBJECTIVE:To evaluate bilateral symmetry in root volume, length, mesiodistal (M/D) and buccopalatal (B/P) widths of maxillary permanent teeth anterior to the first molars. MATERIALS AND METHODS:This cross-sectional study analyzed CBCT data from 189 subjects (93 males and 96 females) with mean age 38.18 ± 8.08 years. Inclusion criteria required fully developed apices and no history of trauma, orthodontic treatment, or craniofacial anomalies. One investigator performed manual-assisted segmentation on 1,202 maxillary incisors, canines, and premolars. Root volume (mm3) root length (mm), M/D width (mm), and B/P width (mm) were measured. A linear mixed-effects model assessed bilateral symmetry. RESULTS:Males exhibited significantly greater root dimensions than females across all measured parameters (p < 0.001). After adjusting for biologic sex and tooth type, no significant main effect of side was observed for root volume (p = 0.196), root length (p = 0.714), or bucco-palatal root width (p = 0.381). A potential side effect was observed for mesiodistal root width (p = 0.014), although this did not meet the prespecified significance threshold of p < 0.01. CONCLUSIONS:Maxillary root dimensions differ by sex, with no statistically significant side effects for root volume, length, or bucco-palatal and mesio-distal widths. For root length, equivalence testing did not establish equivalence within the prespecified ±0.20-mm margin. Clinical research using symmetry should consider stratifying data by sex to ensure that sex-related anatomical differences are not mistaken for clinical pathology. Ultimately, this study provides a valuable baseline knowledge of symmetry that can be used in future research.
Artificial intelligence (AI) emerged as the transformative technology in biomedical imaging to improve the accuracy, efficiency and reproducibility of disease diagnosis. Integration of multimodal imaging data for the automated anatomical assessment in preclinical disease models remains insufficiently explored. This study aimed to develop and validate an AI-assisted framework integrating the magnetic resonance imaging (MRI) and computed tomography (CT) for comprehensive anatomical evaluation of the experimental rat disease models. Count of 180 Sprague-Dawley rats were allocated into healthy control and disease-induced groups representing neurological, pulmonary, hepatic and musculoskeletal disorders. High-resolution MRI and CT datasets were acquired longitudinally and processed using the deep-learning architectures for image segmentation, feature extraction, lesion detection and disease classification. Multimodal imaging repository constitutes 4,320 MRI and 3,960 CT image volumes, 96.8% met predefined quality criteria for analysis. Quantitative imaging revealed the significant increases in lesion volume, edema burden, tissue heterogeneity, structural distortion and tissue density across disease groups compared with controls (p < 0.001). Automated segmentation achieved Dice similarity coefficients ranging from 0.91 to 0.94, indicating excellent agreement with the expert annotations. Integrated MRI-CT AI model demonstrated superior diagnostic performance, achieving accuracy of 96.8%, sensitivity of 95.4%, specificity of 97.6% and area under the curve of 0.987, outperforming MRI-only and CT-only models. Strong correlations were observed between the AI-derived imaging biomarkers and histopathological severity scores (r = 0.86-0.93, p < 0.001). The findings demonstrated that AI-assisted multimodal MRI-CT integration provides highly accurate, reproducible and biologically relevant anatomical characterization of experimental disease models and supporting potential application in advanced preclinical imaging, translational research and future precision diagnostic systems.
