PURPOSE:This study evaluated proximal extracranial vertebral artery (VA) anatomy on computed tomography angiography (CTA), using three side-specific variables: vertebral level of arterial origin, transverse-foramen entry level, and V1 loop morphology. METHODS:Seventy-five CTA cases were reviewed, yielding 150 side-specific VAs. The vertical level of VA origin was classified from C6 to T3 according to vertebral-body thirds and intervening disc levels. Transverse-foramen entry level and V1 morphology were recorded separately for each side. V1 morphology was classified as no loop, single-direction loop, sequential multidirectional loop, combined loop, kink, coil, or fenestration; a single V2 loop recorded in the same field was tabulated separately. Bilateral combinations were analysed at the patient level. RESULTS:The VA arose from the subclavian artery in 143/150 arteries (95.3%) and from the aortic arch in 4/150 arteries (2.7%); the parent vessel was not recorded in 3/150 arteries (2.0%). The most frequent origin levels were the T1 middle third (35/150, 23.3%), T1 upper third (27/150, 18.0%), T1 lower third (26/150, 17.3%), and C7-T1 disc level (20/150, 13.3%). Entry into the transverse foramen occurred at C6 in 136/150 arteries (90.7%), C5 in 9/150 (6.0%), and C7 in 5/150 (3.3%). V1 loop or tortuosity types were identified in 102/150 arteries (68.0%); including a single fenestration and a single V2-segment loop, non-direct V1 configurations were present in 104/150 arteries (69.3%). Exact right-left V1 morphology was asymmetrical in 54/75 patients (72.0%). CONCLUSIONS:This CTA protocol provides a detailed side-specific and bilateral description of proximal extracranial VA anatomy by combining origin height, foraminal entry level, and V1 morphology.
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.
