Current Issue : January-March Volume : 2026 Issue Number : 1 Articles : 5 Articles
Background/Objectives: Bilateral symmetry of craniofacial structures is a fundamental principle in clinical application, particularly in procedures involving unilateral trauma or skeletal loss. The zygomaticomaxillary suture (ZMS), located at the articulation between the zygomatic bone and maxilla, is considered a potentially stable midfacial landmark owing to its distinct anatomical position and relevance in surgical planning. This study aimed to evaluate the bilateral symmetry of the ZMS and its surrounding anatomical structures in healthy adults using three-dimensional CT reconstructions and to develop predictive models for contralateral estimation. Methods: Craniofacial CT scans of 200 adult individuals (101 females and 99 males, aged ≥18 years) were retrospectively analyzed. Fourteen morphometric parameters related to the ZMS and adjacent craniofacial structures were measured bilaterally on 3D reconstructions generated from CT data. Statistical analyses included tests for normality, sex and side comparisons, correlation analysis, and linear regression to develop side-predictive formulas. Results: No statistically significant differences were found between the right and left sides for any parameter, confirming a high degree of bilateral symmetry. However, significant sex-based differences were observed in two parameters: the lateral extension of the ZMS (p = 0.024 right; p = 0.046 left) and piriform aperture width (p = 0.017). Regression models developed for each sex provided reliable estimates of contralateral morphometric values based on single-sided measurements. Conclusions: The results confirm high bilateral symmetry of the ZMS and adjacent midfacial structures, supporting its reliability as a reference point in surgical planning and facial reconstruction. Regression models enhance the accuracy of mirror-based approaches in unilateral midfacial defects....
In this paper, we aimed to evaluate the efficacy and usefulness of three brief, easy-toadminister, and repeatable tests, namely SDMT, Digit Span Forward (DSF), and Digit Span Backward (DSB) in MS patients (MSp), and compared the results with those of healthy volunteers (CONs). We were hoping to identify the most sensitive test that could be used regularly in clinical practice. In addition, we tried to identify the metabolic background of the cognitive setting using the advanced radiological method, Mescher–Garwood (MEGA)- edited 1H Magnetic Resonance Spectroscopy (1H-MRS). A total of 22 relapsing MSp and 22 CONs were enrolled. The SDMT, DSF, and DSB tests were used on all participants. The patients also underwent a 1H-MRS brain examination. In addition to N-Acetyl-Aspartate (tNAA), Myoinositol (mIns), Choline (tCho), and Creatine (tCr) were also evaluated GABA and Glutamate–Glutamine (Glx) ratios. CONs were superior to MSp in the results of all neurocognitive tests. The DSB was found to be the most sensitive test for identifying MSp. The SDMT in MSp correlated with inflammatory and degenerative metabolites in the thalamus, hippocampus, and corpus callosum. A correlation between increased Glxand GABA-ratios and SDMT was found. Unlike the SDMT, the DSF and DSB showed correlations with inflammatory metabolites in the caudate nucleus and hypothalamus. DSF correlated with GABA ratios in the hippocampus. Our study confirms the efficacy of DSF and DSB tests in evaluating working memory cognitive impairment in MSp, showing an association of the tests with specific brain metabolites....
We report the case of a 16-year-old girl presenting with a painless, clinically stable subcutaneous swelling of the nasal dorsum with a three-year history. Despite an extensive multidisciplinary diagnostic work-up—including dermatological, otorhinolaryngological, and radiological evaluations (ultrasound, CT, and MRI)—the nature of the lesion remained indeterminate. In order to achieve a definitive diagnosis while preserving the nasal profile aesthetics, the mass was entirely excised via an endoscope-assisted closed rhinoseptoplasty approach. Histopathological analysis revealed a spindle cell lipoma characterized by CD34 positivity and a Ki-67 proliferation index of less than 1%. This finding is extremely rare in terms of both anatomical location and patient age. The present case highlights the crucial role of histopathological examination in establishing the correct diagnosis, supported by a multidisciplinary assessment....
Accurate shade matching is essential in restorative and prosthetic dentistry yet remains difficult due to subjectivity in visual assessments. We develop and evaluate a deep learning approach for the simultaneous segmentation of natural teeth and shade guides in intraoral photographs using four fine-tuned variants of Segment Anything Model 2 (SAM2: tiny, small, base plus, and large) and a UNet baseline trained under the same protocol. The spatial performance was assessed using the Dice Similarity Coefficient (DSC), the Intersection over the Union (IoU), and the 95th-percentile Hausdorff distance normalized by the ground-truth equivalent diameter (HD95). The color consistency within masks was quantified by the coefficient of variation (CV) of the CIELAB components (L*, a*, b*). The perceptual color difference was measured using CIEDE2000 (ΔE00). On a held-out test set, all SAM2 variants achieved a high overlap accuracy; SAM2-large performed best (DSC: 0.987 ± 0.006; IoU: 0.975 ± 0.012; HD95: 1.25 ± 1.80%), followed by SAM2-small (0.987 ± 0.008; 0.974 ± 0.014; 2.96 ± 11.03%), SAM2-base plus (0.985 ± 0.011; 0.971 ± 0.021; 1.71 ± 3.28%), and SAM2-tiny (0.979 ± 0.015; 0.959 ± 0.028; 6.16 ± 11.17%). UNet reached a DSC = 0.972 ± 0.020, an IoU = 0.947 ± 0.035, and an HD95 = 6.54 ± 16.35%. The CV distributions for all of the prediction models closely matched the ground truth (e.g., GT L*: 0.164 ± 0.040; UNet: 0.144 ± 0.028; SAM2-small: 0.164 ± 0.038; SAM2-base plus: 0.162 ± 0.039). The full-mask ΔE00 was low across models, with the summary statistics reported as the median (mean ± SD): UNet: 0.325 (0.487 ± 0.364); SAM2-tiny: 0.162 (0.410 ± 0.665); SAM2-small: 0.078 (0.126 ± 0.166); SAM2-base plus: 0.072 (0.198 ± 0.417); SAM2-large: 0.065 (0.167 ± 0.257). These ΔE00 values lie well below the ≈1 just noticeable difference threshold on average, indicating close chromatic agreement between the predictions and annotations. Within a single dataset and training protocol, fine-tuned SAM2, especially its larger variants, provides robust spatial accuracy, boundary reliability, and color fidelity suitable for clinical shade-matching workflows, while UNet offers a competitive convolutional baseline. These results indicate technical feasibility rather than clinical validation; broader baselines and external, multi-center evaluations are needed to determine its suitability for routine shade-matching workflows....
A 9.4T brain MRI is the highest resolution MRI scanner in the public market. It offers submillimeter brain imaging with exceptional anatomical detail, making it one of the most powerful tools for detecting subtle structural changes associated with neurological conditions. Current segmentation models are optimized for lower-field MRI (1.5T–3T), and they struggle to perform well on 9.4T data. In this study, we present the GA-MS-UNet++, the world’s first deep learning-based model specifically designed for 9.4T brain MRI segmentation. Our model integrates multi-scale residual blocks, gated skip connections, and spatial channel attention mechanisms to improve both local and global feature extraction. The model was trained and evaluated on 12 patients in the UltraCortex 9.4T dataset and benchmarked against four leading segmentation models (Attention U-Net, Nested U-Net, VDSR, and R2UNet). The GA-MS-UNet++ achieved a state-of-the-art performance across both evaluation sets. When tested against manual, radiologist-reviewed ground truth masks, the model achieved a Dice score of 0.93. On a separate test set using SynthSeg-generated masks as the ground truth, the Dice score was 0.89. Across both evaluations, the model achieved an overall accuracy of 97.29%, precision of 90.02%, and recall of 94.00%. Statistical validation using the Wilcoxon signed-rank test (p < 1 × 10−5) and Kruskal–Wallis test (H = 26,281.98, p < 1 × 10−5) confirmed the significance of these results. Qualitative comparisons also showed a near-exact alignment with ground truth masks, particularly in areas such as the ventricles and gray–white matter interfaces. Volumetric validation further demonstrated a high correlation (R2 = 0.90) between the predicted and ground truth brain volumes. Despite the limited annotated data, the GA-MS-UNet++ maintained a strong performance and has the potential for clinical use. This algorithm represents the first publicly available segmentation model for 9.4T imaging, providing a powerful tool for high-resolution brain segmentation and driving progress in automated neuroimaging analysis....
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