Background: Aetiological mechanisms of demyelinating diseases underlie a very heterogeneous clinical course, ranging from relapsing to progressive, with a longer disease duration leading to physical and cognitive disability. Aim: To identify factors in the development of long-term disability in demyelinating diseases. Materials and Methods: A cross-sectional study was conducted among 64 patients with multiple sclerosis and 33 patients with NMO treated at the Third State Central Hospital. We assessed demographic and clinical data, including the Expanded Disability Status Scale (EDSS). Statistical analysis included descriptive statistics, t-tests, chi-square tests, analysis of variance, correlation, and regression. Result: In MS, relapses (≥1) within 1.0 years of MS onset (OR=5.5, 95% CI 1.11-26.69, p=0.037), optic neuritis (OR=4.3, 95% CI 1.02-21.67, p=0.014), relapse frequency (≥3) (OR=6.6, 95% CI 1.39-31.3, p=0.018), myelitis frequency (≥2) (OR=5.5, 95% CI 1.42-21.67, p=0.014), in NMOSD, time from disease onset to diagnosis (≥3 month) (OR=7.9, 95% CI 1.04-60.7, p=0.046), years of diagnosis (≥3 years) (OR=6.3, 95% CI 1.15-34.12, p=0.034) contributed to disabilty worsening outcomes. Conclusion Disability in demyelinating diseases is affected by many factors.

Background: Multiple sclerosis (MS) is an autoimmune inflammatory neurodegenerative disease of the central nervous system and a major cause of disability in young adults. MRI is essential for diagnosis and monitoring, while early evaluation of structural brain changes is important. Aim: To evaluate brain volumetric changes using automated MRI analysis in patients with MS. Materials and Methods: This case–control study included 50 MS case patients and 50 controls patients examined between 2020–2023 at the Third State Hospital. T1-MPRAGE images were analyzed using volBrain automated segmentation software. Result: The mean age of MS patients was 45.5±11.8 years. Compared with healthy controls, the MS group showed significantly lower total grey matter volume (p=0.002), cortical grey matter volume (p<0.001), subcortical grey matter volume (p=0.006), and cerebral volume (p=0.02). Significant volume reductions were also observed in the temporal (p=0.006) and occipital (p<0.001) lobes. Deep grey matter structures, including the thalamus (p=0.001), caudate nucleus (p=0.009), putamen (p=0.008), and nucleus accumbens (p<0.001), also showed significantly reduced volumes. Disease duration was negatively correlated with total grey matter volume (r=−0.30, p=0.02), thalamic volume (r=−0.28, p=0.04), and occipital lobe volume (r=−0.43, p=0.002). Disease duration was also significantly associated with spinal cord involvement (p=0.01) and grey matter volume (p=0.04). The number of relapses was negatively correlated with total brain volume (r=−0.28, p=0.046). Conclusion Patients with MS demonstrated decreased grey matter volume, particularly within deep grey matter structures, along with increased abnormal white matter volume, suggesting the coexistence of neurodegeneration and demyelination in MS. Brain volume loss in MS appeared to occur independently of aging. Automated quantitative MRI analysis may provide important diagnostic value in detecting structural brain changes associated with MS.

Background: Although breast density is an important determinant of breast cancer risk and the likelihood of masking pathological lesions on mammography, it is insufficient to fully characterize the breast imaging phenotype on its own. Age, body mass index (BMI), total body fat percentage, breast size, compressed breast thickness, tissue distribution, and reproductive history influence not only the biological composition of the breast but also the technical conditions of mammographic imaging. Aim: To synthesize the evidence on how age, BMI, body composition, breast-specific anthropometric characteristics, reproductive factors, and population-specific characteristics influence mammographic imaging. Materials and Methods: In this review, we synthesized evidence from studies indexed in PubMed/MEDLINE, Scopus, Embase, and Web of Science from 2000 through August 2026, focusing on the associations of these factors with mammographic appearance, lesion conspicuity, BI-RADS classification, image quality, radiation dose, and screening performance. Result: Breast density generally decreases with advancing age and during the menopausal transition. Increasing BMI and adipose tissue volume are also associated with lower breast tissue density. Therefore, the absolute volume of fibroglandular and connective tissue should not be inferred directly from percent breast density alone. Breast-specific parameters, particularly compressed breast thickness and breast volume, are clinically important because they influence positioning, compression, exposure parameters, and mean glandular dose. Studies in Asian cohorts have demonstrated significant associations between age, body size, and mammographic characteristics, although substantial inter-population differences have been reported. Advances in digital breast tomosynthesis (DBT) and artificial intelligence (AI) are enabling a transition from single-category breast density assessment toward multidimensional breast phenotyping. Future research should integrate DICOM technical parameters, standardized anthropometric measurements, quantitative breast density measures, and externally validated AI-derived features to improve personalized screening and radiation dose optimization. Conclusion Breast imaging phenotype should be considered a multidimensional construct integrating breast density with age, body composition, breast spatial architecture, imaging parameters, and validated AI-derived features. This approach may improve biological interpretation, radiation dose estimation, and risk stratification while avoiding the oversimplification that a single breast-density percentage or BI-RADS density category can fully characterize the breast phenotype.

Background: Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder associated with cognitive impairment and structural brain alterations. White matter hyperintensities (WMH) detected on magnetic resonance imaging (MRI) serve as critical neuroimaging markers of cerebral small vessel disease. However, research evaluating the WMH burden in the Mongolian population with T2DM using AI-based automated quantitative methods and comparing these findings with a control group remains limited. This research gap provides the rationale and necessity for the current study. Aim: To evaluate and compare the burden of brain white matter hyperintensities (WMH) in adults with type 2 diabetes mellitus (T2DM) versus a control group. Material and Methods: This analytical cross-sectional, retrospective comparative study included participants aged 40 and older who underwent brain MRI at the Mongolia-Japan Hospital of MNUMS between 2019 and 2025. The T2DM group consisted of patients with a confirmed diagnosis, while the control group included individuals without diabetes or prediabetes. After excluding cases with major structural abnormalities, poor image quality, failed automated processing, or incomplete clinical data, 257 out of an initial 283 participants were included in the final analysis (T2DM: 71, Control: 186). 3D T1-weighted MPRAGE and T2-FLAIR MRI sequences were processed using Neurophet AQUA software (version 3.1) to assess total, deep, and periventricular Fazekas scores. Age- and sex-adjusted ordinal logistic regression analysis was performed using SPSS 25.0, with p<0.05 considered statistically significant. Results: A total of 257 participants (T2DM=71, Control=186) were analyzed. There were significant differences between groups in sex distribution (p=0.046) and age categories (p<0.001). The T2DM group exhibited significantly higher total and deep Fazekas scores compared to the control group (total Fazekas: aOR 10.81, 95% CI 5.20-22.49; deep Fazekas: aOR 8.21, 95% CI 4.32-15.60; both p<0.001). Increasing age was associated with higher total (aOR 1.07, 95% CI 1.03-1.11, p=0.001) and deep (aOR 1.04, 95% CI 1.00-1.08, p=0.037) Fazekas scores. Additionally, female sex was associated with higher total (aOR 1.95, 95% CI 1.04–3.63, p=0.036) and deep (aOR 1.95, 95% CI 1.08–3.51, p=0.026) Fazekas score categories. Conclusion Assessment of WMH burden using AI-based automated quantitative MRI processing revealed that T2DM is significantly associated with higher total and deep Fazekas scores. This association remained significant after adjusting for age and sex. These findings suggest that AI-based MRI processing is a suitable method for the objective, quantitative, and standardized assessment of brain WMH burden associated with T2DM.

Background: The human head is an integrated biological structure composed of the brain, cranial bones, facial structures, neuromuscular systems, and sensory organs, all interconnected through coordinated signaling. The relationship between brain development, function, and craniofacial growth is most evident after birth, during which the head increases approximately fourfold in size before adulthood. Brain morphology and volume are associated not only with neurological disorders but also with cognitive and behavioral characteristics. For example, children with holoprosencephaly often present with varying degrees of facial anomalies, while studies have shown that adolescents with malocclusion exhibit higher levels of anxiety, depression, and psychological stress. With the increasing availability of magnetic resonance imaging (MRI), it has become an essential diagnostic tool in clinical practice. MRI is particularly advantageous in pediatric populations due to the absence of ionizing radiation and its superior soft tissue contrast. Recent advances in image processing techniques have enabled comprehensive analysis of both brain structures and craniofacial morphology from a single MRI dataset, forming the basis of this study. Aim: To investigate the relationship between brain tissue volumes and maxillary hypoplasia using MRI-based voxelbased morphometry in children with neurological conditions. Materials and Methods: This study was conducted at the School of Biomedicine, Mongolian National University of Medical Sciences, in collaboration with Chifeng University, as part of the project titled “Investigation of the relationship between craniofacial anomalies and brain disorders using MRI”, funded by the Ministry of Education and Science. Voxel-based morphometry (VBM) analysis was performed on brain MRI scans of children aged 9–11 years. Results: Among the 25 children included in the study, 17 (68%) were male. The youngest average age was observed in the maxillary hypoplasia group (8.4±0.95 years). The signal-to-noise ratio (SNR) of MRI was lowest in the non-temporal epilepsy group (41.1±1.35) and higher in the maxillary hypoplasia group (49.6±8.22). Both epilepsy and maxillary hypoplasia groups demonstrated physiological asymmetry of the cerebral hemispheres, with left parietal lobe dominance (−0.1% and −0.3 cm, respectively). Right frontal lobe dominance (+1.1%) was observed only in the epilepsy group. Conclusion Children aged 9–11 years with maxillary hypoplasia showed a reduction in both gray and white matter volumes of the brain. These findings highlight the importance of early diagnosis of neurodevelopmental disorders affecting odontogenesis and emphasize the need for a multidisciplinary team-based management approach.

Background: Although breast cancer incidence is rising in Mongolia, little is known about how mammographic findings and BI-RADS final assessment categories are recorded in routine clinical practice. Aim: To describe report-derived finding categories and BI-RADS final assessments in a single-centre mammography registry, and to examine the association of age with BI-RADS ≥3. Materials and Methods: In this retrospective analytical cross-sectional analysis of 773 mammographic examination records, the earliest examination per participant was retained, yielding 733 women aged 20–74 years. Free-text findings were assigned to mutually exclusive categories using prespecified hierarchical keyword rules, and BI-RADS categories 3 and 4 were combined as “BI-RADS ≥3”. Group differences were examined with Pearson’s χ² test and the age association with logistic regression; a body mass index (BMI)-adjusted model was fitted in 258 women with recorded height and weight. Results: Mean age was 44.2±10.8 years. The most frequent report categories were no lesion (28.4%), calcification (20.1%), density-related descriptors (19.8%) and other benign/structural findings (16.0%). BI-RADS categories 1, 2, 3 and 4 were recorded in 312 (42.6%), 342 (46.7%), 65 (8.9%) and 14 (1.9%) women, respectively, and BI-RADS ≥3 in 79 (10.8%; 95% CI 8.7–13.2). This proportion differed across age groups (χ²=12.72; P=0.026). Each additional decade of age was associated with higher odds of BI-RADS ≥3 (OR 1.44; 95% CI 1.16–1.79; P=0.001), and the association persisted after BMI adjustment (OR 1.62; 95% CI 1.10–2.36; P=0.013). BMI was not associated with BI-RADS ≥3. Conclusions 1. Among the 733 women examined at a single hospital, the most common mammographic findings were no significant abnormalities (28.4%), calcifications (20.1%), density-related findings (19.8%), and other benign or structural changes (16.0%). 2. Of the final assessments, 89.2% were classified as BI-RADS 1–2, while 10.8% were classified as BI-RADS 3–4. No cases were classified as BI-RADS 0 or BI-RADS 5. 3. For every 10-year increase in age, the odds of receiving a BI-RADS score of ≥3 increased by a factor of 1.44. This association remained independent of body mass index (BMI).

Background: Chest radiography is a fundamental imaging modality used to diagnose acute and chronic pulmonary diseases. It is relatively inexpensive, rapid, and can be performed at the bedside. Pulmonary abnormalities identified on chest radiographs are generally classified into two main categories: diffuse and focal. Depending on lesion size and image quality, abnormalities can be detected with an estimated accuracy of 70–85%. However, radiologists may overlook findings due to several factors, including heavy workloads, emergency conditions, and the presence of small focal lesions measuring less than 1 cm. Previous studies evaluating the application of artificial intelligence (AI) to chest radiographs for detecting lung cancer, COVID-19, and pneumonia have reported that the sensitivity of diagnostic interpretations improves from 88% to 95% when radiologists and AI work together. Aim: To evaluate selected thoracic abnormalities on chest radiographs using artificial intelligence and compare the results with radiologists’ interpretations. Materials and Methods: This retrospective study was conducted in the Department of Radiology at the Mongolia–Japan Hospital of the Mongolian National University of Medical Sciences. A total of 845 chest radiographs obtained between September 10 and September 30, 2022, were reviewed. After applying the exclusion criteria, 721 chest radiographs were included. The interpretations provided by four radiologists were compared with the results generated by an AI system. Results: A total of 721 posteroanterior chest radiographs were included in the study, comprising 377 images from male patients and 344 from female patients. By age group, the images included 24 from patients aged 10–19 years, 114 from those aged 20–29 years, 150 from those aged 30–39 years, 147 from those aged 40–49 years, 97 from those aged 50–59 years, 96 from those aged 60–69 years, 58 from those aged 70–79 years, and 37 from those aged 80–89 years. Radiologists identified abnormalities in 331 (46%) radiographs and reported no abnormalities in 390 (54%). In comparison, the AI system classified 392 (54%) radiographs as abnormal and 329 (46%) as normal. Using the radiologists’ interpretations as the reference standard, the AI system demonstrated a specificity of 95.8% and a sensitivity of 89% for detecting abnormalities on chest radiographs. Conclusion The AI system demonstrated high sensitivity and specificity in detecting abnormalities on chest radiographs, suggesting its potential utility in clinical practice. Its application may be particularly beneficial in supporting the interpretation of chest radiographs used for preventive screening and early detection of pulmonary abnormalities.

Background: The Global Cortical Atrophy (GCA) scale is widely used for visual assessment of cortical atrophy on MRI but is subject to interobserver variability. Automated brain volumetry provides objective measurements; however, its association with visual atrophy grading remains understudied in Mongolia. Aim: To assess the association and agreement between radiology report–based atrophy grades and AI-derived whole-brain volume, and to compare brain volumes in adults with and without type 2 diabetes mellitus (T2DM). Materials and Methods: This retrospective, single-center study included 367 adults aged ≥40 years (T2DM, n=144; controls, n=223). Intracranial volume–adjusted whole-brain volumes were obtained from 3D T1-weighted MRI using Neurophet AQUA v3.1. Atrophy severity recorded in routine radiology reports was coded from 0 to 3. Welch’s ANOVA assessed differences across grades, and ROC analysis evaluated discrimination between adjacent grades. Classification agreement was assessed using stratified five-fold cross-validation and Cohen’s κ. Age- and sex-adjusted ANCOVA compared brain volumes between groups. Results: Mean whole-brain volume decreased across atrophy grades 0–3 (1122.12, 1068.50, 1034.76, and 968.33 mL; Welch’s F(3, 60.04)=207.30; P<0.001). ROC AUCs for adjacent grades were 0.892, 0.897, and 1.000, with corresponding thresholds of 1099.86, 1056.17, and 995.57 mL. Five-fold cross-validation yielded 78.7% exact agreement (289/367), unweighted κ=0.684, and quadratic-weighted κ=0.856. Adjusted whole-brain volume was 11.98 mL lower in T2DM than in controls (95% CI, −22.67 to −1.30; P=0.028), whereas the overall association between T2DM and atrophy grade was not statistically significant (P=0.059). Conclusion Higher report-based atrophy grades were associated with lower whole-brain volumes, with strong quadratic-weighted agreement between volumetric and report-based classifications. Adults with T2DM had lower adjusted brain volumes than controls. Automated volumetry may complement radiologists’ assessments by providing objective quantitative information.

Background: Multidrug-induced long QT syndrome is a serious heart rhythm disorder where multiple medications prolong the QT interval on an electrocardiogram, increasing the risk of a potentially fatal arrhythmia. Long QT syndrome is characterised by heart rate corrected QT interval prolongation and life-threatening arrhythmias, such as polymorphic ventricular tachycardia, Torsades de pointes leading to syncope and sudden death. Case: 21-year-old woman required resuscitation from an apparent cardiac arrest that had occurred after status epilepticus. The woman was diagnosed with long QT syndrome, cardiac channelopathy. She had a long history of syncope, seizure and palpitations. Family history was non-contributory. Home medications included levetiracetam 500 milligrams orally twice and fluoxetine 20 milligrams orally once a day which the patient reported non-compliance with. Levetiracetam is a widely used anti-epileptic medication secondary to its favorable safety profile. To our knowledge, there are few other case reports documenting torsades de pointes after levetiracetam administration, and specifically our case report will be the first documenting cardiac arrest after multidrug administration. Long QT syndrome is not often included in the differential diagnosis of epileptic and non-epileptic seizures. Early recognition of the syndrome is very important because of prognostic and therapeutic consequences. Conclusion Although no hereditary cause was confirmed, this case represents Torsades de Pointes triggered by multiple medications—including long-term low-dose carbamazepine, fluoxetine, diazepam, and high-dose levetiracetam—on a background of probable Romano–Ward syndrome or predisposition to QT prolongation. The arrhythmia progressed to cardiac arrest, requiring ICD implantation.

Background Ischemic stroke or cerebral infarction in young adults (20-50 years) is relatively frequent, accounting for more than 10%-26% of all first strokes and its incidence rises steeply with age. Causes of “Young stroke” are heterogeneous and while it generally has a good prognosis, it has a significant socioeconomic impact, including functional deficits and financial costs. The most frequent causes of cerebral infarction in young adults are cardio-embolism, hypertension, premature atherosclerosis, migraine, smoking and hypercoagulable states. Objective The aim of this study was to compare characteristics of cerebral infarction between young (20-49 years) and old (50-89 years) patients undergoing investigations and treatment according to one common protocol in the tertiary hospital. Methods This Descriptive case series study was conducted in Department of Neurology of First State Central Hospital from October 2014 to July 2016. During this study, we observed 220 patients with first-onset of cerebral infarction from which 90 young patients (under 50 years) and 130 old one (above 50 years), based on prospective study. Data regarding the etiology and risk factors of the stroke, clinical manifestations, and diagnostic test results of patients were examined during their hospital treatment as well as a NIHSS, modified Rankin Scale scores, and Barthel Index at admission and discharge, also at 21 day. Stroke subtyping was conducted in accordance with the Trial of Org 10172 in Acute Stroke Treatment (TOAST) criteria. Results In total, 220 patients with cerebral infarction were included, from which 90 (40.9%) were 20-50 years and 130 (59.1%) were 50-89 years. The proportion of males was higher among both groups: 61.1% vs. 70.0%. Common causes for cerebral infarction in the young patients were current smoking (53.3.1% vs. 37.8%), long-term alcohol consumption (51.1 vs. 12.3), cardiac embolism (36.0% vs. 16.2%), migraine with aura (25.5% vs. 12.2%), infective diseases (15.6% vs. 0.8%), and oral contraceptive use (14.4 vs. 0%). Leading causes for ischemic stroke in old patients were the conventional risk factors such as hypertension (75.4% vs. 38.9%), atherosclerosis (66.9% vs. 31.1), coronary artery disease (24.3% vs. 12%), and diabetes mellitus (26.9% vs. 6.7%). Most of the young stroke patients demonstrated good functional outcomes, at the time of discharge (71.1% vs. 60%) and three weeks (86.6% vs. 66.2%) patients had Rankin Scale scores in the range of 0-2. Conclusions There are significant differences between young and old patients with cerebral infarction regarding to risk factors, etiological subtypes and improvement of functional deficits associated with the stroke. However, severity of stroke on admittance is similar but three weeks outcome is different among young and old patients with relatively rapid improvement of functional deficit in young stroke patients than old one.