Browsing by Author "Wongsripuemtet J."

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    Cognitive performance in patients with neuromyelitis optica: clinical and imaging characteristics
    (2024-12-01) Apiraksattayakul N.; Lerttanatum C.; Maethasith I.; Wongsripuemtet J.; Siritho S.; Jitprapaikulsarn J.; Ongphichetmetha T.; Prayoonwiwat N.; Senanarong V.; Rattanabannakit C.; Apiraksattayakul N.; Mahidol University
    This study aimed to identify the prevalence, clinical and radiographic characteristics, and risk factors for cognitive dysfunction in patients with Neuromyelitis optica spectrum disorder (NMOSD). Eighty-three participants who were diagnosed with NMOSD were recruited. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) and Frontal Assessment Battery (FAB). The mean age of the patients was 47.78 ± 13.14 years, with an average of 12.05 ± 4.62 years of formal education. The majority (54%) exhibited cognitive impairment, defined by a MoCA score < 25 (mean: 22.96 ± 3.82). Disease severity (evaluated by the Expanded Disability Status Scale) and lower formal education levels were associated with cognitive impairment (p = 0.011 and < 0.001, respectively). The annualized relapse rate, disease duration, and AQP4 antibody status were not associated with cognitive impairment. Interestingly, informant-reported cognitive decline was associated with poorer cognitive performance (p = 0.027). Radiographic findings of lesion location and severity were associated with MoCA-assessed cognitive performance, particularly for lesions in the right parietal lobes (p = 0.023). Hippocampal atrophy was negatively correlated with FAB scores. In conclusion, approximately half of the Thai patients with NMOSD exhibited cognitive impairment, which was associated with age, formal education level, disease severity, relative perception, and specific radiological findings. Further studies incorporating comprehensive neuropsychological tests and subjective cognitive complaints are warranted.
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    Normal Pressure Hydrocephalus Classification using Weakly-Supervised Local Feature Extraction
    (2025-09-01) Supratak A.; Kingchan S.; Angkoondittaphong P.; Nava-apisak P.; Wongsripuemtet J.; Noraset T.; Kusakunniran W.; Haddawy P.; Songsaeng D.; Supratak A.; Mahidol University
    Normal Pressure Hydrocephalus (NPH) presents diagnostic challenges because its symptoms often overlap with other neurological conditions. A key radiological NPH indicator is ventricular cerebrospinal fluid (CSF) volume, assessed by neuroradiologists through brain imaging. However, expert availability is limited, highlighting the need for automated tools to assist in patient screening. While automated segmentation tools and pre-trained deep models have been used to estimate CSF volume for NPH diagnosis, they struggle to generalize to new datasets. This is partly due to their heavily reliance on prior knowledge, either through manually engineered features or the datasets used for pretraining segmentation models. Moreover, NPH classification models predominantly depend on global volume metrics, overlooking local CSF volume variations, which can result in suboptimal performance. In this paper, we introduce a new weak supervision method that can train a CSF segmentation model on a target dataset from scratch without needing costly segmentation annotations from experts. We also propose a local volumetric feature extraction algorithm that captures local differences in CSF volumes across different brain partitions along the axial orientation, providing richer information beyond the global metrics. Our weakly-supervised CSF segmentation model, combined with local volumetric features, was evaluated on non-contrast CT scans of 105 NPH and 112 non-NPH patients. The results show that our approach outperforms existing segmentation methods in NPH classification performance (ACC = 0.88, Sen = 0.97, Spec = 0.79, F1 = 0.89, AU-ROC = 0.91). Our model also demonstrates superior performance in screening patients at risk of having NPH compared to the visual-based evaluation of neuroradiologists.
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    Progression of carotid intima-media thickness, visceral fat accumulation and metabolic derangement in people living with HIV initiating antiretroviral therapy: A prospective cohort study at Thailand’s tertiary care center
    (2025-10-01) Boonsaen T.; Ratanasuwan W.; Tassaneetrithep B.; Thientunyakit T.; Wongsripuemtet J.; Thiravit S.; Phatharodom P.; Navanukroh O.; Homsanit M.; Boonsaen T.; Mahidol University
    Background Combination antiretroviral therapy (ART) has extended life expectancy for people with HIV, but long-term treatment is associated with adverse changes in body composition and cardiovascular risk. We evaluated 36-month changes in adiposity, metabolic parameters, and carotid intima-media thickness (cIMT) in Thai adults initiating ART. Methods A prospective cohort of 132 ART-naïve adults was followed for 36 months. Assessments at baseline, 12, 24, and 36 months included anthropometry; body composition by bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DEXA); metabolic and renal indices; and bilateral carotid ultrasound. Longitudinal changes were analyzed using non-parametric tests, and correlations were examined between cIMT and body composition or metabolic measures. Results Virological suppression exceeded 90% and CD4 counts improved steadily. However, notable adiposity changes were observed. Median BMI and waist circumference increased (both p < 0.01); total fat mass rose by 6.7%; visceral adipose tissue (VAT) increased by 33%; and the android/gynoid ratio exceeded 1.0 by 24 months, reflecting central fat redistribution. Fasting glucose increased (p < 0.05) while HOMA-β declined (p < 0.05), indicating early β-cell dysfunction; lipid profiles remained stable. Mean cIMT increased across arterial segments, most prominently at the carotid bifurcations (right: 0.644 mm at baseline to 0.729 mm at 36 months; + 0.085 mm; left: 0.675 mm at baseline to 0.756 mm at 36 months; + 0.081 mm both p < 0.01). Right bifurcation cIMT correlated positively with BMI, waist circumference, VAT, fasting glucose, and total cholesterol (all p < 0.05). Conclusions Despite durable viral suppression and immune recovery, long-term ART was associated with central fat accumulation and progressive cIMT thickening, particularly at the carotid bifurcations. These findings underscore the need for cardiometabolic risk monitoring as part of routine HIV care to identify early changes that precede overt disease.
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    The Diagnostic Accuracy of Artificial Intelligence in Radiological Markers of Normal-Pressure Hydrocephalus (NPH) on Non-Contrast CT Scans of the Brain
    (2023-09-01) Songsaeng D.; Nava-apisak P.; Wongsripuemtet J.; Kingchan S.; Angkoondittaphong P.; Phawaphutanon P.; Supratak A.; Mahidol University
    Diagnosing normal-pressure hydrocephalus (NPH) via non-contrast computed tomography (CT) brain scans is presently a formidable task due to the lack of universally agreed-upon standards for radiographic parameter measurement. A variety of radiological parameters, such as Evans’ index, narrow sulci at high parietal convexity, Sylvian fissures’ dilation, focally enlarged sulci, and more, are currently measured by radiologists. This study aimed to enhance NPH diagnosis by comparing the accuracy, sensitivity, specificity, and predictive values of radiological parameters, as evaluated by radiologists and AI methods, utilizing cerebrospinal fluid volumetry. Results revealed a sensitivity of 77.14% for radiologists and 99.05% for AI, with specificities of 98.21% and 57.14%, respectively, in diagnosing NPH. Radiologists demonstrated NPV, PPV, and an accuracy of 82.09%, 97.59%, and 88.02%, while AI reported 98.46%, 68.42%, and 77.42%, respectively. ROC curves exhibited an area under the curve of 0.954 for radiologists and 0.784 for AI, signifying the diagnostic index for NPH. In conclusion, although radiologists exhibited superior sensitivity, specificity, and accuracy in diagnosing NPH, AI served as an effective initial screening mechanism for potential NPH cases, potentially easing the radiologists’ burden. Given the ongoing AI advancements, it is plausible that AI could eventually match or exceed radiologists’ diagnostic prowess in identifying hydrocephalus.