• Journal of the Korean Society of Radiology
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• v.84 no.3
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• pp.638-652
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• 2023

Purpose To investigate the MRI markers for the prediction of amyloid β (Aβ)-positivity in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and to evaluate the differences in MRI markers between Aβ-positive (Aβ [+]) and -negative groups using the machine learning (ML) method. Materials and Methods This study included 139 patients with MCI and AD who underwent amyloid PET-CT and brain MRI. Patients were divided into Aβ (+) (n = 84) and Aβ-negative (n = 55) groups. Visual analysis was performed with the Fazekas scale of white matter hyperintensity (WMH) and cerebral microbleeds (CMB) scores. The WMH volume and regional brain volume were quantitatively measured. The multivariable logistic regression and ML using support vector machine, and logistic regression were used to identify the best MRI predictors of Aβ-positivity. Results The Fazekas scale of WMH (p = 0.02) and CMB scores (p = 0.04) were higher in Aβ (+). The volumes of hippocampus, entorhinal cortex, and precuneus were smaller in Aβ (+) (p < 0.05). The third ventricle volume was larger in Aβ (+) (p = 0.002). The logistic regression of ML showed a good accuracy (81.1%) with mini-mental state examination (MMSE) and regional brain volumes. Conclusion The application of ML using the MMSE, third ventricle, and hippocampal volume is helpful in predicting Aβ-positivity with a good accuracy.

• Journal of Preventive Medicine and Public Health
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• v.32 no.1
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• pp.48-59
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• 1999

Objectives. This study was conducted to evaluate the association between upper extremity musculoskeletal symptoms and Rapid Upper Limb Assessment(RULA) in vehicle assembly line workers. The goal of this study is to show the feasibility of RULA as a checklist for work related musculoskeletal symptoms (WMSDs) in Korean workers. Methods. The total number of 199 people from the department of assembly and 115 people from the department of Quality Control(QC) in automotive plant were subjects for this cross sectional study. A standard symptom questionnaire survey has been used for the individual characteristics, work history, musculosketal symptoms and non-occupational covariates. The data were obtained by applying one-on-one interview for the all subjects. RULA has been applied for ergonomic work posture analysis and the primary ergonomic risk sure was computed by RULA method. Association between upper extremity musculoskeletal symptoms and RULA were assessed by multiple logistic regression analysis. Results. A total of 314 workers was examined. The prevalence of musculoskeletal symptoms by NIOSH case definition was 62.4%. The distribution of musculoskeletal symptoms by the part of the body turned out to be following; back:41.4%, neck: 32.8%, shoulder: 26.4%, arm: 10.5% and hand:29.3%. The relationship of the individual RULA scores were statistically significant for the prevalence of musculoskeletal symptoms. As the result of the multiple logistic regressioin analysis, grand final score (OR=2.250 95% CI: 1.402-3.612) was associated with musculoskeletal symptoms in any part of the body.; upper arm score(OR=1.786 95% CI: 1.036-3.079) and posture score A(OR=1.634 95% CI: 1.016-2.626) in neck; muscel use score(OR=3.076 95% CI:1.782-5.310) and posture score A(OR=1.798 95% CI: 1.072-3.017) in shoulder; upper arm score(OR=1.715 95% CI: 1.083-2.715) and muscel use score(OR=2.057 95% CI:1.303-3.248) in neck & shoulder; muscle use score(OR=10.662 95% CI: 3.180-35.742) in arm; writst/wist score(OR=2.068 95% CI: 1.130-3.786) and muscle use score(OR=2.215 95% CI: 1.284-3.819) in hand & wrist.; muscle use score of trunk (OR=2.601 95% CI: 1.147-5.901) in back. Conclusions. Musculoskeletal symptoms of the extremities were strongly associated with individual RULA body score. These results show that RULA can be used as a useful assessment tool for the evaluation of musculoskeletal loading which is known to contribute to work-related musculoskeletal disorders. RULA also can be used as a screening tool or incorporated into a wider ergonomic assessment of epidemiological, physical, mental, environmental and organizational factors. As shown in this study, complement of the analysis system for the other risk factors and characterizing between the upper limb and back part will be needed for future work.