Image recognition and neuroimaging are increasingly being used to understand the progression of Alzheimer’s disease (AD). However, image data from single-photon emission computed tomography (SPECT) are limited. Medical image analysis requires large, labeled training datasets. Therefore, studies have focused on overcoming this problem. In this study, the detection performance of five convolutional neural network (CNN) models (MobileNet V2 and NASNetMobile (lightweight models); VGG16, Inception V3, and ResNet (heavier weight models)) on medical images was compared to establish a classification model for epidemiological research. Brain scan image data were collected from 99 subjects, and 4711 images were used. Demographic data were compared using the chi-squared test and one-way analysis of variance with Bonferroni’s post hoc test. Accuracy and loss functions were used to evaluate the performance of CNN models. The cognitive abilities screening instrument and mini mental state exam scores of subjects with a clinical dementia rating (CDR) of 2 were considerably lower than those of subjects with a CDR of 1 or 0.5. This study analyzed the classification performance of various CNN models for medical images and proved the effectiveness of transfer learning in identifying the mild cognitive impairment, mild AD, and moderate AD scoring based on SPECT images.

Background: Delirium is an acute cognitive change characterized by behavioral and psychological features, such as visual and auditory hallucinations, sleep disturbances, and emotional confusion. It can lead to extended hospital stays, increased mortality risk, and higher nursing costs. In postoperative hip fracture patients, delirium results in a higher complication rate, poorer functional recovery, increased readmission rates, repeat surgeries, and elevated mortality. Despite these serious consequences, the literature provides limited information on the incidence of postoperative delirium following hip fracture surgeries in Asians. Additionally, there is a lack of long‐term, comprehensive nationwide population‐based studies, highlighting an important area for future research. This study aims to understand the incidence and risk factors of postoperative delirium in hip fracture patients using representative population data.Methods: We conducted a retrospective cohort study using the Taiwan National Health Insurance Research Database (NHIRD) from 2009 to 2020. The cohort consisted of 118,682 patients aged 65 years or older who were diagnosed with hip fractures. The delirium incidence was observed per 1000 person‐years. The Cox proportional hazards model was used to investigate the incidence of delirium among hip fracture patients.Results: The incidence of the first episode of delirium after hip surgery in the elderly was 1.87 events per 1000 PYs. Factors associated with delirium included being female (adjusted hazard ratio [aHR]: 0.59; 95% confidence interval [CI]: 0.53–0.64), age ≥ 95 years (aHR: 3.52; 95% CI: 2.74–4.51), comorbid dementia (aHR: 2.63; 95% CI: 2.38–2.92), and ICU stay 2–3 days (aHR: 2.85; 95% CI: 1.28–6.37). The occurrence of delirium was significantly associated with an ICU stay of ≥ 4 days, dementia, as well as 30‐day, 90‐day, and 1‐year mortality (p < 0.001).Conclusions: This study highlights the relatively low incidence of postoperative delirium in elderly hip fracture patients in Taiwan. Key risk factors identified include advanced age, female gender, comorbid dementia, and prolonged ICU stays. These findings underscore the need for targeted prevention and early intervention strategies to improve patient outcomes.

Environmental factors are crucial determinants of disability in schizophrenic patients. Using data from the 2014–2018 Certification of Disability and Care Needs dataset, we identified 3882 adult patients (46.78% females; age, 51.01 ± 13.9 years) with schizophrenia. We found that patients with severe schizophrenia had lower capacity and performance than those with moderate schizophrenia. The chances of having an access barrier to environmental chapter 1 (e1) products and technology in moderate schizophrenic patients and in severe schizophrenic patients were 29.5% and 37.8%, respectively. Logistic regression analyses demonstrated that the performance score was related to accessibility barriers in the categories described in e1, with adequate fitness of models in category e110 for personal consumption, e115 for personal usage in daily living activities, and e120 for personal outdoor and indoor mobility and transportation. Furthermore, the capacity-performance discrepancy was higher in moderate schizophrenic patients with accessibility barriers in the e110, e115, and e120 categories than that in moderate schizophrenic patients without accessibility barriers. However, severe schizophrenic patients with category e120 accessibility barriers were prone to a lower discrepancy, with institutional care a potentially decreasing factor. In conclusion, providing an e1 barrier-free environment is necessary for patients with schizophrenia to decrease their disability.

Given k (≥2) independent and normally distributed populations with unknown means and unknown (and possibly unequal) variances, we used the single-stage sampling procedure for multiple comparison with a control to the problem of unequal sample sizes. A simulation of the family-wise error type I was conducted to validate the quality of the procedure and a numerical example was provided for illustrating this procedure. We provided a user-friendly interface by R shiny to obtain the critical values for the practitioners.

To investigate the extent to which employment and productivity losses could be prevented by mitigating cervical spinal cord injury (SCI) and concurrent traumatic brain injury (TBI) in Taiwan. Using Taiwan's National Health Insurance Database, we identified individuals with cervical SCI and concurrent TBI, with the 7-year data collection period (2012-2018) and a general population cohort as the reference. Survival and employment status were extrapolated to lifetime and age using rolling extrapolation with restricted cubic spline regression and generalized linear models. This study included 2,852 patients with cervical SCI and 367 with concurrent TBI. In cervical SCI, loss-of-life expectancy, loss-of-lifetime employment duration (LED), and loss-of-lifetime insured salary (LIS; a proxy for productivity) were 19.3 years, 7.5 years, and $122,998, respectively. The relative loss-of-LED of motor-vehicle-related cervical SCI with concurrent TBI was significantly higher than that without TBI (72.7% vs. 64.2%). The relative loss-of-LED in middle-aged cervical SCI was significantly higher than loss-of-LIS (58.2% vs. 63.5%), suggesting that total productivity was more severely affected than working time. This 7-year nationwide cohort provides the first lifetime employment and productivity loss estimates for cervical SCI, demonstrating that concurrent TBI and younger age amplify economic burden. The findings shift the policy focus from generic SCI prevention to specific, high-yield interventions: mandatory TBI screening, age-tailored vocational programs, and productivity-targeted workplace accommodations. These evidence-based figures enable precise cost-benefit analyses for preventive measures (e.g. enhanced helmet legislation, road safety) and rehabilitation investments, offering actionable data for Taiwan's health and labor policymakers.