Use of a Deep-Learning Algorithm to Guide Novices in Performing Focused Assessment With Sonography in Trauma.

This quality improvement study compares the diagnostic quality and completion time between ultrasonography operators guided by artificial intelligence vs those without such assistance.

Deep Learning Assisted Detection of Abdominal Free Fluid in Morison's Pouch During Focused Assessment With Sonography in Trauma.

Background: The use of focused assessment with sonography in trauma (FAST) enables clinicians to rapidly screen for injury at the bedsides of patients. Pre-hospital FAST improves diagnostic accuracy and streamlines patient care, leading to dispositions to appropriate treatment centers. In this study, we determine the accuracy of artificial intelligence model-assisted free-fluid detection in FAST examinations, and subsequently establish an automated feedback system, which can help inexperienced sonographers improve their interpretation ability and image acquisition skills. Methods: This is a single-center study of patients admitted to the emergency room from January 2020 to March 2021. We collected 324 patient records for the training model, 36 patient records for validation, and another 36 patient records for testing. We balanced positive and negative Morison's pouch free-fluid detection groups in a 1:1 ratio. The deep learning (DL) model Residual Networks 50-Version 2 (ResNet50-V2) was used for training and validation. Results: The accuracy, sensitivity, and specificity of the model performance for ascites prediction were 0.961, 0.976, and 0.947, respectively, in the validation set and 0.967, 0.985, and 0.913, respectively, in the test set. Regarding feedback prediction, the model correctly classified qualified and non-qualified images with an accuracy of 0.941 in both the validation and test sets. Conclusions: The DL algorithm in ResNet50-V2 is able to detect free fluid in Morison's pouch with high accuracy. The automated feedback and instruction system could help inexperienced sonographers improve their interpretation ability and image acquisition skills.

Treatment with normobaric or hyperbaric oxygen and its effect on neuropsychometric dysfunction after carbon monoxide poisoning: A systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: Carbon monoxide (CO) poisoning may result in acute neurological sequelae, cognitive sequelae, and delay neurological sequelae. The administration of hyperbaric oxygen (HBO) to prevent the development of delayed neurological sequelae in CO poisoning have extensively investigated but conflicting results have been reported. We performed a systematic literature review and meta-analysis of randomized controlled trials (RCTs) evaluating HBO treatment and its effect on neuropsychometric dysfunction after CO poisoning. METHODS: We searched Medline, Embase, Pubmed, and the Cochrane Register of Controlled Trials from inception to December 2017. Eligible studies compared HBO therapy with normobaric oxygen (NBO) in patients with CO poisoning. RESULTS: Six studies compared HBO with NBO in CO poisoning patients. Compared with patients treated with NBO, a lower percentage of patients treated with HBO reported headache (16.2% vs 16.5%, relative risk [RR] = 0.83, 95% CI = 0.38-1.80), memory impairment (18.2% vs 23.8%, RR = 0.80, 95% CI = 0.43-1.49), difficulty concentrating (15.0% vs 18.4%, RR = 0.86, 95% CI = 0.55-1.34), and disturbed sleep (14.7% vs 16.2%, RR = 0.91, 95% CI = 0.59-1.39). Two sessions of HBO treatment exhibited no advantage over one session. CONCLUSIONS: The meta-analysis indicated that compared with CO poisoning patients treated with NBO, HBO treated patients have a lower incidence of neuropsychological sequelae, including headache, memory impairment, difficulty concentrating, disturbed sleep, and delayed neurological sequelae. Taking into consideration the cost-effectiveness of one session of HBO, one session of HBO treatment could be an economical option for patients with CO poisoning with high severity.

Quantitative comorbidity risk assessment of dementia in Taiwan: A population-based cohort study.

Dementia is one of the most burdensome illnesses in elderly populations worldwide. However, the literature about multiple risk factors for dementia is scant.To develop a simple, rapid, and appropriate predictive tool for the clinical quantitative assessment of multiple risk factors for dementia.A population-based cohort study.Based on the Taiwan National Health Insurance Research Database, participants first diagnosed with dementia from 2000 to 2009 and aged ≥65 years in 2000 were included.A logistic regression model with Bayesian supervised learning inference was implemented to evaluate the quantitative effects of 1- to 6-comorbidity risk factors for dementia in the elderly Taiwanese population: depression, vascular disease, severe head injury, hearing loss, diabetes mellitus (DM), and senile cataract, identified from a nationwide longitudinal population-based database.This study enrolled 4749 (9.5%) patients first diagnosed as having dementia. Aged, female, urban residence, and low income were found as independent sociodemographic risk factors for dementia. Among all odds ratios (ORs) of 2-comorbidity risk factors for dementia, comorbid depression and vascular disease had the highest adjusted OR of 6.726. The 5-comorbidity risk factors, namely depression, vascular disease, severe head injury, hearing loss, and DM, exhibited the highest OR of 8.767. Overall, the quantitative effects of 2 to 6 comorbidities and age difference on dementia gradually increased; hence, their ORs were less than additive. These results indicate that depression is a key comorbidity risk factor for dementia.The present findings suggest that physicians should pay more attention to the role of depression in dementia development. Depression is a key cormorbidity risk factor for dementia. It is the urgency of evaluating the nature of the link between depression and dementia; and further testing what extent controlling depression could effectively lead to the prevention of dementia.

The association of socioeconomic status and access to low-volume service providers in breast cancer.

BACKGROUND: No large-scale study has explored the combined effect of patients' individual and neighborhood socioeconomic status (SES) on their access to a low-volume provider for breast cancer surgery. The purpose of this study was to explore under a nationwide universal health insurance system whether breast cancer patients from a lower individual and neighborhood SES are disproportionately receiving breast cancer surgery from low-volume providers. METHODS: 5,750 patients who underwent breast cancer surgery in 2006 were identified from the Taiwan National Health Insurance Research Database. The Cox proportional hazards model was used to compare the access to a low-volume provider between the different individual and neighborhood SES groups after adjusting for possible confounding and risk factors. Hosmer-Lemeshow goodness-of-fit statistic was used to determine how well the model fit the data. RESULTS: Univariate analysis data shows that patients in disadvantaged neighborhood were more likely to receive breast cancer surgery at low-volume hospitals; and lower-SES patients were more likely to receive surgery from low-volume surgeons. In multivariate analysis, after adjusting for patient characteristics, the odds ratios of moderate- and low-SES patients in disadvantaged neighborhood receiving surgery at low-volume hospitals was 1.47 (95% confidence interval=1.19-1.81) and 1.31 (95% confidence interval=1.05-1.64) respectively compared with high-SES patients in advantaged neighborhood. Moderate- and low-SES patients from either advantaged or disadvantaged neighborhood had an odds ratios ranging from 1.51 to 1.80 (p<0.001) to receiving surgery from low-volume surgeons. In Hosmer-Lemeshow goodness-of-fit test, p>0.05 that shows the model has a good fit. CONCLUSIONS: In this population-based cross-sectional study, even under a nationwide universal health insurance system, disparities in access to healthcare existed. Breast cancer patients from a lower individual and neighborhood SES are more likely to receive breast cancer surgery from low-volume providers. The authorities and public health policies should keep focusing on these vulnerable groups.