The advancement of stethoscope technology has enabled high quality recording of patient sounds. We used an electronic stethoscope to record lung sounds from healthy and unhealthy subjects. The dataset includes sounds from seven ailments (i.e., asthma, heart failure, pneumonia, bronchitis, pleural effusion, lung fibrosis, and chronic obstructive pulmonary disease (COPD)) as well as normal breathing sounds. The dataset presented in this article contains the audio recordings from the examination of the chest wall at various vantage points. The stethoscope placement on the subject was determined by the specialist physician performing the diagnosis. Each recording was replicated three times corresponding to various frequency filters that emphasize certain bodily sounds. The dataset can be used for the development of automated methods that detect pulmonary diseases from lung sounds or identify the correct type of lung sound. The same methods can also be applied to the study of heart sounds.

Sleep quality is of paramount importance for human health. This multi-site study measures the proportion and types of self-reported sleep disorders in medical students and evaluates their association with academic performance by Grade Point Average (GPA). A cross-sectional survey was conducted on medical students from two medical schools in Jordan during the 2018/2019 academic year. The study utilized the SLEEP-50 questionnaire to estimate the proportion of several sleep disorders and their effects on daily functioning. Below average GPAs were considered poor academic performance. 1041 medical students' online surveys were analyzed from two medical schools’ campuses, representing a 29.7% response rate. Their mean age was 22 ± 2.1 years (ranging from 18 to 37) and 52.6% were female. The mean body mass index was 24.2 ± 4.4 kg/m2. According to the SLEEP-50 questionnaire, the prevalence of sleep disorders among studied medical students ranged from 0.6% for sleep state misperception (SSM) to 23.1% for hypersomnia. Using binary logistic regression, after adjusting for gender and obesity, poor academic performance was associated with a risk for insomnia [adjusted odds ratio (OR) = 1.96, p < 0.001]; affective disorder [OR = 2.24, P < 0.001]; SSM [OR = 6.40, p = 0.045]; narcolepsy [OR = 9.54, p = 0.045]; and circadian rhythm disorders [OR = 2.03, p < 0.001]. Sleep disorders are common among medical students. Several sleep disorders were associated with poor academic performance. Proper diagnosis and treatment of sleep disorders may remedy this issue.

The COVID-19 global pandemic has wreaked havoc on every aspect of our lives. More specifically, healthcare systems were greatly stretched to their limits and beyond. Advances in artificial intelligence have enabled the implementation of sophisticated applications that can meet clinical accuracy requirements. In this study, customized and pre-trained deep learning models based on convolutional neural networks were used to detect pneumonia caused by COVID-19 respiratory complications. Chest X-ray images from 368 confirmed COVID-19 patients were collected locally. In addition, data from three publicly available datasets were used. The performance was evaluated in four ways. First, the public dataset was used for training and testing. Second, data from the local and public sources were combined and used to train and test the models. Third, the public dataset was used to train the model and the local data were used for testing only. This approach adds greater credibility to the detection models and tests their ability to generalize to new data without overfitting the model to specific samples. Fourth, the combined data were used for training and the local dataset was used for testing. The results show a high detection accuracy of 98.7% with the combined dataset, and most models handled new data with an insignificant drop in accuracy.

Al-Mistarehi AH, et al. Clin Exp Vaccine Res. 2021 May;10(2):171-182. https://doi.org/10.7774/cevr.2021.10.2.171