Evaluation of Intoxication Level with EOG Analysis and Machine Learning: A Study on Driving Simulator.

Driving after consuming alcohol can be dangerous, as it negatively affects judgement, reaction time, coordination, and decision-making abilities, increasing the risk of accidents and putting oneself and other road users in danger. Therefore, it is critical to establish reliable and accurate methods to detect and assess intoxication levels. One such approach is electrooculography (EOG), a non-invasive technique that measures eye movements, which has been linked to intoxication levels and holds promise as a method of estimating them. In recent years, machine learning algorithms have been utilized to analyze EOG signals to estimate various physiological and behavioural states. The purpose of this study was to investigate the viability of using EOG analysis and machine learning to estimate intoxication levels in a simulated driving scenario. EOG signals were measured using JINS MEME_R smart glasses and the level of intoxication was simulated using drunk vision goggles. We employed traditional signal processing techniques and feature engineering strategies. For classification, we used boosted decision trees, obtaining a prediction accuracy of over 94% for a four-class classification problem. Our results indicate that EOG analysis and machine learning can be utilized to accurately estimate intoxication levels in a simulated driving scenario.

Leukocyte deep learning classification assessment using Shapley additive explanations algorithm.

INTRODUCTION: A peripheral blood smear is a basic test for hematological disease diagnosis. This test is performed manually in many places worldwide, which requires both time and qualified staff. Large laboratories are equipped with digital morphology analyzers, some of which are based on deep learning methods. However, it is difficult to explain to scientists how they work. In this paper, we proposed to add an explanatory factor to enhance the interpretability of deep learning models in leukocyte classification. METHODS: 10 297 single images of leukocytes obtained from peripheral blood smears were included in this study. Pre-trained and fully trained VGG16 and VGG19 models were used to classify the leukocytes, and Shapley Additive Explanations (SHAP) DeepExplainer was applied to visualize the area of cells that were significant for classification. The output images from the DeepExplainer were compared with cellular elements that are essential to laboratory practice. RESULTS: The accuracy of our fully trained models was 99.81% for VGG16 and 99.79% for VGG19. It achieved slightly better results than the partially trained model, which scored 98.67% for VGG16 and 98.33% for VGG19. Their SHAP explanations indicated the significance of cellular structures in microscopic examination. Explanations in the pre-trained models have proved the cell and nucleus contours to be relevant to classification, while explanations in the fully trained models pointed to the cytoplasm area. CONCLUSION: Despite different SHAP DeepExplainer explanations for fully and partially trained models, this method appears to be helpful for the verification of leukocyte classification in automated peripheral blood smear examination.

Heartbeat Detection in Seismocardiograms with Semantic Segmentation.

Heartbeat detection is an essential part of cardiac signal analysis because it is recognized as a representative measure of cardiac function. The gold standard for heartbeat detection is to locate QRS complexes in electrocardiograms. Due to the development of sensors and information and communication technologies (ICT), seismocardiography (SCG) is becoming a viable alternative to electrocardiography to monitor heart rate. In this work, we propose a system for detecting the heartbeat based on seismocardiograms using deep learning methods. The study was carried out with a publicly available data set (CEBS) that contains simultaneous measurements of ECG, breathing signal, and seismocardiograms. Our approach to heartbeat detection in seismocardiograms uses a model based on a ResNet-based convolutional neural network and contains a squeeze and excitation unit. Our model scored state-of-the-art results (Jaccard and F1 score above 97%) on the test dataset, demonstrating its high reliability.

Extreme Situation Experienced by Dental Students of the Medical University of Silesia Due to the SARS-CoV-2 Epidemic during the First Lockdown.

The pandemic declared in many countries in 2020 due to COVID-19 led to the freezing of economies and the introduction of distance learning in both schools and universities. This unusual situation has affected the mental state of citizens, which has the potential to lead to the development of post-traumatic stress and depression. This study aimed to assess the level of stress in dental students in the context of the outbreak of the SARS-CoV-2 virus pandemic. A survey on the PSS-10 scale was prepared to measure the level of perceived stress. The study included 164 dental students at the Faculty of Medical Sciences of the Medical University of Silesia in Katowice, Poland. The results showed the impact of COVID-19 on the stress of students, with 67.7% reporting high levels of stress. The study also revealed that stress was higher among older female students. This paper recommends that the university provide more intensive psychological care as psychological first aid strategies in epidemics or natural disasters and to consider telemedicine in order to deliver services due to the limitations of the pandemic.

The Assessment of the Condition of Knee Joint Surfaces with Acoustic Emission Analysis.

The knee joint, being the largest joint in the human body, is responsible for a great percentage of leg movements. The diagnosis of the state of knee joints is usually based on X-ray scan, ultrasound imaging, computerized tomography (CT), magnetic resonance imaging (MRI), or arthroscopy. In this study, we aimed to create an inexpensive, portable device for recording the sound produced by the knee joint, and a dedicated application for its analysis. During the study, we examined fourteen volunteers of different ages, including those who had a knee injury. The device effectively enables the recording of the sounds produced by the knee joint, and the spectral analysis used in the application proved its reliability in evaluating the knee joint condition.

Evaluation of the Most Stressful Dental Treatment Procedures of Conservative Dentistry among Polish Dental Students.

BACKGROUND: Dental schools are considered to be a very stressful environment; the stress levels of dental students are higher than those of the general population. The aim of this study was to assess the level of stress among dental students while performing specific dental procedures. METHODS: A survey was conducted among 257 participants. We used an original questionnaire, which consisted of 14 questions assigned to three categories: I-Diagnosis, II-Caries Treatment, and III-Endodontic Treatment. Each participant marked their perceived level of stress during the performed dental treatment procedures. The scale included values of 0-6, where 0 indicates no stress, while 6 indicates high stress. RESULTS: Third- (p=0.006) and fourth-year (p=0.009) women were characterized by a higher level of perceived stress during dental procedures related to caries treatment. Caries treatment procedures were the most stressful for 18.3% of third-year students, 4.3% of fourth-year students, and 3.2% of fifth-year students. Furthermore, 63.4% of third-year students, 47.3% of fourth-year students, and 17.2% of fifth-year students indicated that they felt a high level of stress when performing endodontic procedures. CONCLUSION: Third- and fourth-year female students are characterized by a higher level of stress during caries and endodontic treatment procedures. The most stressful treatments for participants were endodontic treatment procedures.