AVD-YOLOv5: a new lightweight network architecture for high-speed aortic valve detection from a new and large echocardiography dataset.

Heart disease detection is currently gaining widespread attention as a means to enhance the accuracy of cardiologists' diagnoses from cardiac images and reduce diagnosis time. Although high-resolution computed tomography (CT) images are typically favored for heart disease detection, the drawbacks of cost and radiation exposure to patients necessitate alternative approaches. In this context, utilizing ultrasound images becomes pivotal to mitigate radiation risks and maintain cost-effectiveness. In this paper, we propose a novel lightweight model, AVD-YOLOv5, designed for automated aortic valve detection on echocardiography images. This model incorporates several enhancements to the YOLOv5 architecture. Notably, the depth-wise separable convolution significantly contributes to the model's lightweight design by reducing the number of parameters while maintaining precision. We have also created a new and larger dataset comprising 260 echocardiography images specifically for aortic valve detection. Experimental results indicate that the precision value of the modified ADV-YOLOv5 model stands at 94.3%, with a recall value of 86.8%. The model also demonstrates a notable 67% reduction in inference time compared to the original YOLOv5 model. Although there is a marginal reduction in precision by 0.94%, the model's efficiency is significantly increased. The proposed system can be used by cardiologists for more efficient and reliable diagnosis.

Psychological state and predictors of psychiatric morbidity in COVID-19 patients six weeks after discharge.

To be able to detect possible psychological distress and long-term deterioration caused by COVID-19, following the patient, who has recovered, is crucial. Therefore, this study (i); aims to examine the ongoing fear-loss of control, the rate of anxiety, depression, and post-traumatic stress disorder levels following the 6th week after discharge; (ii) to examine the effect of post-traumatic stress disorder on anxiety, and depression and (iii) within the same context to reveal the developmental markers of psychiatric morbidity and the risk group. The study includes 180 patients who were hospitalized with COVID-19 diagnosis. Sociodemographic Data Form, the Hospital Anxiety Depression Scale and the Impact of Event Scale-Revised were used in the current study. High rates of symptoms of anxiety, depression, and PTSD were reported by the inpatients, as more than one-third scored above the anxiety and depression cut-off scores of borderline abnormal and abnormal. Also, 37.22 % of the participants reported the likely presence of PTSD symptoms. Anxiety and depression were significantly positively related to the symptoms of PTSD. The results suggest that there is psychiatric morbidity in anxiety, depression, and post-traumatic stress disorder and that especially posttraumatic stress poses a risk for other psychopathologies.