Impact of COVID-19 Pandemic on the Clinical Activities in Obstetrics and Gynecology: A National Survey in China

The effect of fear of COVID-19 on health-seeking behaviors and Pap smear test rates in women

This study was carried out to determine the effect of fear of COVID-19 on health-seeking behaviors and Pap smear test rates in women. This study was conducted online between June and September 2021. Women aged 21-65 years were invited to participate in the study via social media. A total of 396 women comprised the sample for this study. Data were collected the Fear of COVID-19 Scale, and the Health-seeking Behavior Scale. Data were evaluated using descriptive statistics, parametric tests and regression analysis. As a result of the study, 11.1 percent of women were found to have had a Pap smear test during the pandemic period. Fear of COVID-19 was determined to have a positive and significant effect on online health-seeking behavior (t = 7.915, p < .001), professional health-seeking behavior (t = 4.503, p < .001), traditional health-seeking behavior (t = 6.037, p < .001), and general health-seeking behavior(t = 8.390, p < .001) while it did not have any effect on having a Pap smear test (p > .05). This study showed that although the fear of COVID-19 positively affected health-seeking behaviors in women, the rates of gynecological examination and Pap smear test rates were low during the pandemic.

Automatic Segmentation of Novel Coronavirus Pneumonia Lesions in CT Images Utilizing Deep-Supervised Ensemble Learning Network

Background: The novel coronavirus disease 2019 (COVID-19) has been spread widely in the world, causing a huge threat to the living environment of people. Objective: Under CT imaging, the structure features of COVID-19 lesions are complicated and varied greatly in different cases. To accurately locate COVID-19 lesions and assist doctors to make the best diagnosis and treatment plan, a deep-supervised ensemble learning network is presented for COVID-19 lesion segmentation in CT images. Methods: Since a large number of COVID-19 CT images and the corresponding lesion annotations are difficult to obtain, a transfer learning strategy is employed to make up for the shortcoming and alleviate the overfitting problem. Based on the reality that traditional single deep learning framework is difficult to extract complicated and varied COVID-19 lesion features effectively that may cause some lesions to be undetected. To overcome the problem, a deep-supervised ensemble learning network is presented to combine with local and global features for COVID-19 lesion segmentation. Results: The performance of the proposed method was validated in experiments with a publicly available dataset. Compared with manual annotations, the proposed method acquired a high intersection over union (IoU) of 0.7279 and a low Hausdorff distance (H) of 92.4604. Conclusion: A deep-supervised ensemble learning network was presented for coronavirus pneumonia lesion segmentation in CT images. The effectiveness of the proposed method was verified by visual inspection and quantitative evaluation. Experimental results indicated that the proposed method has a good performance in COVID-19 lesion segmentation.