ABSTRACT
Background: Fear of childbirth (FOC) is one of the most common psychological symptoms among pregnant women and significantly relates to cesarean section, anxiety, and depression. However, it is not clear the prevalence and risk factors of FOC among Chinese pregnant women since the outbreak of the COVID-19 pandemic. Aims: The objective of this study was to examine the associations between coping styles, intolerance of uncertainty, and FOC. Method: From December 2021 to April 2022, a cross-sectional survey was conducted in two hospitals in China through convenient sampling. The cross-sectional survey was conducted among 969 pregnant women, which included the Childbirth Attitude Questionnaire (CAQ), Intolerance of Uncertainty Scale-12 (IUS-12), and Simplified Coping Style Questionnaire (SCSQ). Results: The total prevalence of FOC was 67.8%. The percentages of women with mild (a score of 28-39), moderate (40-51), and severe FOC (52-64) were 43.6, 20.2, and 4.0%, respectively. The regression results indicated that primiparas, unplanned pregnancy, few spousal support, intolerance of uncertainty, and negative coping styles were significant risk factors of FOC. Women who adopt positive coping strategies experienced a lower level of childbirth fear. Conclusion: These findings suggest that cultivating positive coping styles and obtaining sufficient childbirth information may be helpful for mothers' mental health. Regular screening assessment of perinatal psychological symptoms, such as the high level of intolerance of uncertainty and negative coping styles, should be adopted to reduce the risk of fear of childbirth.
ABSTRACT
With the continuous development of computer technology, big data acquisition and imaging methods, the application of artificial intelligence (AI) in medical fields is expanding. The use of machine learning and deep learning in the diagnosis and treatment of ophthalmic diseases is becoming more widespread. As one of the main causes of visual impairment, myopia has a high global prevalence. Early screening or diagnosis of myopia, combined with other effective therapeutic interventions, is very important to maintain a patient's visual function and quality of life. Through the training of fundus photography, optical coherence tomography, and slit lamp images and through platforms provided by telemedicine, AI shows great application potential in the detection, diagnosis, progression prediction and treatment of myopia. In addition, AI models and wearable devices based on other forms of data also perform well in the behavioral intervention of myopia patients. Admittedly, there are still some challenges in the practical application of AI in myopia, such as the standardization of datasets; acceptance attitudes of users; and ethical, legal and regulatory issues. This paper reviews the clinical application status, potential challenges and future directions of AI in myopia and proposes that the establishment of an AI-integrated telemedicine platform will be a new direction for myopia management in the post-COVID-19 period.