ABSTRACT
Developing countries like Nepal face challenges in accessing health services due to sparse distribution in communities, difficult geographic terrain, limited transportation, poverty, and lack of health human expertise in rural areas. The COVID-19 pandemic added woes to the wound. To address this gap, the Hospital for Children, Eye, ENT, and Rehabilitation Services adopted an innovative approach to remote rural patient care using telehealth and artificial intelligence in close coordination with IT professionals and healthcare professionals. We developed a deep learning-based disease prediction model that incorporates telemedicine with AI for screening and diagnosing Eye and ENT diseases using nonspecialist health workers. Deep learning-based disease prediction models in Diabetic Retinopathy (DR) and Glaucoma added quality specialized services to telehealth. This paper presents the adoption of digital innovations and the incorporation of telehealth to tackle various diseases. To predict DR, 61,458 colorful retinal photographs from fundus photography and 1500 for Glaucoma were used. To reduce the biases, EyePACS data sets were also incorporated. Inception V3 transfer learning model was used for DR and employed DenseNet architecture for Glaucoma. An accuracy of more than 90 %in both models was achieved. Accurate specialized diagnosis, better medical care, patient monitoring, limited specialized hospital visits, and easier with shorter wait times are now possible. In the future, this successful model can be replicated nationally and in other developing countries. © 2022 IEEE.
ABSTRACT
Background: One severely impacted sector during a COVID-19 pandemic is the field of Medical Education. Initially, when the Medical students were sent home based on Government Lockdown orders, it was very hard to imagine that it would change the way of teaching especially in this field. After successfully adjusting to this new scenario the question of the hour is how many students and the teachers did adjust? What were their perceptions? And overall what was the effectiveness of this exercise? This study puts in a sincere effort to find the same. Methods: The student’s and the teacher’s perceptions were taken using Likert’s scale when the online sessions were going on. Another student’s perception was taken about a live lecture class in a classroom. The perception scores of the students were compared. An online test was taken after online sessions which were proctored by teachers and the marks attained by the students reflected the effectiveness of the programme. Results: The perception score of the students was better for live classes than that for the online session. There is no significant difference between the marks scored after the online session when compared to the marks scored when live lectures were being taken. Conclusion: The medical students have been benefitted by this exercise during the pandemic. Further, this mode of teaching should be implemented when regular classes will go on. © @IJCRR.