RESUMO
OBJECTIVE: Meibomian gland dysfunction (MGD) is a primary cause of dry eye disease. Analysis of MGD, its severity, shapes and variation in the acini of the meibomian glands (MGs) is receiving much attention in ophthalmology clinics. Existing methods for diagnosing, detection and analysing meibomianitis are not capable to quantify the irregularities to IR (infrared) images of MG area such as light reflection, interglands and intraglands boundaries, the improper focus of the light and positioning, and eyelid eversion. METHODS AND ANALYSIS: We proposed a model that is based on adversarial learning that is, conditional generative adversarial network that can overcome these blatant challenges. The generator of the model learns the mapping from the IR images of the MG to a confidence map specifying the probabilities of being a pixel of MG. The discriminative part of the model is responsible to penalise the mismatch between the IR images of the MG and confidence map. Furthermore, the adversarial learning assists the generator to produce a qualitative confidence map which is transformed into binary images with the help of fixed thresholding to fulfil the segmentation of MG. We identified MGs and interglands boundaries from IR images. RESULTS: This method is evaluated by meiboscoring, grading, Pearson correlation and Bland-Altman analysis. We also judged the quality of our method through average Pompeiu-Hausdorff distance, and Aggregated Jaccard Index. CONCLUSIONS: This technique provides a significant improvement in the quantification of the irregularities to IR. This technique has outperformed the state-of-art results for the detection and analysis of the dropout area of MGD.
RESUMO
Amidst to current Coronavirus infectious disease 2019 (COVID-19) pandemic, the international pharmaceutical federation stated that pharmacists being a part of the healthcare system had a crucial role in the management cycle of COVID-19 outbreak. The purpose of this study was to assess the knowledge, attitude and practice of community pharmacists, to snapshot their current preparedness and awareness regarding COVID-19. An online survey was conducted among a sample of 393 community pharmacists from two provinces; Punjab and Khyber-Pakhtunkhwa, Pakistan during a period of strict lockdown (10th to 30th April 2020). A validated (Cronbach alpha= 0.077) self-administered questionnaire comprised of five sections (Demographics, source of information, knowledge, attitude, and practice) was used for data collection. Logistic regression was applied to find potential factors associated with good knowledge, attitude, and practice by using SPSS version 21. Of total 393 participants, 71.5% (n=281) had good knowledge, 44% (n=175) had positive attitude and 57.3% (n=225) had good practice regarding COVID-19. Social media (45.29%, n=178) was reported as the main source to seek information regarding COVID-19. Results revealed that the age of [≥]26 years, Ph.D. degree level, and good knowledge were the substantial determinants (P<0.05) of a good attitude. Similarly, community pharmacist who had an experience of >5 years, hold a Ph.D. degree, good knowledge and good attitude had higher odds of good practice compared to reference categories (P<0.05). The findings demonstrated that the majority of community pharmacists had good knowledge, but had a poor attitude and practice towards the COVID-19. This study also highlighted the disparity in some aspects of knowledge, attitude, and practice that must be addressed in future educational, awareness, and counselling programs.
