Automated classification of retinal vessels in fundus images is the first step towards measurement of retinal characteristics that can be used to screen and diagnose vessel abnormalities for cardiovascular and retinal disorders. This paper presents a novel approach to vessel classification to compute the artery/vein ratio (AVR) for all blood vessel segments in the fundus image. The features extracted are then subjected to a selection procedure using Random Forests (RF) where the features that contribute most to classification accuracy are chosen as input to a polynomial kernel Support Vector Machine (SVM) classifier. The most dominant feature was found to be the vessel information obtained from the Light plane of the LAB color space. The SVM is then subjected to one time training using 10-fold cross validation on images randomly selected from the VICAVR dataset before testing on an independent test dataset, derived from the same database. An Area Under the ROC Curve (AUC) of 97.2% was obtained on an average of 100 runs of the algorithm. The proposed algorithm is robust due to the feature selection procedure, and it is possible to get similar accuracies across many datasets.

To assess attitudes of pre-clinical undergraduate medical students toward learning smartphone funduscopy (SF) and its appropriateness as a teaching tool. Patients and Methods: Second year medical students received instruction on direct ophthalmoscopy (DO) and SF; they were then paired with a peer and randomly assigned to perform DO or SF first. The SF technique involved freehand alignment of the axes of the smartphone camera with a condenser lens. Both techniques were done through a maximally dilated pupil. A questionnaire was completed to acquire data on baseline experience, performance of both examination techniques, attitudes, and appropriateness. Statistical significance testing and Bland-Altman analysis were used to determine differences between DO and SF, and a multivariable mixed regression model was fitted to identify any predictors for positive attitudes toward DO or SF. Results: One hundred thirty-seven (137) individuals completed the study. A similar proportion of students could identify the optic nerve, macula, and vessels using DO and SF. However, self-reported quality scores were higher for DO for the optic nerve (p = 0.006) and macula (p = 0.08). The mean (standard deviation) attempts to identify these major structures were 2.7 (SD 2.3) for DO and 4.5 (SD 2.9) for SF (p < 0.001). Attitudes of students were consistently more positive toward DO across the five questions assessed. A small subset of students had equally positive attitudes toward DO and SF. Improved quality scores were predictive of positive attitudes for both DO and SF. Ultimately, 24% of students preferred SF over DO. Conclusion: Among inexperienced examiners of the fundus through a dilated pupil, SF is a non-inferior technique to DO in identifying structures. Despite overall favorable attitudes towards the more familiar DO, those students who quickly learned the SF technique had similar satisfaction scores. Teaching SF should be considered in undergraduate medical education.

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