BACKGROUND: Ocular changes are traditionally associated with only a few hepatobiliary diseases. These changes are non-specific and have a low detection rate, limiting their potential use as clinically independent diagnostic features. Therefore, we ai...
IMPORTANCE: Adherence to screening for vision-threatening proliferative sickle cell retinopathy is limited among patients with sickle cell hemoglobinopathy despite guidelines recommending dilated fundus examinations beginning in childhood. An automat...
BACKGROUND: Facial palsy assessment is nonstandardized. Clinician-graded scales are limited by subjectivity and observer bias. Computer-aided grading would be desirable to achieve conformity in facial palsy assessment and to compare the effectiveness...
BACKGROUND: In current approaches to vision screening in the community, a simple and efficient process is needed to identify individuals who should be referred to tertiary eye care centres for vision loss related to eye diseases. The emergence of dee...
BACKGROUND: The application of deep learning to retinal photographs has yielded promising results in predicting age, sex, blood pressure, and haematological parameters. However, the broader applicability of retinal photograph-based deep learning for ...
This study aims to develop a deep learning framework to determine the Severity of Alopecia Tool (SALT) score for measurement of hair loss in patients with alopecia areata.
PURPOSE: To investigate whether and to what extent central serous chorioretinopathy (CSC) depicted on color fundus photographs can be assessed using deep learning technology.
Advances in computer science and photography not only are pervasive but are also quantifiably influencing the practice of medicine. Recent progress in both software and hardware technology has translated into the design of advanced artificial neural ...
BACKGROUND: In this study, a novel and fully automatic skin disease classification approach is proposed using statistical feature extraction and Artificial Neural Network (ANN) based classification using first and second order statistical moments, th...
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