PURPOSE: Pathological examination, the current gold standard for differentiating eyelid basal cell carcinoma (BCC) and sebaceous gland carcinoma (SGC), is invasive, time-consuming, and often inaccessible in primary care hospitals. Therefore, a non-in...
Accurate anatomical measurements of the eyelids are essential in periorbital plastic surgery for both disease treatment and procedural planning. Recent researches in eye diseases have adopted deep learning works to measure MRD. However, such works en...
Facial nerve paralysis (FNP) impair eyelid closure and blinking, risking ophthalmic complications and vision loss. Current detection methods primarily rely on static facial asymmetries, overlooking the dynamic eyelid movements during blinking that ar...
PURPOSE: Online health information seekers may access information produced by artificial intelligence language models such as ChatGPT (OpenAI). The medical field may pose a significant challenge for incorporating these applications given the training...
PURPOSE: Trachoma surveys are used to estimate the prevalence of trachomatous inflammation-follicular (TF) to guide mass antibiotic distribution. These surveys currently rely on human graders, introducing a significant resource burden and potential f...
Journal of plastic, reconstructive & aesthetic surgery : JPRAS
Jul 25, 2024
BACKGROUND: Various classification systems for tear trough deformity (TTD) have been published; however, their complexity can pose challenges in clinical use, especially for less experienced surgeons. It is believed that artificial intelligence (AI) ...
PURPOSE: To develop and validate a deep learning facial landmark detection network to automate the assessment of periocular anthropometric measurements.
Lagophthalmos is the incomplete closure of the eyelids posing the risk of corneal ulceration and blindness. Lagophthalmos is a common symptom of various pathologies. We aimed to program a convolutional neural network to automatize lagophthalmos diagn...
Deep neural networks (DNNs) have enabled recent advances in the accuracy and robustness of video-oculography. However, to make robust predictions, most DNN models require extensive and diverse training data, which is costly to collect and label. In t...
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