OBJECTIVE: To evaluate the effectiveness of a deep learning-based style adaptation strategy in improving the diagnostic accuracy and cross-camera generalisability of artificial intelligence (AI) for detecting diabetic retinopathy (DR).
BACKGROUND: Strabismus is a common ocular misalignment that can impair binocular vision if untreated. Conventional diagnosis and treatment rely on clinical prism diopter (PD) readings, which quantify deviation along with base direction. However, thes...
BACKGROUND: Artificial intelligence has become part of healthcare with a multitude of applications being customized to roles required in clinical practice. There has been an expanding growth and development of computer technology with increasing appe...
PURPOSE: To evaluate the performance of a multimodal large language model (LLM), Claude 3.5 Sonnet, in interpreting meibography images for Meibomian gland dropout grading and morphological abnormality detection.
PURPOSE: To develop and validate a deep learning model for the detection of functional blepharoptosis from external ocular photographs, and to quantify the impact of augmenting the training data with synthetic images on model performance.
PURPOSE OF REVIEW: The integration of artificial intelligence (AI) in the diagnosis and management of anterior segment diseases has rapidly expanded, demonstrating significant potential to revolutionize clinical practice.
IMPORTANCE: While prospective studies have investigated the accuracy of artificial intelligence (AI) for detection of diabetic retinopathy (DR) and diabetic macular edema (DME), to date, little published data exist on the clinical performance of thes...
Optometry and vision science : official publication of the American Academy of Optometry
Jan 13, 2025
SIGNIFICANCE: Optimal meibography utilization and interpretation are hindered due to poor lid presentation, blurry images, or image artifacts and the challenges of applying clinical grading scales. These results, using the largest image dataset analy...
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