AIMC Topic: Deep Learning

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Artificial intelligence and ophthalmic surgery.

Current opinion in ophthalmology
PURPOSE OF REVIEW: Artificial intelligence and deep learning have become important tools in extracting data from ophthalmic surgery to evaluate, teach, and aid the surgeon in all phases of surgical management. The purpose of this review is to highlig...

Generative adversarial networks in ophthalmology: what are these and how can they be used?

Current opinion in ophthalmology
PURPOSE OF REVIEW: The development of deep learning (DL) systems requires a large amount of data, which may be limited by costs, protection of patient information and low prevalence of some conditions. Recent developments in artificial intelligence t...

Deep learning-based natural language processing in ophthalmology: applications, challenges and future directions.

Current opinion in ophthalmology
PURPOSE OF REVIEW: Artificial intelligence (AI) is the fourth industrial revolution in mankind's history. Natural language processing (NLP) is a type of AI that transforms human language, to one that computers can interpret and process. NLP is still ...

Artificial intelligence in myopia: current and future trends.

Current opinion in ophthalmology
PURPOSE OF REVIEW: Myopia is one of the leading causes of visual impairment, with a projected increase in prevalence globally. One potential approach to address myopia and its complications is early detection and treatment. However, current healthcar...

Deep learning for abdominal ultrasound: A computer-aided diagnostic system for the severity of fatty liver.

Journal of the Chinese Medical Association : JCMA
BACKGROUND: The prevalence of nonalcoholic fatty liver disease is increasing over time worldwide, with similar trends to those of diabetes and obesity. A liver biopsy, the gold standard of diagnosis, is not favored due to its invasiveness. Meanwhile,...

Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning.

JAMA ophthalmology
IMPORTANCE: Quantitative volumetric measures of retinal disease in optical coherence tomography (OCT) scans are infeasible to perform owing to the time required for manual grading. Expert-level deep learning systems for automatic OCT segmentation hav...

Applications of interpretability in deep learning models for ophthalmology.

Current opinion in ophthalmology
PURPOSE OF REVIEW: In this article, we introduce the concept of model interpretability, review its applications in deep learning models for clinical ophthalmology, and discuss its role in the integration of artificial intelligence in healthcare.