Ophthalmology

Equitable Implementation of Artificial Intelligence in Medical Imaging: What Can be Learned from Implementation Science?

Artificial intelligence (AI) has been rapidly adopted in various health care domains. Molecular imaging, accordingly, has demonstrated growing academic and commercial interest in AI. Unprepared and inequitable implementation and scale-up of AI in health care may pose challenges. Implementation of AI, as a complex intervention, may face various barriers, at individual, interindividual, organization...

Identification of glaucoma from fundus images using deep learning techniques.

PURPOSE: Glaucoma is one of the preeminent causes of incurable visual disability and blindness across the world due to elevated intraocular pressure within the eyes. Accurate and timely diagnosis is essential for preventing visual disability. Manual detection of glaucoma is a challenging task that needs expertise and years of experience.

Improved FCM algorithm for fisheye image cluster analysis for tree height calculation.

The height of standing trees is an important index in forestry research. This index is not only hard to measure directly but also the environmental fa...

Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future.

Convolutional neural networks (CNNs) were inspired by early findings in the study of biological vision. They have since become successful tools in com...

Deep Learning-based Diagnosis of Glaucoma Using Wide-field Optical Coherence Tomography Images.

PURPOSE: (1) To evaluate the performance of deep learning (DL) classifier in detecting glaucoma, based on wide-field swept-source optical coherence to...

Applications of interpretability in deep learning models for ophthalmology.

PURPOSE OF REVIEW: In this article, we introduce the concept of model interpretability, review its applications in deep learning models for clinical o...

Gaps in standards for integrating artificial intelligence technologies into ophthalmic practice.

PURPOSE OF REVIEW: The purpose of this review is to provide an overview of healthcare standards and their relevance to multiple ophthalmic workflows, ...

Artificial intelligence-based predictions in neovascular age-related macular degeneration.

PURPOSE OF REVIEW: Predicting treatment response and optimizing treatment regimen in patients with neovascular age-related macular degeneration (nAMD)...

Clinician-driven artificial intelligence in ophthalmology: resources enabling democratization.

PURPOSE OF REVIEW: This article aims to discuss the current state of resources enabling the democratization of artificial intelligence (AI) in ophthal...

Artificial intelligence in myopia: current and future trends.

PURPOSE OF REVIEW: Myopia is one of the leading causes of visual impairment, with a projected increase in prevalence globally. One potential approach ...

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

PURPOSE OF REVIEW: Artificial intelligence (AI) is the fourth industrial revolution in mankind's history. Natural language processing (NLP) is a type ...

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

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...

Artificial intelligence and ophthalmic surgery.

PURPOSE OF REVIEW: Artificial intelligence and deep learning have become important tools in extracting data from ophthalmic surgery to evaluate, teach...

Detection of Optic Disc Abnormalities in Color Fundus Photographs Using Deep Learning.

BACKGROUND: To date, deep learning-based detection of optic disc abnormalities in color fundus photographs has mostly been limited to the field of gla...

Perception and memory in the medial temporal lobe: Deep learning offers a new lens on an old debate.

In this issue of Neuron, Bonnen et al. (2021) use artificial neural networks to resolve a long-standing controversy surrounding the neurocognitive dic...

Deep-Learning-Based Pre-Diagnosis Assessment Module for Retinal Photographs: A Multicenter Study.

PURPOSE: Artificial intelligence (AI) deep learning (DL) has been shown to have significant potential for eye disease detection and screening on retin...

Training for object recognition with increasing spatial frequency: A comparison of deep learning with human vision.

The ontogenetic development of human vision and the real-time neural processing of visual input exhibit a striking similarity-a sensitivity toward spa...

Emergence of Content-Agnostic Information Processing by a Robot Using Active Inference, Visual Attention, Working Memory, and Planning.

Generalization by learning is an essential cognitive competency for humans. For example, we can manipulate even unfamiliar objects and can generate me...

[Deep learning-based dental plaque detection on permanent teeth and the influenced factors].

To develop an artificial intelligence system for detecting dental plaque on permanent teeth and find the influenced factors. Photos of the labial or...

Convolutional neural networks can decode eye movement data: A black box approach to predicting task from eye movements.

Previous attempts to classify task from eye movement data have relied on model architectures designed to emulate theoretically defined cognitive proce...