Ophthalmology

Dissecting self-supervised learning methods for surgical computer vision.

The field of surgical computer vision has undergone considerable breakthroughs in recent years with the rising popularity of deep neural network-based methods. However, standard fully-supervised approaches for training such models require vast amounts of annotated data, imposing a prohibitively high cost; especially in the clinical domain. Self-Supervised Learning (SSL) methods, which have begun t...

Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography.

Here, we have developed a deep learning method to fully automatically detect and quantify six main clinically relevant atrophic features associated with macular atrophy (MA) using optical coherence tomography (OCT) analysis of patients with wet age-related macular degeneration (AMD). The development of MA in patients with AMD results in irreversible blindness, and there is currently no effective m...

Re: Vogl et al.: Predicting topographic disease progression and treatment response of pegcetacoplan in geographic atrophy quantified by deep learning (Ophthalmol Retina. 2023;7:4-13).

Ocular images-based artificial intelligence on systemic diseases.

PURPOSE: To provide a summary of the research advances on ocular images-based artificial intelligence on systemic diseases.

A deep learning analysis of body kinematics during magnetically tethered flight.

Flying rely on their vision to detect visual objects and adjust their flight course. Despite their robust fixation on a dark, vertical bar, our under...

Development of a deep learning system to detect glaucoma using macular vertical optical coherence tomography scans of myopic eyes.

Myopia is one of the risk factors for glaucoma, making accurate diagnosis of glaucoma in myopic eyes particularly important. However, diagnosis of gla...

Supervised deep learning with vision transformer predicts delirium using limited lead EEG.

As many as 80% of critically ill patients develop delirium increasing the need for institutionalization and higher morbidity and mortality. Clinicians...

Robot-assisted stereoencephalography vs subdural electrodes in the evaluation of temporal lobe epilepsy.

OBJECTIVE: Invasive video-electroencephalography (iVEEG) is the gold standard for evaluation of refractory temporal lobe epilepsy before second stage ...

Eye for an AI: More-than-seeing, fauxtomation, and the enactment of uncertain data in digital pathology.

Artificial Intelligence (AI) tools are being developed to assist with increasingly complex diagnostic tasks in medicine. This produces epistemic disru...

EOG Signal Classification with Wavelet and Supervised Learning Algorithms KNN, SVM and DT.

The work carried out in this paper consists of the classification of the physiological signal generated by eye movement called Electrooculography (EOG...

Deep learning for asbestos counting.

The PCM (phase contrast microscopy) method for asbestos counting needs special sample treatments, hence it is time consuming and rather expensive. As ...

Computer Vision Based on a Modular Neural Network for Automatic Assessment of Physical Therapy Rehabilitation Activities.

Physical rehabilitation techniques during the treatment of clinical pathology are one of the most challenging areas for the medical structure, patient...

Deep Learning-Based Estimation of Implantable Collamer Lens Vault Using Optical Coherence Tomography.

PURPOSE: To develop and validate a deep learning neural network for automated measurement of implantable collamer lens (ICL) vault using anterior segm...

Robot-assisted vs. manually guided stereoelectroencephalography for refractory epilepsy: a systematic review and meta-analysis.

Robotic assistance has improved electrode implantation precision in stereoelectroencephalography (SEEG) for refractory epilepsy patients. We sought to...

Review on challenges for robotic eye surgery; surgical systems, technologies, cost-effectiveness, and controllers.

In recent decades, a number of surgical systems have been developed and are applied for a growing variety of surgeries. This review will consider the ...

EE-Explorer: A Multimodal Artificial Intelligence System for Eye Emergency Triage and Primary Diagnosis.

PURPOSE: To develop a multimodal artificial intelligence (AI) system, EE-Explorer, to triage eye emergencies and assist in primary diagnosis using met...

Establishment and Evaluation of Intelligent Diagnostic Model for Ophthalmic Ultrasound Images Based on Deep Learning.

OBJECTIVE: The goal of the work described here was to construct a deep learning-based intelligent diagnostic model for ophthalmic ultrasound images to...

Tracking Online Interest in Artificial Intelligence in Ophthalmology Using Google Trends.

PURPOSE: To examine trends in internet search queries related to artificial intelligence (AI) in ophthalmology and determine the correlation between o...

From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches.

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are...

Development and international validation of custom-engineered and code-free deep-learning models for detection of plus disease in retinopathy of prematurity: a retrospective study.

BACKGROUND: Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed through interval screening by paediatric ophthalmol...