Ophthalmology

Evaluating the effectiveness of chatbots and traditional resources in patient education on dry eye disease.

CLINICAL RELEVANCE: Artificial intelligence chatbots demonstrate potential as valuable educational resources for patients with dry eye disease, offering complementary information to established medical platforms.

Predicting Retinal Nerve Fiber Layer Thickness From Ocular Hypertension Treatment Study Optic Disc Photographs.

IMPORTANCE: Deep learning predictions of retinal nerve fiber layer (RNFL) thickness derived from optic disc photographs may help to determine risk for development of primary open-angle glaucoma (POAG) in patients with ocular hypertension.

Predictive modeling for early detection of refractory esophageal stricture following esophageal atresia surgery: insight from a machine learning study.

BACKGROUND: Refractory esophageal stricture (RES) presents a challenging complication after esophageal atresia (EA) repair. Earlier identification of ...

The Eye of Horus: Enhancing Head and Neck Cancer Care with Computer Vision.

BACKGROUND: Head and neck cancer (HNC) poses formidable treatment challenges globally due to complex anatomy, image interpretation difficulties, and o...

Evaluating real-world performance of an automated offline glaucoma AI on a smartphone fundus camera across glaucoma severity stages.

PURPOSE: Leveraging an artificial intelligence system (AI) for glaucoma screening can mitigate the current challenges and provide prompt detection and...

Metasurface-enhanced terahertz imaging for glioblastoma in orthotopic xenograft mouse model combined with neural network decision making.

Terahertz (THz) optical sensing and imaging offer significant potential in a range of biological and medical applications owing to their low-energy, n...

A computer vision-based approach for high-throughput automated analysis of Arabidopsis seedling phenotypes.

Phenotype observations are common methodologies in plant biology studies, ranging from recording growth parameters to flowering dates. Identifying mut...

Mechanism of cognitive style effects on hazard recognition of construction workers: A machine learning-aided approach.

BackgroundHazard recognition is a critical skill for construction workers and primarily consists of two stages: visual search and cognitive processes....

Computer-vision research powers surveillance technology.

An increasing number of scholars, policymakers and grassroots communities argue that artificial intelligence (AI) research-and computer-vision researc...

Topo-CNN: Retinal Image Analysis with Topological Deep Learning.

The analysis of fundus images is vital for early detection of retinal diseases such as diabetic retinopathy (DR), glaucoma, and age-related macular de...

Integrating ocular and clinical features to enhance intravenous glucocorticoid response prediction in thyroid eye disease: a machine learning approach.

PURPOSES: Predicting intravenous glucocorticoid (IVGC) efficacy in thyroid eye disease (TED) is vital for personalized treatment and minimizing side e...

AI in Medical Questionnaires: Innovations, Diagnosis, and Implications.

This systematic review aimed to explore the current applications, potential benefits, and issues of artificial intelligence (AI) in medical questionna...

Real-world faricimab switch in France: artificial intelligence-based detection of changes in exudative signs in difficult-to-treat neovascular age-related macular degeneration.

PURPOSE: Some patients with neovascular age-related macular degeneration (nAMD) have persistent signs of exudation under treatment with intravitreal i...

Recognizing American Sign Language gestures efficiently and accurately using a hybrid transformer model.

Gesture recognition plays a vital role in computer vision, especially for interpreting sign language and enabling human-computer interaction. Many exi...

Towards a comprehensive characterization of arteries and veins in retinal imaging.

Retinal fundus imaging is crucial for diagnosing and monitoring eye diseases, which are often linked to systemic health conditions such as diabetes an...

A multimodal visual-language foundation model for computational ophthalmology.

Early detection of eye diseases is vital for preventing vision loss. Existing ophthalmic artificial intelligence models focus on single modalities, ov...

Designing Clinical Decision Support Systems (CDSS)-A User-Centered Lens of the Design Characteristics, Challenges, and Implications: Systematic Review.

BACKGROUND: Clinical decision support systems (CDSS) have the potential to play a crucial role in enhancing health care quality by providing evidence-...

Comparison of a novel gamified binocular therapy and standard monocular patching therapy in treating unilateral amblyopia in young children: a prospective, multicenter, randomized controlled trial.

BACKGROUND: Amblyopia is a leading cause of visual impairment in children, and conventional treatments often encounter challenges related to poor comp...

Large-scale observational study of AI-based patient and surgical material verification system in ophthalmology: real-world evaluation in 37 529 cases.

BACKGROUND: Surgical errors in ophthalmology can have devastating consequences. We developed an artificial intelligence (AI)-based surgical safety sys...

Recent advances and future perspectives of long-acting ophthalmic preparations (LAOPs) in clinical applications.

The intricate structure of ocular barriers significantly impedes drug penetration, leading to suboptimal efficacy of conventional ophthalmic formulati...