Ophthalmology

Bioinspired multisensory neural network with crossmodal integration and recognition.

The integration and interaction of vision, touch, hearing, smell, and taste in the human multisensory neural network facilitate high-level cognitive functionalities, such as crossmodal integration, recognition, and imagination for accurate evaluation and comprehensive understanding of the multimodal world. Here, we report a bioinspired multisensory neural network that integrates artificial optic, ...

Gaze and Event Tracking for Evaluation of Recommendation-Driven Purchase.

Recommendation systems play an important role in e-commerce turnover by presenting personalized recommendations. Due to the vast amount of marketing content online, users are less susceptible to these suggestions. In addition to the accuracy of a recommendation, its presentation, layout, and other visual aspects can improve its effectiveness. This study evaluates the visual aspects of recommender ...

Deep learning for the classification of medical kidney disease: a pilot study for electron microscopy.

Artificial intelligence (AI) is a new frontier and often enigmatic for medical professionals. Cloud computing could open up the field of computer visi...

Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network.

Owing to the recent development in deep learning, machine vision has been widely used in intelligent manufacturing equipment in multiple fields, inclu...

Prediction of Phakic Intraocular Lens Vault Using Machine Learning of Anterior Segment Optical Coherence Tomography Metrics.

PURPOSE: To compare the achieved vault using the conventional manufacturer's nomogram and the predicted vault using machine learning, in a large cohor...

Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study.

PURPOSE: To compare the performance of a novel convolutional neural network (CNN) classifier and human graders in detecting angle closure in EyeCam (C...

Within-category representational stability through the lens of manipulable objects.

Our ability to recognize an object amongst many exemplars is one of our most important features, and one that putatively distinguishes humans from non...

Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation.

A common shortfall of supervised deep learning for medical imaging is the lack of labeled data, which is often expensive and time consuming to collect...

Neural Encoding and Decoding With Distributed Sentence Representations.

Building computational models to account for the cortical representation of language plays an important role in understanding the human linguistic sys...

Artificial Intelligence in the assessment of diabetic retinopathy from fundus photographs.

: Over the next 25 years, the global prevalence of diabetes is expected to grow to affect 700 million individuals. Consequently, an unprecedented numb...

Human and Human-Interfaced AI Interactions: Modulation of Human Male Autonomic Nervous System via Pupil Mimicry.

Pupillary alterations in virtual humans induce neurophysiological responses within an observer. Technological advances have enabled rapid developments...

Simultaneous Hand-Eye and Intrinsic Calibration of a Laser Profilometer Mounted on a Robot Arm.

A method for simultaneous laser profilometer and hand-eye calibration in relation to an industrial robot as well as its implementation is presented. I...

Detection of Diabetic Retinopathy from Ultra-Widefield Scanning Laser Ophthalmoscope Images: A Multicenter Deep Learning Analysis.

PURPOSE: To develop a deep learning (DL) system that can detect referable diabetic retinopathy (RDR) and vision-threatening diabetic retinopathy (VTDR...

Machine Learning of Patient Characteristics to Predict Admission Outcomes in the Undiagnosed Diseases Network.

IMPORTANCE: The Undiagnosed Diseases Network (UDN) is a national network that evaluates individual patients whose signs and symptoms have been refract...

Predicting Incremental and Future Visual Change in Neovascular Age-Related Macular Degeneration Using Deep Learning.

PURPOSE: To evaluate the predictive usefulness of quantitative imaging biomarkers, acquired automatically from OCT scans, of cross-sectional and futur...

Visual Analytics of a Computer-Aided Diagnosis System for Pancreatic Lesions.

Machine learning is a powerful and effective tool for medical image analysis to perform computer-aided diagnosis (CAD). Having great potential in impr...

Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network.

BACKGROUND: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma ...

Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images.

We aimed to develop a model to predict visual field (VF) in the central 10 degrees in patients with glaucoma, by training a convolutional neural netwo...

Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification.

Deep learning (DL) has been successfully applied to the diagnosis of ophthalmic diseases. However, rare diseases are commonly neglected due to insuffi...

Application of Machine Learning to Predict Patient No-Shows in an Academic Pediatric Ophthalmology Clinic.

Patient "no-shows" are missed appointments resulting in clinical inefficiencies, revenue loss, and discontinuity of care. Using secondary electronic h...