Ophthalmology

Peripapillary atrophy classification using CNN deep learning for glaucoma screening.

Glaucoma is the second leading cause of blindness worldwide, and peripapillary atrophy (PPA) is a morphological symptom associated with it. Therefore, it is necessary to clinically detect PPA for glaucoma diagnosis. This study was aimed at developing a detection method for PPA using fundus images with deep learning algorithms to be used by ophthalmologists or optometrists for screening purposes. T...

Good view frames from ultrasonography (USG) video containing ONS diameter using state-of-the-art deep learning architectures.

This paper presents an automated method for detection of the diagnostically prominent frames containing optic nerve sheath (ONS) from ocular ultrasonography video using deep learning; such frames are referred to as "Good View" frames in this paper. Vivid acquisition and measurement of diagnostic features during ultrasound imaging is a challenging task; it needs a highly skilled and experienced med...

A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study.

BACKGROUND: There is no simple model to screen for Alzheimer's disease, partly because the diagnosis of Alzheimer's disease itself is complex-typicall...

Accurate detection of dairy cow mastitis with deep learning technology: a new and comprehensive detection method based on infrared thermal images.

Mastitis is one of the most common diseases in dairy cows and has a negative impact on their welfare and life, causing significant economic losses to ...

Differentiating a pachychoroid and healthy choroid using an unsupervised machine learning approach.

The purpose of this study was to introduce a new machine learning approach for differentiation of a pachychoroid from a healthy choroid based on enhan...

Hint-Based Image Colorization Based on Hierarchical Vision Transformer.

Hint-based image colorization is an image-to-image translation task that aims at creating a full-color image from an input luminance image when a smal...

Research on Athlete Detection Method Based on Visual Image and Artificial Intelligence System.

Pedestrian detection and tracking based on computer vision has gradually become an international pattern recognition, which is one of the most active ...

Detecting High-Risk Factors and Early Diagnosis of Diabetes Using Machine Learning Methods.

Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, ...

Point-of-care SARS-CoV-2 sensing using lens-free imaging and a deep learning-assisted quantitative agglutination assay.

The persistence of the global COVID-19 pandemic caused by the SARS-CoV-2 virus has continued to emphasize the need for point-of-care (POC) diagnostic ...

A Novel Computer Vision Model for Medicinal Plant Identification Using Log-Gabor Filters and Deep Learning Algorithms.

Computer vision is the science that enables computers and machines to see and perceive image content on a semantic level. It combines concepts, techni...

A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-Based Semantic Scene Understanding Using Multi-Modal Sensor Fusion.

Simultaneous Localisation and Mapping (SLAM) is one of the fundamental problems in autonomous mobile robots where a robot needs to reconstruct a previ...

Japan Ocular Imaging Registry: a national ophthalmology real-world database.

In 2017, the Japanese Ophthalmological Society (JOS) created the Japan Ocular Imaging (JOI) registry, a national database of images and clinical data ...

Training Deep Learning Models to Work on Multiple Devices by Cross-Domain Learning with No Additional Annotations.

PURPOSE: To create an unsupervised cross-domain segmentation algorithm for segmenting intraretinal fluid and retinal layers on normal and pathologic m...

Deep Learning-Based Noise Reduction Improves Optical Coherence Tomography Angiography Imaging of Radial Peripapillary Capillaries in Advanced Glaucoma.

PURPOSE: We applied deep learning-based noise reduction (NR) to optical coherence tomography-angiography (OCTA) images of the radial peripapillary cap...

Imaging of the optic nerve: technological advances and future prospects.

Over the past decade, ocular imaging strategies have greatly advanced the diagnosis and follow-up of patients with optic neuropathies. Developments in...

Development of a deep learning algorithm for myopic maculopathy classification based on OCT images using transfer learning.

PURPOSE: To apply deep learning (DL) techniques to develop an automatic intelligent classification system identifying the specific types of myopic mac...

Medical Staff and Resident Preferences for Using Deep Learning in Eye Disease Screening: Discrete Choice Experiment.

BACKGROUND: Deep learning-assisted eye disease diagnosis technology is increasingly applied in eye disease screening. However, no research has suggest...

A novel machine learning-based approach for the detection and analysis of spontaneous synaptic currents.

Spontaneous synaptic activity is a hallmark of biological neural networks. A thorough description of these synaptic signals is essential for understan...

Understanding transformation tolerant visual object representations in the human brain and convolutional neural networks.

Forming transformation-tolerant object representations is critical to high-level primate vision. Despite its significance, many details of tolerance i...

A digital mask to safeguard patient privacy.

The storage of facial images in medical records poses privacy risks due to the sensitive nature of the personal biometric information that can be extr...