Ophthalmology

Reliable Vision-Based Grasping Target Recognition for Upper Limb Prostheses.

Computer vision has shown promising potential in wearable robotics applications (e.g., human grasping target prediction and context understanding). However, in practice, the performance of computer vision algorithms is challenged by insufficient or biased training, observation noise, cluttered background, etc. By leveraging Bayesian deep learning (BDL), we have developed a novel, reliable vision-b...

DCNN-based prediction model for detection of age-related macular degeneration from color fundus images.

Age-related macular degeneration (AMD) is a degenerative disorder in the macular region of the eye. AMD is the leading cause of irreversible vision loss in the elderly population. With the increase in aged population in the world, there is an urgent need to develop low-cost, hassle-free, and portable equipment diagnostic and analytical tools for early diagnosis. As AMD detection is done by examini...

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology.

PURPOSE OF REVIEW: The application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent y...

A deep learning approach for detection of shallow anterior chamber depth based on the hidden features of fundus photographs.

BACKGROUND AND OBJECTIVES: Patients with angle-closure glaucoma (ACG) are asymptomatic until they experience a painful attack. Shallow anterior chambe...

Design and Implementation of a UAV-Based Airborne Computing Platform for Computer Vision and Machine Learning Applications.

Visual sensing of the environment is crucial for flying an unmanned aerial vehicle (UAV) and is a centerpiece of many related applications. The abilit...

DilUnet: A U-net based architecture for blood vessels segmentation.

BACKGROUND AND OBJECTIVE: Retinal image segmentation can help clinicians detect pathological disorders by studying changes in retinal blood vessels. T...

Application of High Throughput Technologies in the Development of Acute Myeloid Leukemia Therapy: Challenges and Progress.

Acute myeloid leukemia (AML) is a complex hematological malignancy characterized by extensive heterogeneity in genetics, response to therapy and long-...

Improving Haptic Response for Contextual Human Robot Interaction.

For haptic interaction, a user in a virtual environment needs to interact with proxies attached to a robot. The device must be at the exact location d...

Deep Learning Algorithm-Based MRI Image in the Diagnosis of Diabetic Macular Edema.

This study investigates the value of magnetic resonance imaging (MRI) based on a deep learning algorithm in the diagnosis of diabetic macular edema (D...

Deep learning identify retinal nerve fibre and choroid layers as markers of age-related macular degeneration in the classification of macular spectral-domain optical coherence tomography volumes.

Visual attention prediction improves performance of autonomous drone racing agents.

Humans race drones faster than neural networks trained for end-to-end autonomous flight. This may be related to the ability of human pilots to select ...

Machine Learning Based Lens-Free Shadow Imaging Technique for Field-Portable Cytometry.

The lens-free shadow imaging technique (LSIT) is a well-established technique for the characterization of microparticles and biological cells. Due to ...

Simultaneous Calibration of the Hand-Eye, Flange-Tool and Robot-Robot Relationship in Dual-Robot Collaboration Systems.

A multi-robot collaboration system can complete more complex tasks than a single robot system. Ensuring the calibration accuracy between robots in the...

Application of Improved U-Net Convolutional Neural Network for Automatic Quantification of the Foveal Avascular Zone in Diabetic Macular Ischemia.

OBJECTIVES: The foveal avascular zone (FAZ) is a biomarker for quantifying diabetic macular ischemia (DMI), to automate the identification and quantif...

Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques.

Diabetic Retinopathy (DR) is a predominant cause of visual impairment and loss. Approximately 285 million worldwide population is affected with diabet...

Artificial Intelligence for Glaucoma: Creating and Implementing Artificial Intelligence for Disease Detection and Progression.

On September 3, 2020, the Collaborative Community on Ophthalmic Imaging conducted its first 2-day virtual workshop on the role of artificial intellige...

Computer Vision-Based Path Planning for Robot Arms in Three-Dimensional Workspaces Using Q-Learning and Neural Networks.

Computer vision-based path planning can play a crucial role in numerous technologically driven smart applications. Although various path planning meth...

Detecting visually significant cataract using retinal photograph-based deep learning.

Age-related cataracts are the leading cause of visual impairment among older adults. Many significant cases remain undiagnosed or neglected in communi...

Extraction of Active Medications and Adherence Using Natural Language Processing for Glaucoma Patients.

Accuracy of medication data in electronic health records (EHRs) is crucial for patient care and research, but many studies have shown that medication ...

Multi-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS.

Age-related macular degeneration (AMD) is the leading cause of vision loss. Some patients experience vision loss over a delayed timeframe, others at a...