Ophthalmology

Clinical Interpretable Deep Learning Model for Glaucoma Diagnosis.

Despite the potential to revolutionise disease diagnosis by performing data-driven classification, clinical interpretability of ConvNet remains challenging. In this paper, a novel clinical interpretable ConvNet architecture is proposed not only for accurate glaucoma diagnosis but also for the more transparent interpretation by highlighting the distinct regions recognised by the network. To the bes...

CD4+ versus CD8+ T-lymphocyte identification in an integrated microfluidic chip using light scattering and machine learning.

T lymphocytes are a group of cells representing the main effectors of human adaptive immunity. Characterization of the most representative T-lymphocyte subclasses, CD4+ and CD8+, is challenging, but has a significant impact on clinical decisions. Up to now, T lymphocytes have been identified by quite complex cytometric assays, which are based on antibody labeling. However, a label-free approach ba...

Glaucoma management in the era of artificial intelligence.

Glaucoma is a result of irreversible damage to the retinal ganglion cells. While an early intervention could minimise the risk of vision loss in glauc...

Discrimination of the behavioural dynamics of visually impaired infants via deep learning.

Sensory loss is associated with behavioural changes, but how behavioural dynamics change when a sensory modality is impaired remains unclear. Here, by...

A Machine Learning-Based Prediction Platform for P-Glycoprotein Modulators and Its Validation by Molecular Docking.

P-glycoprotein (P-gp) is an important determinant of multidrug resistance (MDR) because its overexpression is associated with increased efflux of vari...

A human-in-the-loop deep learning paradigm for synergic visual evaluation in children.

Visual development during early childhood is a vital process. Examining the visual acuity of children is essential for early detection of visual abnor...

Robust and Accurate Hand-Eye Calibration Method Based on Schur Matric Decomposition.

To improve the accuracy and robustness of hand-eye calibration, a hand-eye calibration method based on Schur matric decomposition is proposed in this ...

Optimal selection of SOP and SPH using fuzzy inference system for on-line epileptic seizure prediction based on EEG phase synchronization.

Living conditions of patients with refractory epilepsy will be significantly improved by a successful prediction of epileptic seizures. A proper warni...

A microsurgical robot research platform for robot-assisted microsurgery research and training.

PURPOSE: Ocular surgery, ear, nose and throat surgery and neurosurgery are typical types of microsurgery. A versatile training platform can assist mic...

The Use of Transdermal Estrogen in Castrate-resistant, Steroid-refractory Prostate Cancer.

BACKGROUND: Androgen-deprivation therapy is the mainstay of treatment for metastatic prostate cancer. Corticosteroids and estrogens are also useful ag...

DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images.

Recent studies have shown that convolutional neural networks (CNNs) can be more accurate, efficient and even deeper on their training if they include ...

Joint optic disc and cup segmentation using semi-supervised conditional GANs.

Glaucoma is a chronic and widespread eye disease threatening humans' irreversible vision loss. The cup-to-disc ratio (CDR), one of the most important ...

REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs.

Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most...

A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head.

Optical coherence tomography (OCT) has become an established clinical routine for the in vivo imaging of the optic nerve head (ONH) tissues, that is c...

IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge.

Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Scre...

Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs.

PURPOSE: To develop and validate a deep learning (DL) algorithm that predicts referable glaucomatous optic neuropathy (GON) and optic nerve head (ONH)...

The impact of artificial intelligence in the diagnosis and management of glaucoma.

Deep learning (DL) is a subset of artificial intelligence (AI), which uses multilayer neural networks modelled after the mammalian visual cortex capab...

Discrimination of indoor versus outdoor environmental state with machine learning algorithms in myopia observational studies.

BACKGROUND: Wearable smart watches provide large amount of real-time data on the environmental state of the users and are useful to determine risk fac...

Deep learning based retinal OCT segmentation.

We look at the recent application of deep learning (DL) methods in automated fine-grained segmentation of spectral domain optical coherence tomography...

Predicting gene regulatory interactions based on spatial gene expression data and deep learning.

Reverse engineering of gene regulatory networks (GRNs) is a central task in systems biology. Most of the existing methods for GRN inference rely on ge...