Explainable machine learning framework for cataracts recognition using visual features.

Cataract is the leading ocular disease of blindness and visual impairment globally. Deep neural networks (DNNs) have achieved promising cataracts recognition performance based on anterior segment optical coherence tomography (AS-OCT) images; however, they have poor explanations, limiting their clinical applications. In contrast, visual features extracted from original AS-OCT images and their transform forms (e.

Score Prior Guided Iterative Solver for Speckles Removal in Optical Coherent Tomography Images.

Optical coherence tomography (OCT) is a widely used non-invasive imaging modality for ophthalmic diagnosis. However, the inherent speckle noise becomes the leading cause of OCT image quality, and efficient speckle removal algorithms can improve image readability and benefit automated clinical analysis. As an ill-posed inverse problem, it is of utmost importance for speckle removal to learn suitable priors.

Diagnostic criteria of anterior segment swept-source optical coherence tomography to detect gonioscopic angle closure.

Aims: To compare the diagnostic performance of 360° anterior segment optical coherence tomography assessment by applying normative percentile cut-offs versus iris trabecular contact (ITC) for detecting gonioscopic angle closure.

Methods: In this multicentre study, 394 healthy individuals were included in the normative dataset to derive the age-specific and angle location-specific normative percentiles of angle open distance (AOD500) and trabecular iris space area (TISA500) which were measured every 10° for 360°. 119 healthy participants and 170 patients with angle closure by gonioscopy were included in the test dataset to investigate the diagnostic performance of three sets of criteria for detection of gonioscopic angle closure: (1) the 10th and (2) the 5th percentiles of AOD500/TISA500, and (3) ITC (ie, AOD500/TISA500=0 mm/mm).

Multi-style spatial attention module for cortical cataract classification in AS-OCT image with supervised contrastive learning.

Background And Objective: Precise cortical cataract (CC) classification plays a significant role in early cataract intervention and surgery. Anterior segment optical coherence tomography (AS-OCT) images have shown excellent potential in cataract diagnosis. However, due to the complex opacity distributions of CC, automatic AS-OCT-based CC classification has been rarely studied.

Structural Priors Guided Network for the Corneal Endothelial Cell Segmentation.

The segmentation of blurred cell boundaries in cornea endothelium microscope images is challenging, which affects the clinical parameter estimation accuracy. Existing deep learning methods only consider pixel-wise classification accuracy and lack of utilization of cell structure knowledge. Therefore, the segmentation of the blurred cell boundary is discontinuous.

Crystalline lens nuclear age prediction as a new biomarker of nucleus degeneration.

Background: The crystalline lens is a transparent structure of the eye to focus light on the retina. It becomes muddy, hard and dense with increasing age, which makes the crystalline lens gradually lose its function. We aim to develop a nuclear age predictor to reflect the degeneration of the crystalline lens nucleus.

Assessment of Iris Trabecular Contact in Eyes with Gonioscopic Angle-Closure.

Purpose: To investigate the extent of iris trabecular contact (ITC) measured by anterior segment OCT (AS-OCT) and its association with primary angle-closure (PAC) and PAC glaucoma (PACG) in eyes with gonioscopic angle-closure and to determine the diagnostic performance of ITC for detection of gonioscopic angle-closure.

Design: Multicenter, prospective study.

Participants: A total of 119 healthy participants with gonioscopic open-angle and 170 patients with gonioscopic angle-closure (94 with PAC suspect and 76 with PAC/PACG) were included.

Lens structure segmentation from AS-OCT images via shape-based learning.

Background And Objectives: The lens is one of the important refractive media in the eyeball. Abnormality of the nucleus or cortex in the lens can lead to ocular disorders such as cataracts and presbyopia. To achieve an accurate diagnosis, segmentation of these ocular structures from anterior segment optical coherence tomography (AS-OCT) is essential.

Attention to region: Region-based integration-and-recalibration networks for nuclear cataract classification using AS-OCT images.

Nuclear cataract (NC) is a leading eye disease for blindness and vision impairment globally. Accurate and objective NC grading/classification is essential for clinically early intervention and cataract surgery planning. Anterior segment optical coherence tomography (AS-OCT) images are capable of capturing the nucleus region clearly and measuring the opacity of NC quantitatively.

Mixed pyramid attention network for nuclear cataract classification based on anterior segment OCT images.

Nuclear cataract (NC) is a leading ocular disease globally for blindness and vision impairment. NC patients can improve their vision through cataract surgery or slow the opacity development with early intervention. Anterior segment optical coherence tomography (AS-OCT) image is an emerging ophthalmic image type, which can clearly observe the whole lens structure.

Adaptive feature squeeze network for nuclear cataract classification in AS-OCT image.

Nuclear cataract (NC) is an age-related cataract disease. Cataract surgery is an effective method to improve the vision and life quality of NC patients. Anterior segment optical coherence tomography (AS-OCT) images are noninvasive, reproductive, and easy-measured, which can capture opacity clearly on the lens nucleus region.

Reproducibility of deep learning based scleral spur localisation and anterior chamber angle measurements from anterior segment optical coherence tomography images.

Aims: To apply a deep learning model for automatic localisation of the scleral spur (SS) in anterior segment optical coherence tomography (AS-OCT) images and compare the reproducibility of anterior chamber angle (ACA) width between deep learning located SS (DLLSS) and manually plotted SS (MPSS).

Methods: In this multicentre, cross-sectional study, a test dataset comprising 5166 AS-OCT images from 287 eyes (116 healthy eyes with open angles and 171 eyes with primary angle-closure disease (PACD)) of 287 subjects were recruited from four ophthalmology clinics. Each eye was imaged twice by a swept-source AS-OCT (CASIA2, Tomey, Nagoya, Japan) in the same visit and one eye of each patient was randomly selected for measurements of ACA.

Hybrid Variation-Aware Network for Angle-Closure Assessment in AS-OCT.

Automatic angle-closure assessment in Anterior Segment OCT (AS-OCT) images is an important task for the screening and diagnosis of glaucoma, and the most recent computer-aided models focus on a binary classification of anterior chamber angles (ACA) in AS-OCT, i.e., open-angle and angle-closure.

Angle-closure assessment in anterior segment OCT images via deep learning.

Precise characterization and analysis of anterior chamber angle (ACA) are of great importance in facilitating clinical examination and diagnosis of angle-closure disease. Currently, the gold standard for diagnostic angle assessment is observation of ACA by gonioscopy. However, gonioscopy requires direct contact between the gonioscope and patients' eye, which is uncomfortable for patients and may deform the ACA, leading to false results.

An Efficient Lens Structures Segmentation Method on AS-OCT Images.

Lens structures segmentation on anterior segment optical coherence tomography (AS-OCT) images is a fundamental task for cataract grading analysis. In this paper, in order to reduce the computational cost while keeping the segmentation accuracy, we propose an efficient segmentation method for lens structures segmentation. At first, we adopt an efficient semantic segmentation network in the work, and used it to extract the lens area image instead of the conventional object detection method, and then used it once again to segment the lens structures.