Deep-learning prediction of cardiovascular outcomes from routine retinal images in individuals with type 2 diabetes.

Background: Prior studies have demonstrated an association between retinal vascular features and cardiovascular disease (CVD), however most studies have only evaluated a few simple parameters at a time. Our aim was to determine whether a deep-learning artificial intelligence (AI) model could be used to predict CVD outcomes from routinely obtained diabetic retinal screening photographs and to compare its performance to a traditional clinical CVD risk score.

Methods: We included 6127 individuals with type 2 diabetes without myocardial infarction or stroke prior to study entry.

Identification of bullets fired from air guns using machine and deep learning methods.

Ballistics (the linkage of bullets and cartridge cases to weapons) is a common type of evidence encountered in criminal cases around the world. The interest lies in determining whether two bullets were fired using the same firearm. This paper proposes an automated method to classify bullets from surface topography and Land Engraved Area (LEA) images of the fired pellets using machine and deep learning methods.

Fine-mapping of retinal vascular complexity loci identifies Notch regulation as a shared mechanism with myocardial infarction outcomes.

There is increasing evidence that the complexity of the retinal vasculature measured as fractal dimension, D, might offer earlier insights into the progression of coronary artery disease (CAD) before traditional biomarkers can be detected. This association could be partly explained by a common genetic basis; however, the genetic component of D is poorly understood. We present a genome-wide association study (GWAS) of 38,000 individuals with white British ancestry from the UK Biobank aimed to comprehensively study the genetic component of D and analyse its relationship with CAD.

Association Between Retinal Features From Multimodal Imaging and Schizophrenia.

Importance: The potential association of schizophrenia with distinct retinal changes is of clinical interest but has been challenging to investigate because of a lack of sufficiently large and detailed cohorts.

Objective: To investigate the association between retinal biomarkers from multimodal imaging (oculomics) and schizophrenia in a large real-world population.

Design, Setting, And Participants: This cross-sectional analysis used data from a retrospective cohort of 154 830 patients 40 years and older from the AlzEye study, which linked ophthalmic data with hospital admission data across England.

Association of Thigh Muscle Strength with Texture Features Based on Proton Density Fat Fraction Maps Derived from Chemical Shift Encoding-Based Water-Fat MRI.

Purpose: Based on conventional and quantitative magnetic resonance imaging (MRI), texture analysis (TA) has shown encouraging results as a biomarker for tissue structure. Chemical shift encoding-based water-fat MRI (CSE-MRI)-derived proton density fat fraction (PDFF) of thigh muscles has been associated with musculoskeletal, metabolic, and neuromuscular disorders and was demonstrated to predict muscle strength. The purpose of this study was to investigate PDFF-based TA of thigh muscles as a predictor of thigh muscle strength in comparison to mean PDFF.

Texture Features of Proton Density Fat Fraction Maps from Chemical Shift Encoding-Based MRI Predict Paraspinal Muscle Strength.

Texture analysis (TA) has shown promise as a surrogate marker for tissue structure, based on conventional and quantitative MRI sequences. Chemical-shift-encoding-based MRI (CSE-MRI)-derived proton density fat fraction (PDFF) of paraspinal muscles has been associated with various medical conditions including lumbar back pain (LBP) and neuromuscular diseases (NMD). Its application has been shown to improve the prediction of paraspinal muscle strength beyond muscle volume.

A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification.

The eye affords a unique opportunity to inspect a rich part of the human microvasculature non-invasively via retinal imaging. Retinal blood vessel segmentation and classification are prime steps for the diagnosis and risk assessment of microvascular and systemic diseases. A high volume of techniques based on deep learning have been published in recent years.

Vertebral Bone Marrow Heterogeneity Using Texture Analysis of Chemical Shift Encoding-Based MRI: Variations in Age, Sex, and Anatomical Location.

Vertebral bone marrow composition has been extensively studied in the past and shown potential as imaging biomarker for osteoporosis, hematopoietic, and metabolic disorders. However, beyond quantitative assessment of bone marrow fat, little is known about its heterogeneity. Therefore, we investigated bone marrow heterogeneity of the lumbar spine using texture analysis of chemical-shift-encoding (CSE-MRI) based proton density fat fraction (PDFF) maps and its association with age, sex, and anatomical location.

Multidetector Computed Tomography Imaging: Effect of Sparse Sampling and Iterative Reconstruction on Trabecular Bone Microstructure.

Multidetector computed tomography-based trabecular bone microstructure analysis ensures promising results in fracture risk prediction caused by osteoporosis. Because multidetector computed tomography is associated with high radiation exposure, its clinical routine use is limited. Hence, in this study, we investigated in 11 thoracic midvertebral specimens whether trabecular texture parameters are comparable derived from (1) images reconstructed using statistical iterative reconstruction (SIR) and filtered back projection as criterion standard at different exposures (80, 150, 220, and 500 mAs) and (2) from SIR-based sparse sampling projections (12.

Effect of radiation dose reduction on texture measures of trabecular bone microstructure: an in vitro study.

Osteoporosis is characterized by bone loss and degradation of bone microstructure leading to fracture particularly in elderly people. Osteoporotic bone degeneration and fracture risk can be assessed by bone mineral density and trabecular bone score from 2D projection dual-energy X-ray absorptiometry images. However, multidetector computed tomography image based quantification of trabecular bone microstructure showed significant improvement in prediction of fracture risk beyond that from bone mineral density and trabecular bone score; however, high radiation exposure limits its use in routine clinical in vivo examinations.

Automated diabetic macular edema (DME) grading system using DWT, DCT Features and maculopathy index.

The cause of diabetic macular edema (DME) is due to prolonged and uncontrolled diabetes mellitus (DM) which affects the vision of diabetic subjects. DME is graded based on the exudate location from the macula. It is clinically diagnosed using fundus images which is tedious and time-consuming.

Automated screening system for retinal health using bi-dimensional empirical mode decomposition and integrated index.

Posterior Segment Eye Diseases (PSED) namely Diabetic Retinopathy (DR), glaucoma and Age-related Macular Degeneration (AMD) are the prime causes of vision loss globally. Vision loss can be prevented, if these diseases are detected at an early stage. Structural abnormalities such as changes in cup-to-disc ratio, Hard Exudates (HE), drusen, Microaneurysms (MA), Cotton Wool Spots (CWS), Haemorrhages (HA), Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in PSED can be identified by manual examination of fundus images by clinicians.

Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features.

Age-related Macular Degeneration (AMD) affects the central vision of aged people. It can be diagnosed due to the presence of drusen, Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in the fundus images. It is labor intensive and time-consuming for the ophthalmologists to screen these images.

Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review.

Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs.

Application of different imaging modalities for diagnosis of Diabetic Macular Edema: A review.

Diabetic Macular Edema (DME) is caused by accumulation of extracellular fluid from hyperpermeable capillaries within the macula. DME is one of the leading causes of blindness among Diabetes Mellitus (DM) patients. Early detection followed by laser photocoagulation can save the visual loss.

Local configuration pattern features for age-related macular degeneration characterization and classification.

Age-related Macular Degeneration (AMD) is an irreversible and chronic medical condition characterized by drusen, Choroidal Neovascularization (CNV) and Geographic Atrophy (GA). AMD is one of the major causes of visual loss among elderly people. It is caused by the degeneration of cells in the macula which is responsible for central vision.

Advances in quantitative muscle ultrasonography using texture analysis of ultrasound images.

Musculoskeletal ultrasound imaging can be used to investigate the skeletal muscle structure in terms of architecture (thickness, cross-sectional area, fascicle length and fascicle pennation angle) and texture. Gray-scale analysis is commonly used to characterize transverse scans of the muscle. Gray mean value is used to distinguish between normal and pathologic muscles, but it depends on the image acquisition system and its settings.

Application of higher-order spectra for automated grading of diabetic maculopathy.

Diabetic macular edema (DME) is one of the most common causes of visual loss among diabetes mellitus patients. Early detection and successive treatment may improve the visual acuity. DME is mainly graded into non-clinically significant macular edema (NCSME) and clinically significant macular edema according to the location of hard exudates in the macula region.

Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images.

Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs.

Decision support system for age-related macular degeneration using discrete wavelet transform.

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population.

Computer-aided diagnosis of diabetic retinopathy: a review.

Diabetes mellitus may cause alterations in the retinal microvasculature leading to diabetic retinopathy. Unchecked, advanced diabetic retinopathy may lead to blindness. It can be tedious and time consuming to decipher subtle morphological changes in optic disk, microaneurysms, hemorrhage, blood vessels, macula, and exudates through manual inspection of fundus images.

Automated detection of optic disk in retinal fundus images using intuitionistic fuzzy histon segmentation.

The human eye is one of the most sophisticated organs, with perfectly interrelated retina, pupil, iris cornea, lens, and optic nerve. Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Uncontrolled diabetic retinopathy (DR) and glaucoma may lead to blindness.

Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment.

In the case of carotid atherosclerosis, to avoid unnecessary surgeries in asymptomatic patients, it is necessary to develop a technique to effectively differentiate symptomatic and asymptomatic plaques. In this paper, we have presented a data mining framework that characterizes the textural differences in these two classes using several grayscale features based on a novel combination of trace transform and fuzzy texture. The features extracted from the delineated plaque regions in B-mode ultrasound images were used to train several classifiers in order to prepare them for classification of new test plaques.

Brownian motion curve-based textural classification and its application in cancer diagnosis.

Objective: To develop an automated diagnostic methodology based on textural features of the oral mucosal epithelium to discriminate normal and oral submucous fibrosis (OSF).

Study Design: A total of 83 normal and 29 OSF images from histopathologic sections of the oral mucosa are considered. The proposed diagnostic mechanism consists of two parts: feature extraction using Brownian motion curve (BMC) and design ofa suitable classifier.