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Prediction of retinopathy progression using deep learning on retinal images within the Scottish screening programme.

The British journal of ophthalmology
BACKGROUND/AIMS: National guidelines of many countries set screening intervals for diabetic retinopathy (DR) based on grading of the last screening retinal images. We explore the potential of deep learning (DL) on images to predict progression to ref...

Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda.

The British journal of ophthalmology
BACKGROUND: Evidence on the practical application of artificial intelligence (AI)-based diabetic retinopathy (DR) screening is needed.

Detecting the corneal neovascularisation area using artificial intelligence.

The British journal of ophthalmology
AIMS: To create and assess the performance of an artificial intelligence-based image analysis tool for the measurement and quantification of the corneal neovascularisation (CoNV) area.

Autonomous screening for laser photocoagulation in fundus images using deep learning.

The British journal of ophthalmology
BACKGROUND: Diabetic retinopathy (DR) is a leading cause of blindness in adults worldwide. Artificial intelligence (AI) with autonomous deep learning algorithms has been increasingly used in retinal image analysis, particularly for the screening of r...

Explainability of three-dimensional convolutional neural networks for functional magnetic resonance imaging of Alzheimer's disease classification based on gradient-weighted class activation mapping.

PloS one
Currently, numerous studies focus on employing fMRI-based deep neural networks to diagnose neurological disorders such as Alzheimer's Disease (AD), yet only a handful have provided results regarding explainability. We address this gap by applying sev...

Deep learning and digital pathology powers prediction of HCC development in steatotic liver disease.

Hepatology (Baltimore, Md.)
BACKGROUND AND AIMS: Identifying patients with steatotic liver disease who are at a high risk of developing HCC remains challenging. We present a deep learning (DL) model to predict HCC development using hematoxylin and eosin-stained whole-slide imag...

Evaluation of different machine learning algorithms for extraction decision in orthodontic treatment.

Orthodontics & craniofacial research
INTRODUCTION: The extraction decision significantly affects the treatment process and outcome. Therefore, it is crucial to make this decision with a more objective and standardized method. The objectives of this study were (1) to identify the best-pe...

Robot-assisted gait training improves walking and cerebral connectivity in children with unilateral cerebral palsy.

Pediatric research
BACKGROUND: Robot-assisted gait training (RAGT) is promising to help walking rehabilitation in cerebral palsy, but training-induced neuroplastic effects have little been investigated.

Interpretable machine learning for evaluating risk factors of freeway crash severity.

International journal of injury control and safety promotion
Machine learning (ML) models are widely employed for crash severity modelling, yet their interpretability remains underexplored. Interpretation is crucial for comprehending ML results and aiding informed decision-making. This study aims to implement ...

Intraoperative left-sided colorectal anastomotic testing in clinical practice: a multi-treatment machine-learning analysis of the iCral3 prospective cohort.

Updates in surgery
BACKGROUND: Current evidence about intraoperative anastomotic testing after left-sided colorectal resections is still controversial. The aim of this study was to analyze the impact of Indocyanine Green fluorescent angiography (ICG-FA) and air-leak te...