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Noninvasive Evaluation of Liver Fibrosis Reverse Using Artificial Neural Network Model for Chronic Hepatitis B Patients.

Computational and mathematical methods in medicine
The diagnostic performance of an artificial neural network model for chronic HBV-induced liver fibrosis reverse is not well established. Our research aims to construct an ANN model for estimating noninvasive predictors of fibrosis reverse in chronic ...

Is it possible to detect cerebral dominance via EEG signals by using deep learning?

Medical hypotheses
Each brain hemisphere is dominant for certain functions such as speech. The determination of speech laterality prior to surgery is of paramount importance for accurate risk prediction. In this study, we aimed to determine speech laterality via EEG si...

Grading of hepatocellular carcinoma based on diffusion weighted images with multiple b-values using convolutional neural networks.

Medical physics
PURPOSE: To effectively grade hepatocellular carcinoma (HCC) based on deep features derived from diffusion weighted images (DWI) with multiple b-values using convolutional neural networks (CNN).

Data-driven self-calibration and reconstruction for non-cartesian wave-encoded single-shot fast spin echo using deep learning.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Current self-calibration and reconstruction methods for wave-encoded single-shot fast spin echo imaging (SSFSE) requires long computational time, especially when high accuracy is needed.

Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks.

IEEE transactions on medical imaging
The Hausdorff Distance (HD) is widely used in evaluating medical image segmentation methods. However, the existing segmentation methods do not attempt to reduce HD directly. In this paper, we present novel loss functions for training convolutional ne...

Deep-Learning Approach to Automatic Identification of Facial Anomalies in Endocrine Disorders.

Neuroendocrinology
BACKGROUND: Deep learning has the potential to assist the medical diagnostic process. We aimed to identify facial anomalies associated with endocrinal disorders using a deep-learning approach to facilitate the process of diagnosis and follow-up.

Denoising and artefact removal for transthoracic echocardiographic imaging in congenital heart disease: utility of diagnosis specific deep learning algorithms.

The international journal of cardiovascular imaging
Deep learning (DL) algorithms are increasingly used in cardiac imaging. We aimed to investigate the utility of DL algorithms in de-noising transthoracic echocardiographic images and removing acoustic shadowing artefacts specifically in patients with ...

Implementation of a cloud-based referral platform in ophthalmology: making telemedicine services a reality in eye care.

The British journal of ophthalmology
BACKGROUND: Hospital Eye Services (HES) in the UK face an increasing number of optometric referrals driven by progress in retinal imaging. The National Health Service (NHS) published a 10-year strategy (NHS Long-Term Plan) to transform services to me...

Is it possible to automatically assess pretreatment digital rectal examination documentation using natural language processing? A single-centre retrospective study.

BMJ open
OBJECTIVES: To develop and test a method for automatic assessment of a quality metric, provider-documented pretreatment digital rectal examination (DRE), using the outputs of a natural language processing (NLP) framework.