Deep Learning-Based Approaches for Enhanced Diagnosis and Comprehensive Understanding of Carpal Tunnel Syndrome.

Carpal tunnel syndrome (CTS) is a prevalent medical condition resulting from compression of the median nerve in the hand, often caused by overuse or age-related factors. In this study, a total of 160 patients participated, including 80 individuals with CTS presenting varying levels of severity across different age groups. Numerous studies have explored the use of machine learning (ML) and deep learning (DL) techniques for CTS diagnosis.

Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques.

Carpal tunnel syndrome (CTS) is a clinical disease that occurs due to compression of the median nerve in the carpal tunnel. The determination of the severity of carpal tunnel syndrome is essential to provide appropriate therapeutic interventions. Machine learning (ML)-based modeling can be used to classify diseases, make decisions, and create new therapeutic interventions.

Precise Identification of Prostate Cancer from DWI Using Transfer Learning.

The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions.

Early diabetic retinopathy diagnosis based on local retinal blood vessel analysis in optical coherence tomography angiography (OCTA) images.

Purpose: This paper introduces a new computer-aided diagnosis (CAD) system for detecting early-stage diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA) images.

Methods: The proposed DR-CAD system is based on the analysis of new local features that describe both the appearance and retinal structure in OCTA images. It starts with a new segmentation approach that has the ability to extract the blood vessels from superficial and deep retinal OCTA maps.

Retracted: Early diabetic retinopathy diagnosis based on local retinal blood vessels analysis in optical coherence tomography angiography (OCTA) images.

The above article from Medical Physics, published online on 22 February 2018 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the authors, the journal Editor in Chief and John Wiley & Sons Ltd. The retraction has been agreed following an investigation carried out by the editors due to major overlap with a previously published article: British Journal of Ophthalmology (BJO) (Sandhu HS, Eladawi N, Elmogy M, et al Automated diabetic retinopathy detection using optical coherence tomography angiography: a pilot study, British Journal of Ophthalmology Published Online First: 23 January 2018.

Classification of retinal diseases based on OCT Images.

Optical Coherence Topography (OCT) is an emerging biomedical imaging technology that offers non-invasive real-time, high-resolution imaging of highly scattering tissues. It is widely used in ophthalmology to perform diagnostic imaging on the structure of the anterior eye and the retina. Clinical studies are carried out to assess the application of OCT for some retinal diseases.

Automatic blood vessels segmentation based on different retinal maps from OCTA scans.

The retinal vascular network reflects the health of the retina, which is a useful diagnostic indicator of systemic vascular. Therefore, the segmentation of retinal blood vessels is a powerful method for diagnosing vascular diseases. This paper presents an automatic segmentation system for retinal blood vessels from Optical Coherence Tomography Angiography (OCTA) images.