Prognostic enrichment for early-stage Huntington's disease: An explainable machine learning approach for clinical trial.

Background: In Huntington's disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on in vivo brain imaging markers, which reflect neuropathology well before clinical diagnosis. Machine learning methods offer a degree of sophistication which could significantly improve prognosis and stratification by leveraging multimodal biomarkers from large datasets.

An Open-source Image Analysis Toolbox for Quantitative Retinal Optical Coherence Tomography Angiography.

Background: Qualitative and quantitative assessment of retinal perfusion using optical coherence tomography angiography (OCTA) has shown to be effective in the treatment and management of various retinal and optic nerve diseases. However, manual analyses of OCTA images to calculate metrics related to Foveal Avascular Zone (FAZ) morphology, and retinal vascular density and morphology are costly, time-consuming, subject to human error, and are exposed to both inter and intra operator variability.

Objective: This study aimed to develop an open-source software framework for quantitative OCTA (QOCTA).

Assessment of macular findings by OCT angiography in patients without clinical signs of diabetic retinopathy: radiomics features for early screening of diabetic retinopathy.

This cross-sectional study aimed to quantitatively analyze the optical coherence tomography angiography (OCTA) images using MATLAB-based software and evaluate the initial changes in macular vascular density and the distortion of the foveal avascular zone (FAZ), before the clinical appearance of diabetic retinopathy. For this purpose, 21 diabetic patients without any clinical features indicating DR, and 21 healthy individuals matched with patients based on their demographic characteristics were included. Macular thickness, macular vascular density, and morphological changes of FAZ were assessed using OCTA.

Comparison of flexion relaxation phenomenon between female yogis and matched non-athlete group.

Background: Trunk flexion is a common exercise during daily activities. Flexion relaxation phenomenon (FRP) occurs during forward bending in which there is a sudden silence of erector spinae (ES) muscles. The pattern of forward bending differs in yoga practitioners.

A machine learning-based system for detecting leishmaniasis in microscopic images.

Background: Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease.

Cross Comparison of Motor Unit Potential Features Used in EMG Signal Decomposition.

Feature extraction is an important step of resolving an electromyographic (EMG) signal into its component motor unit potential trains, commonly known as EMG decomposition. Until now, different features have been used to represent motor unit potentials (MUPs) and improve decomposition processing time and accuracy, but a major limitation is that no systematic comparison of these features exists. In an EMG decomposition system, like any pattern recognition system, the features used for representing MUPs play an important role in the overall performance of the system.

Accuracy of software designed for automated localization of the inferior alveolar nerve canal on cone beam CT images.

Objectives: The aim of this study was to design and evaluate a new method for automated localization of the inferior alveolar nerve canal on CBCT images.

Methods: The proposed method is based on traversing both panoramic and cross-sectional slices. For the panoramic slices, morphological skeletonization is imposed, and a modified Hough transform is used while traversing the cross-sectional slices.