End-to-end deep learning patient level classification of affected territory of ischemic stroke patients in DW-MRI.

Purpose: To develop an end-to-end DL model for automated classification of affected territory in DWI of stroke patients.

Materials And Methods: In this retrospective multicenter study, brain DWI studies from January 2017 to April 2020 from Center 1, from June 2020 to December 2020 from Center 2, and from November 2019 to April 2020 from Center 3 were included. Four radiologists labeled images into five classes: anterior cerebral artery (ACA), middle cerebral artery (MCA), posterior circulation (PC), and watershed (WS) regions, as well as normal images.

Machine learning-based classification of varicocoele grading: A promising approach for diagnosis and treatment optimization.

Background: Varicocoele is a correctable cause of male infertility. Although physical examination is still being used in diagnosis and grading, it gives conflicting results when compared to ultrasonography-based varicocoele grading.

Objectives: We aimed to develop a multi-class machine learning model for the grading of varicocoeles based on ultrasonographic measurements.

Deep Learning Classification of Ischemic Stroke Territory on Diffusion-Weighted MRI: Added Value of Augmenting the Input with Image Transformations.

Our primary aim with this study was to build a patient-level classifier for stroke territory in DWI using AI to facilitate fast triage of stroke to a dedicated stroke center. A retrospective collection of DWI images of 271 and 122 consecutive acute ischemic stroke patients from two centers was carried out. Pretrained MobileNetV2 and EfficientNetB0 architectures were used to classify territorial subtypes as middle cerebral artery, posterior circulation, or watershed infarcts along with normal slices.

Voxel level dense prediction of acute stroke territory in DWI using deep learning segmentation models and image enhancement strategies.

Purpose: To build a stroke territory classifier model in DWI by designing the problem as a multiclass segmentation task by defining each stroke territory as distinct segmentation targets and leveraging the guidance of voxel wise dense predictions.

Materials And Methods: Retrospective analysis of DWI images of 218 consecutive acute anterior or posterior ischemic stroke patients examined between January 2017 to April 2020 in a single center was carried out. Each stroke area was defined as distinct segmentation target with different class labels.

Comparison of the fibrosis degree using acoustic radiation force impulse elastography and diffusion-weighted magnetic resonance imaging in chronic hepatitis cases.

Objective: The aim of this study was to investigate the correlation of fibrosis stages in cases of chronic hepatitis by comparing shear wave elastography and diffusion-weighted magnetic resonance imaging.

Methods: A total of 46 chronic hepatitis patients with an age range of 20-50 years were classified into three groups based on their fibrosis stages. Comparison group 1: the presence of fibrosis (S0 and S1≤); comparison group 2: the presence of significant fibrosis (≤S2 and S3≤); and comparison group 3: the presence of cirrhosis (≤S4 and S6).