Radiology

Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality.

This study aimed to evaluate the impact of radiation dose and focal spot size on the image quality of super-resolution deep-learning reconstruction (SR-DLR) in comparison with iterative reconstruction (IR) and normal-resolution DLR (NR-DLR) algorithms for cardiac CT. Catphan-700 phantom was scanned on a 320-row scanner at six radiation doses (small and large focal spots at 1.4-4.3 and 5.8-8.8 mGy,...

AI-based automated evaluation of image quality and protocol tailoring in patients undergoing MRI for suspected prostate cancer.

PURPOSE: To develop and validate an artificial intelligence (AI) application in a clinical setting to decide whether dynamic contrast-enhanced (DCE) sequences are necessary in multiparametric prostate MRI.

A machine learning model based on clinical features and ultrasound radiomics features for pancreatic tumor classification.

OBJECTIVE: This study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the...

Automatic Quantitative Assessment of Muscle Strength Based on Deep Learning and Ultrasound.

Skeletal muscle is a vital organ that promotes human movement and maintains posture. Accurate assessment of muscle strength is helpful to provide valu...

Assessing the effectiveness of artificial intelligence (AI) in prioritising CT head interpretation: study protocol for a stepped-wedge cluster randomised trial (ACCEPT-AI).

INTRODUCTION: Diagnostic imaging is vital in emergency departments (EDs). Accessibility and reporting impacts ED workflow and patient care. With radio...

Challenges and opportunities in the development and clinical implementation of artificial intelligence based synthetic computed tomography for magnetic resonance only radiotherapy.

Synthetic computed tomography (sCT) generated from magnetic resonance imaging (MRI) can serve as a substitute for planning CT in radiation therapy (RT...

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration.

Accurately predicting potential evapotranspiration (PET) is crucial in water resource management, agricultural planning, and climate change studies. T...

[Artificial intelligence in diagnostic radiology for dose management : Advances and perspectives using the example of computed tomography].

CLINICAL-METHODOLOGICAL PROBLEM: Imaging procedures employing ionizing radiation require compliance with European directives and national regulations ...

Diagnosis of placenta accreta spectrum using ultrasound texture feature fusion and machine learning.

INTRODUCTION: Placenta accreta spectrum (PAS) is an obstetric disorder arising from the abnormal adherence of the placenta to the uterine wall, often ...

Time-Dependent Deep Learning Prediction of Multiple Sclerosis Disability.

The majority of deep learning models in medical image analysis concentrate on single snapshot timepoint circumstances, such as the identification of c...

Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography.

BACKGROUND: To apply machine learning (ML) algorithms to perform multiclass diabetic retinopathy (DR) classification using both clinical data and opti...

Artificial intelligence-based automatic nidus segmentation of cerebral arteriovenous malformation on time-of-flight magnetic resonance angiography.

OBJECTIVE: Accurate nidus segmentation and quantification have long been challenging but important tasks in the clinical management of Cerebral Arteri...

ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis.

The traditional diagnostic process for autism spectrum disorder (ASD) is subjective, where early and accurate diagnosis significantly affects treatmen...

Autism spectrum disorders detection based on multi-task transformer neural network.

Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that cause people difficulties in social interaction and communication. Identifying A...

Transformer-Based Multilabel Deep Learning Model Is Efficient for Detecting Ankle Lateral and Medial Ligament Injuries on Magnetic Resonance Imaging and Improving Clinicians' Diagnostic Accuracy for Rotational Chronic Ankle Instability.

PURPOSE: To develop a deep learning (DL) model that can simultaneously detect lateral and medial collateral ligament injuries of the ankle, aiding in ...

[Accelerated musculoskeletal magnetic resonance imaging with deep learning-based image reconstruction at 0.55 T-3 T].

CLINICAL/METHODICAL ISSUE: Magnetic resonance imaging (MRI) is a central component of musculoskeletal imaging. However, long image acquisition times c...

A Predictive Model for Intraoperative Cerebrospinal Fluid Leak During Endonasal Pituitary Adenoma Resection Using a Convolutional Neural Network.

BACKGROUND: Cerebrospinal fluid (CSF) leak during endoscopic endonasal transsphenoidal surgery can lead to postoperative complications. The clinical a...

Point-of-care AI-enhanced novice echocardiography for screening heart failure (PANES-HF).

The increasing prevalence of heart failure (HF) in ageing populations drives demand for echocardiography (echo). There is a worldwide shortage of trai...

A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging.

Images obtained from single-photon emission computed tomography for myocardial perfusion imaging (MPI SPECT) contain noises and artifacts, making card...

Imaging Flow Cytometry and Convolutional Neural Network-Based Classification Enable Discrimination of Hematopoietic and Leukemic Stem Cells in Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is a heterogenous blood cancer with a dismal prognosis. It emanates from leukemic stem cells (LSCs) arising from the gene...