Radiology

Artificial intelligence for ultrasound microflow imaging in breast cancer diagnosis.

PURPOSE: To develop and evaluate artificial intelligence (AI) algorithms for ultrasound (US) microfl...

Posterior circulation ischemic stroke: radiomics-based machine learning approach to identify onset time from magnetic resonance imaging.

PURPOSE: Posterior circulation ischemic stroke (PCIS) possesses unique features. However, previous s...

A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features.

BACKGROUND: Positron emission tomography (PET) is extensively employed for diagnosing and staging va...

Anatomically aware dual-hop learning for pulmonary embolism detection in CT pulmonary angiograms.

Pulmonary Embolisms (PE) represent a leading cause of cardiovascular death. While medical imaging, t...

Cross-patch feature interactive net with edge refinement for retinal vessel segmentation.

Retinal vessel segmentation based on deep learning is an important auxiliary method for assisting cl...

Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours.

BACKGROUND: The timely identification and management of ovarian cancer are critical determinants of ...

An Automated Deep Learning-Based Framework for Uptake Segmentation and Classification on PSMA PET/CT Imaging of Patients with Prostate Cancer.

Uptake segmentation and classification on PSMA PET/CT are important for automating whole-body tumor ...

The US Government's Latest Presidential Executive Order on Artificial Intelligence: Potential Implications in Radiology.

Diagnostic performance of a deep-learning model using F-FDG PET/CT for evaluating recurrence after radiation therapy in patients with lung cancer.

OBJECTIVE: We developed a deep learning model for distinguishing radiation therapy (RT)-related chan...

Artificial intelligence in multiple sclerosis management: Challenges in a new era.

Multiple sclerosis poses diagnostic and therapeutic challenges for healthcare professionals, with a ...

MSK-TIM: A Telerobotic Ultrasound System for Assessing the Musculoskeletal System.

The aim of this paper is to investigate technological advancements made to a robotic tele-ultrasound...

Identifying Patients with CSF-Venous Fistula Using Brain MRI: A Deep Learning Approach.

BACKGROUND AND PURPOSE: Spontaneous intracranial hypotension is an increasingly recognized condition...

Artificial Intelligence and Breast US: Radiologists Won't Regret Opening Pandora's Box.

AI Applications to Breast MRI: Today and Tomorrow.

In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partl...

Performance Evaluation of Deep, Shallow and Ensemble Machine Learning Methods for the Automated Classification of Alzheimer's Disease.

Artificial intelligence (AI)-based approaches are crucial in computer-aided diagnosis (CAD) for vari...

A novel machine learning model for breast cancer detection using mammogram images.

The most fatal disease affecting women worldwide now is breast cancer. Early detection of breast can...

Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials.

BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS le...

Radiologists and trainees' perspectives on artificial intelligence.

BACKGROUND AND OBJECTIVES: The purpose of this study was to investigate perspectives held by radiolo...

Are deep learning classification results obtained on CT scans fair and interpretable?

Following the great success of various deep learning methods in image and object classification, the...

Evaluation of large language models performance against humans for summarizing MRI knee radiology reports: A feasibility study.

OBJECTIVES: This study addresses the critical need for accurate summarization in radiology by compar...

The Promise of Reciprocal Human and Machine Learning for Observational Error Reduction in Diagnostic Imaging.