Radiology

Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis.

The conventional statistical approach for analyzing resting state functional MRI (rs-fMRI) data struggles to accurately distinguish between patients with multiple sclerosis (MS) and those with neuromyelitis optic spectrum disorders (NMOSD), highlighting the need for improved diagnostic efficacy. In this study, multilevel functional metrics including resting state functional connectivity, amplitude...

Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer.

BACKGROUND: This study explores the clinical value of a machine learning (ML) model based on ultrasound radiomics features of primary foci, combined with clinicopathologic factors to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) for patients with breast cancer (BC).

Deep Learning Technology for Classification of Thyroid Nodules Using Multi-View Ultrasound Images: Potential Benefits and Challenges in Clinical Application.

BACKGRUOUND: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nod...

Deep Learning for Predicting Difficulty in Radical Prostatectomy: A Novel Evaluation Scheme.

OBJECTIVE: To explore new metrics for assessing radical prostatectomy difficulty through a two-stage deep learning method from preoperative magnetic r...

Application of deep learning in automated localization and interpretation of coronary artery calcification in oncological PET/CT scans.

Coronary artery calcification (CAC) is a key marker of coronary artery disease (CAD) but is often underreported in cancer patients undergoing non-gate...

Weakly-supervised thyroid ultrasound segmentation: Leveraging multi-scale consistency, contextual features, and bounding box supervision for accurate target delineation.

Weakly-supervised learning (WSL) methods have gained significant attention in medical image segmentation, but they often face challenges in accurately...

Artificial intelligence performance in ultrasound-based lymph node diagnosis: a systematic review and meta-analysis.

BACKGROUND AND OBJECTIVES: Accurate classification of lymphadenopathy is essential for determining the pathological nature of lymph nodes (LNs), which...

DML-MFCM: A multimodal fine-grained classification model based on deep metric learning for Alzheimer's disease diagnosis.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder. There are no drugs and methods for the treatment of AD, but early intervention c...

The ANFIS-RSM based multi-objective optimization and modelling of ultrasound-assisted extraction of polyphenols from jamun fruit (Syzygium cumini).

Given their potential as natural substitutes for artificial additives and their health advantages, the extraction of bioactive substances like polyphe...

Clinical validation of artificial intelligence-based single-subject morphometry without normative reference database.

BACKGROUND: Single-subject voxel-based morphometry (VBM) is a powerful technique for reader-independent detection of brain atrophy in structural magne...

A deep learning approach versus expert clinician panel in the classification of posterior circulation infarction.

BACKGROUND: Posterior circulation infarction (POCI) is common. Imaging techniques such as non-contrast-CT (NCCT) and diffusion-weighted-magnetic-reson...

Generation of high-resolution MPRAGE-like images from 3D head MRI localizer (AutoAlign Head) images using a deep learning-based model.

PURPOSE: Magnetization prepared rapid gradient echo (MPRAGE) is a useful three-dimensional (3D) T1-weighted sequence, but is not a priority in routine...

Clinical Application of Deep Learning-Assisted Needles Reconstruction in Prostate Ultrasound Brachytherapy.

PURPOSE: High dose rate (HDR) prostate brachytherapy (BT) procedure requires image-guided needle insertion. Given that general anesthesia is often emp...

A systematic review of deep learning in MRI-based cerebral vascular occlusion-based brain diseases.

Neurological disorders, including cerebral vascular occlusions and strokes, present a major global health challenge due to their high mortality rates ...

A benchmark of deep learning approaches to predict lung cancer risk using national lung screening trial cohort.

Deep learning (DL) methods have demonstrated remarkable effectiveness in assisting with lung cancer risk prediction tasks using computed tomography (C...

Improving spleen segmentation in ultrasound images using a hybrid deep learning framework.

This paper introduces a novel method for spleen segmentation in ultrasound images, using a two-phase training approach. In the first phase, the SegFor...

End-to-end deep-learning model for the detection of coronary artery stenosis on coronary CT images.

PURPOSE: We examined whether end-to-end deep-learning models could detect moderate (≥50%) or severe (≥70%) stenosis in the left anterior descending ar...

Diagnostic accuracy in coronary CT angiography analysis: artificial intelligence versus human assessment.

BACKGROUND: Visual assessment of coronary CT angiography (CCTA) is time-consuming, influenced by reader experience and prone to interobserver variabil...

Visualizing Preosteoarthritis: Updates on UTE-Based Compositional MRI and Deep Learning Algorithms.

Osteoarthritis (OA) is heterogeneous and involves structural changes in the whole joint, such as cartilage, meniscus/labrum, ligaments, and tendons, m...

A Radiomic-Clinical Model of Contrast-Enhanced Mammography for Breast Cancer Biopsy Outcome Prediction.

RATIONALE AND OBJECTIVES: In the USA over 1 million breast biopsies are performed annually. Approximately 9.6% diagnostic exams were given Breast Imag...