Radiology

Assessing the influence of kernel selection on chest computed tomography image quality across varying dose levels using TrueFidelity reconstruction.

Deep learning image reconstruction (DLIR) utilizes neural networks to generate high-quality computed tomography (CT) images. One commercially available DLIR software is TrueFidelity from GE Healthcare. The Standard kernel was the only available reconstruction kernel previously, but recently other kernels, including the Lung kernel, have been introduced by GE. This study aimed to evaluate the image...

Assessing the robustness of AI lesion risk scores at different exposure settings using an anthropomorphic breast phantom.

To assess the robustness of risk scores provided by an artificial intelligence (AI) system for digital mammography (DM), when varying the exposure conditions. An anthropomorphic breast phantom containing a lesion, was imaged with DM at different tube voltages (kV), tube loadings (mAs), and anode/filter combinations (W/Rh, Mo/Mo, and Mo/Rh). The organ doses were extracted from the DICOM header and ...

Annotation and characterization of lesions in breast tomosynthesis images.

Rapid adoption of artificial intelligence methods in breast imaging research emphasizes the need for large, appropriately curated image databases for ...

Improving visibility of small anatomical details on low and ultra-low dose computed tomography with artificial intelligence-based image reconstructions.

To assess how computed tomography (CT) image reconstruction techniques affect perceived diagnostic image quality at varying radiation dose levels in c...

Biosignatures of cognitive basic symptoms mark a distinct neurodevelopmental pathway to schizophrenia.

Efforts to predict schizophrenia risk using biological data have been hampered by the heterogeneity of current "clinical-high-risk" (CHR-P) criteria, ...

Deep learning based gestational age estimation from multi-view fetal brain magnetic resonance imaging.

BACKGROUND: Gestational age (GA) is essential for assessing fetal development, but conventional methods such as last menstrual period and ultrasound a...

The Performance of Artificial Intelligence in Classifying Molecular Markers in Adult-Type Gliomas Using Histopathological Images: Systematic Review.

BACKGROUND: Adult-type gliomas are among the most prevalent and lethal primary central nervous system tumors, where prompt and accurate diagnosis is e...

Breast density reporting in Aotearoa New Zealand: policy imperatives and research priorities.

Breast density influences both breast cancer risk and the sensitivity of mammographic screening. Several countries routinely notify women of their bre...

MuTriM: A multiscale deep learning model integrating longitudinal radiomics and pathomic features for predicting recurrence and adjuvant radiation benefit in breast cancer.

PURPOSE: We present MuTriM, a multimodal deep learning model integrating DCE-MRI and whole-slide pathology to predict survival and radiation benefit i...

Illuminating the Invisible: Translational Molecular Imaging in Cardiovascular Medicine.

Cardiovascular disease remains the predominant cause of global morbidity and mortality, with recent epidemiological trends indicating a resurgence in ...

Infrared thermography in the detection of arthritis: a systematic review of diagnostic performance compared with joint ultrasound.

Early detection of arthritis in autoimmune rheumatic diseases (ARDs) is critical to prevent irreversible damage. Joint ultrasound (US) offers high sen...

[Expert consensus on the clinical application of robotic-assisted bronchoscopy].

Robotic-assisted bronchoscopy (RAB) is an emerging diagnostic and interventional technology which integrates thin-slice CT-based virtual airway recons...

Re-defining the 3Rs of pre-clinical research in nuclear medicine.

Pre-clinical nuclear medicine occupies a distinctive position in biomedical research, simultaneously driving radioligand discovery for diagnostic imag...

Quantification, radiomics and artificial intelligence in infection imaging: Current status and future directions in nuclear medicine.

Nuclear medicine infection imaging has traditionally relied on semantic visual interpretation supported by simple semi-quantitative indices. While eff...

Predicting the Postoperative Recurrence Risk in Soft-Tissue Sarcomas of the Extremities and Trunk Using MRI-Based Nomogram.

RATIONALE AND OBJECTIVES: This study aims to develop a comprehensive nomogram for predicting the 3-year recurrence risk of patients with soft-tissue s...

A deep learning framework for breast cancer diagnosis using Swin Transformer and Dual-Attention Multi-scale Fusion Network.

Breast cancer is among the most prevalent cancers affecting women worldwide, and early detection through mammography is critical to reducing mortality...

Comparative Performance of 3 Artificial Intelligence Systems for Lung Nodule Characterization in Low-Dose Computed Tomography Screening.

PURPOSE: This study evaluates 3 artificial intelligence (AI) systems in detecting, characterizing, and classifying lung nodules on low-dose computed t...

From Multimodal Data to Clinical Insight: An Explainable Model for Preoperative Salivary Gland Lesion Diagnosis.

OBJECTIVE: To develop and validate a multimodal dual-step support vector machine model (SVM-DualNet) for the preoperative three-class classification o...

Artificial intelligence for diagnosis and triage in oral cancer: a clinician‑centered narrative review.

BACKGROUND: Early diagnosis of oral squamous cell carcinoma (OSCC) remains challenging, with survival largely stage-dependent at presentation. Artific...

Initial experience of deep learning reconstruction algorithm in lung kernel: clinical usefulness for lung nodules at ultra-low-dose protocol.

OBJECTIVES: TrueFidelity (TF), a deep learning image reconstruction algorithm that was originally available only in standard kernel, has recently beco...