Radiology

Large language models accurately extract aortic information from abdominal imaging reports in a large, real-world database.

OBJECTIVE: Maintaining robust surveillance programs for abdominal aortic aneurysms (AAAs) is important, but these programs are expensive and labor-intensive, typically requiring manual data review by trained health care professionals. Studies have shown that natural language processing software can assist in these functions, but each task-specific algorithm requires human-directed training before ...

Evaluation of Approved AI-based Brain Aneurysm Detection Software in Clinical Practice: Comparison with Radiologist Assessment and Image Re-review.

PURPOSE: This study evaluated the performance of artificial intelligence (AI)-based brain aneurysm detection software in clinical settings, aiming to assess its utility as a supportive tool for radiologists. Metrics included sensitivity, positive predictive value (PPV), F1 score, and false positives (FPs) per case. METHODS: A retrospective analysis of 442 cases (March 2023-August 2024) compared AI...

Anterior segment optical coherence tomography in uveitis: A comprehensive review of clinical applications, diagnostic insights, and future directions.

Anterior segment optical coherence tomography (AS-OCT) is emerging as an essential tool in the diagnosis and monitoring of uveitis. Offering noninvasi...

Automatically Measuring Kidney, Liver, and Cyst Volumes in Autosomal Dominant Polycystic Kidney Disease.

KEY POINTS: TraceOrg is a web-based tool that automatically labels kidney, liver, and cysts, reporting volumes and Mayo Imaging Classification. Extern...

Deep learning-based combined noise reduction and contrast enhancement for post-neoadjuvant pancreatic cancer CT: does improved image quality translate to better resectability assessment?

PURPOSE: To evaluate whether deep learning-based combined noise reduction and contrast enhancement reconstruction (DLR) improves image quality and res...

Applications and clinical translation of artificial intelligence in CBCT-based detection of endodontic lesions: a scoping review.

BACKGROUND: Artificial intelligence (AI) especially deep learning (DL) has significantly revolutionized medical image analysis, which include dental d...

Development and Deployment of a Machine Learning Model to Triage the Use of Prostate MRI (ProMT-ML) in Patients With Suspected Prostate Cancer.

BACKGROUND: Access to prostate MRI remains limited due to resource constraints and the need for expert interpretation. PURPOSE: To develop machine lea...

A multi-task deep learning framework for intraoperative diagnosis of thyroid cancer metastasis using whole slide images.

BACKGROUND: Lymph node metastasis (LNM) is a critical prognostic indicator in papillary thyroid carcinoma (PTC), significantly influencing surgical de...

Temporal dynamics and predictive modeling of oral epithelial dysplasia features during carcinogenesis.

OBJECTIVE: To investigate the temporal evolution and predictive value of individual histopathological features of oral epithelial dysplasia (OED) duri...

Anatomical, physiological and inflammatory characterization of nonculprit vessels in patients undergoing primary PCI for ST-elevation myocardial infarction in the presence of multivessel disease: Rationale and design of the PICNIC study.

BACKGROUND: Up to 50% of patients presenting with ST-elevation myocardial infarction (STEMI) have multivessel coronary artery disease (CAD). Randomize...

Spin and Gradient Multiple Overlapping-Echo Detachment Imaging (SAGE-MOLED): Highly Efficient T2, T 2 * $$ {T}_2^{\ast } $$ , and M0 Mapping for Simultaneous Perfusion and Permeability Measurements.

PURPOSE: Combined spin- and gradient-echo EPI (SAGE-EPI) offers advantages in tissue quantification and dynamic imaging but suffers from low spatial r...

Fully automated multi-sequence detection and alignment of focal liver lesions in dynamic contrast-enhanced MRI.

OBJECTIVES: To validate an artificial intelligence (AI) method for fully automated detection and alignment of focal liver lesions (FLLs) in multi-sequ...

Deep learning for accurate tumour volume measurement and prediction of therapy response in paediatric osteosarcoma.

OBJECTIVES: To assess treatment response in osteosarcoma, two automated convolutional neural networks (CNNs) were developed to quantify tumour volumes...

Combination of artificial intelligence and chest computed tomography to assess bone mineral density.

OBJECTIVES: To evaluate the diagnostic accuracy of artificial intelligence-assisted opportunistic chest CT for osteoporosis/osteopenia screening in a ...

Precision medicine in orthopaedics: A review of current technologies and future directions.

PURPOSE: To synthesise the paradigm shift towards precision medicine in orthopaedics, where individual anatomical, biomechanical, molecular and kinema...

BreAST-U²Net: A Twin-Stream U2Net with Attention-based Tumor Fusion for 2-D Tumor Segmentation in Automated Breast Ultrasound.

BACKGROUND: Automated breast ultrasound (ABUS) shows potential for breast cancer diagnosis but faces tumor segmentation challenges due to limited anno...

Artificial intelligence-based segmentation of small renal masses: a multi-center, multi-scanner, multi-sequence study.

OBJECTIVES: This study aims to develop an artificial intelligence (AI)-based automated segmentation method for small renal masses (SRMs) using multi-c...

Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection.

OBJECTIVES: Parametric tissue mapping enables quantitative cardiac tissue characterization but is limited by inter-observer variability during manual ...

Fetal cardiac remodeling in second trimester in pregnancies with pre-eclampsia and/or fetal growth restriction: deep-learning-based approach using population-wide data.

OBJECTIVE: Pre-eclampsia (PE) and fetal growth restriction (FGR) have been shown to impact fetal cardiac remodeling in the third trimester and postnat...

AI-Quantified ¹¹C-MET PET/CT bone marrow metabolic activity for prognostic assessment in newly diagnosed multiple myeloma.

PURPOSE: To develop and validate an AI method for automated quantification of whole-skeleton bone marrow (BM) metabolic activity using Carbon 11 (11C)...