Radiology

Adverse Pregnancy Outcomes in Women With Immune Abnormalities: Machine Learning Model Development and Validation Using First-Trimester Sonographic Features.

BACKGROUND: The maintenance and progression of pregnancy rely on immune homeostasis at the maternal-fetal interface. However, pregnancy complicated by autoimmune abnormalities can disrupt this balance and significantly increase the risk of adverse pregnancy outcomes (APOs). OBJECTIVE: This study aimed to (1) develop an interpretable predictive tool for APOs in patients with immune abnormalities an...

Clinical Implementation of AI for Pulmonary Embolism Detection in over 30,000 CT Pulmonary Angiography Examinations.

Purpose To quantify postimplementation concordance between a U.S. Food and Drug Administration-cleared artificial intelligence (AI) tool and AI-informed radiologists for pulmonary embolism (PE) detection on CT pulmonary angiography (CTPA), with real-time adjudication of discordances. Materials and Methods A PE AI tool (AIDOC, Tel Aviv, Israel) was retrospectively implemented in the clinic across a...

Artificial Intelligence for Digital Breast Tomosynthesis Screening with and without prior Examinations in BreastScreen Norway.

Purpose To evaluate the performance of an artificial intelligence (AI) model for digital breast tomosynthesis (DBT) with and without prior screening e...

Multimodality Imaging in Myocarditis: Integrating Etiology, Diagnosis, and Risk Stratification.

PURPOSE OF REVIEW: Myocarditis presents with heterogenous clinical manifestations and remains diagnostically challenging due to nonspecific biomarkers...

Dual-conditioned diffusion model with anatomical guidance for geometric distortion correction in prostate MRI.

OBJECTIVE: Geometric distortion from susceptibility artifacts in diffusion-weighted imaging (DWI) degrades anatomical fidelity and complicates clinica...

The Integration of Artificial Intelligence in Radiation Medical Physics: Insights from an International Survey with Regional Variability.

BACKGROUND: Artificial intelligence (AI) is considered to be a leading technology in radiation medical physics, which has the potential for improving ...

Deep learning-based intraluminal gas modeling for anatomically accurate synthetic CT in MRI-based radiation therapy.

Stochastic bowel and rectal gas complicates MRI/CT deformable image registration (DIR) for synthetic CT (sCT) generation, requiring manual corrections...

Canadian radiology: 2026 update.

Canadian radiology continues to produce scholarship that is technically sophisticated, clinically relevant, and increasingly attentive to the wider sy...

A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS).

The integration of artificial intelligence (AI) into clinical decision support (CDS) holds promise for proactive, personalized, and precision care. Ho...

Development and validation of a deep learning model for liver shear stiffness regression using abdominal multiparametric MRI across multiple sites and vendors.

OBJECTIVE: Liver stiffness measurement is important for assessing chronic liver disease (CLD). MR elastography (MRE) requires specialized hardware and...

YOLO-LS: a novel deep learning framework for brain tumor segmentation in Magnetic Resonance Imaging.

Brain tumors exhibit high heterogeneity in morphology, texture, and location, making accurate recognition and segmentation critical for clinical diagn...

UveAI: clinic-ready scoring of retinal inflammation in uveitis on widefield fluorescein angiography using AI.

Retinal inflammation is a key determinant of visual prognosis in uveitis, yet its assessment on fluorescein angiography remains subjective, labor-inte...

Development and validation of a versatile foundation model for cine cardiac magnetic resonance image analysis.

BACKGROUND: Cardiac magnetic resonance imaging is central to cardiovascular diagnosis and management, yet extracting key clinical measurements remains...

Deep residual network fusing CT images and clinical variables to predict lung adenocarcinoma aggressiveness.

BACKGROUND: Lung adenocarcinoma presenting as ground-glass nodules (GGNs) comprises three invasive subtypes (adenocarcinoma in situ [AIS], minimally i...

Application of a multimodal MRI model integrating radiomics and habitat features for predicting glioma pathology and prognosis.

BACKGROUND: Accurate grading and prognostic assessment of glioma requires integrating key molecular biomarkers, including IDH mutation status and the ...

Shaping the future of postoperative recurrence in Crohn's disease: personalised approaches with AI-enabled imaging and multi-omics.

Postoperative recurrence (POR) is a major challenge in the long-term management of Crohn's disease (CD), affecting up to 70% of patients within the fi...

Intratumoral and peritumoral radiomics based on multi-parametric magnetic resonance imaging for predicting microsatellite instability in endometrial cancer.

PURPOSE: This study aimed to evaluate the utility of intratumoral and peritumoral radiomics derived from multi-parametric magnetic resonance imaging f...

Real-world insights into coronary CTA prognostication: value of semiquantitative scores.

PURPOSE: Several semiquantitative coronary computed tomography angiography (CCTA) scores including different parameters describing stenosis degree, pl...

Age-adjusted neurofilament light chain cutoffs for diagnosing neurodegenerative dementia: a two-threshold machine learning approach.

Clinical implementation of neurofilament light chain (NfL), a biomarker of neurodegeneration, remains challenging due to absence of reliable cutoffs a...

Artificial Intelligence (Pattern Recognition) in Musculoskeletal Imaging: The Future or Hype?

Artificial intelligence and automated pattern recognition, in particular, have been described as the next frontier in musculoskeletal imaging. However...