Radiology

Clinical validation pipeline of a deep learning model for segmenting and quantifying intracranial and ventricular volumes on computed tomography.

The field of radiology is experiencing a surge in demand due to advances in medical imaging, particularly in techniques such as magnetic resonance imaging and computed tomography (CT). However, the interpretation of these scans relies heavily on the availability of experts, which is challenging in resource-limited regions. Recent advances in artificial intelligence and deep learning offer promisin...

Electrocochleographic findings in patients with Ménière's disease: associations with hearing thresholds and endolymphatic hydrops.

OBJECTIVE: To evaluate the associations of extratympanic electrocochleography (ECochG) parameters with hearing thresholds and gadolinium-enhanced magnetic resonance imaging (MRI)-confirmed endolymphatic hydrops (EH) in patients with Ménière's disease (MD). METHODS: In this prospective cross-sectional study, patients with definite MD were enrolled between March and June 2024. All underwent pure-ton...

[Dual-axis evolution model of physical intervention-bioprinting depth for in situ 3D bioprinting in vivo and research perspectives].

In situ 3D bioprinting in vivo is leading a profound paradigm shift of manufacturing in regenerative medicine. However, to achieve the leap from mere ...

The promise of quantitative approaches to computed tomography imaging in pulmonary sarcoidosis.

PURPOSE OF REVIEW: Pulmonary sarcoidosis is characterized by marked radiologic heterogeneity and limited reproducibility of visual high-resolution com...

An unsupervised physics-informed neural deconvolution framework for ultrasound imaging.

Objective. Point spread function (PSF) and clutter noise are primary causes of ultrasound quality degradation. Physical deconvolution can reduce PSF e...

Preventing fragility fractures: AI-based opportunistic osteoporosis detection in routine CT ​.

UNLABELLED: This study shows that an artificial intelligence tool can identify patterns of reduced bone mineral density in routine CT scans that would...

Brain cone-beam CT image quality improvement using a deep-learning-based denoising method: a multicenter retrospective study.

PURPOSE: Deep learning (DL) denoising may improve cone-beam CT (CBCT) image quality for point-of-care stroke assessment in the interventional suite. T...

Multimodal artificial intelligence in prostate cancer: integrating multiparametric MRI with clinicopathological, molecular, and functional imaging data.

Multimodal artificial intelligence (AI) is reshaping prostate cancer imaging by moving beyond MRI-only algorithms toward models that integrate multipa...

PRECISE v2 in active surveillance prostate MRI: toward a structured lexicon for longitudinal interpretation.

Active surveillance increasingly incorporates prostate MRI for longitudinal assessment of lesion stability or progression, yet terminology for serial ...

AI-Assisted Tumor Boundary Delineation via Targeted Ultrasmall Iron Oxide Nanoprobe for High-Contrast HER2-Positive Tumor Imaging.

Breast cancer continues to be a leading cause of cancer-related mortality in women globally, where precise diagnosis and clear tumor demarcation are c...

Artificial intelligence applications in radiology: the new frontier in interstitial lung diseases.

BACKGROUND: Interstitial lung disease (ILD) comprises a heterogeneous group of disorders with diverse clinical behaviors, for which early diagnosis, a...

Fetal brain MRI analysis: Towards clinical translation and impact.

While slice-to-volume registration and super-resolution reconstruction laid the foundation for motion-corrected 3D T2-weighted fetal brain magnetic re...

Radiologic, Pathologic, and Deep Learning Predictors of Response to Immune Checkpoint Blockade in Renal Cell Carcinoma Patients Undergoing Post-Treatment Nephrectomy.

Response assessment of primary kidney tumors in the consolidation cytoreductive and neoadjuvant settings offers a unique opportunity to inform postope...

Deep Learning-guided Late-Phase Dynamic Frame Prediction for Accelerated Patlak Analysis in 18F-FDG-PET Brain Imaging.

PURPOSE: Dynamic 18F-FDG-PET enables the quantitative assessment of cerebral glucose metabolism but requires prolonged acquisition times, which pose c...

Artificial Intelligence Optimizes Efficiency and Sonographer Workflow in Routine Adult Transthoracic Echocardiography: A Mixed-Methods Time-Motion Study.

Integrating multimodality CT-MRI deep learning with clinical-radiological features for preoperative hepatocellular carcinoma grading: a multicenter study.

OBJECTIVE: To develop and externally validate an integrated model that combines multimodality CT-MRI deep learning with clinical and radiological feat...

MSCT-Trans: A Multi-scale Convolutional Neural Network Token Transformer for Interpretable Ultrasound Image Classification.

Automated ultrasound image classification is increasingly important for clinical decision support in breast, thyroid and fetal screening. However, dep...

Deep Learning-Based Automated Segmentation of the Suprascapular Nerve and Adjacent Structures in Dynamic Supraclavicular Ultrasound.

OBJECTIVE: The suprascapular nerve (SSN) provides major motor and sensory innervation to the shoulder. Its accurate identification on ultrasound is ch...

Multiparametric MRI Habitat Imaging for Preoperative Assessment of Ki-67 Proliferation Index in Meningiomas: A Multicenter Study.

BACKGROUND: Preoperative assessment of meningioma proliferative activity relies on the postoperative Ki-67. Habitat imaging captures proliferative var...

Intramuscular Fat Assessed by AI-Assisted Muscle Ultrasound: Association With Cardiometabolic Risk Factors and Diabetic Nephropathy in Diabetes Mellitus.

AIMS: Intramuscular fat (IMF) is increasingly recognized as a marker of ectopic adiposity and adverse cardiometabolic outcomes. Artificial intelligenc...