Radiology

Deep Learning-Based Model for Breast Implant Classification in Ultrasonography: A Multi-Institutional Model Development and Validation Study.

BACKGROUND: The increasing prevalence of implant-based breast surgeries highlights a critical gap in patient knowledge regarding implant information, exacerbated by inadequate record-keeping and emerging safety concerns. OBJECTIVES: The authors of this study address the need for reliable implant identification methods by developing a deep learning model capable of classifying breast implants using...

Refining deep learning segmentation in gallium-68-prostate-specific membrane antigen-11 positron emission tomography: evaluation of small lesion filtering and intersection-over-union thresholds.

PURPOSE: Evaluate how small lesion filtering and different intersection-over-union (IoU) thresholds influence deep learning segmentation of Ga-68-PSMA-11 PET images in prostate cancer. METHODS: A 3D U-Net was trained on 115 patient scans with manual contours as ground truth. Performance was assessed at voxel, lesion and patient levels. Lesions less than 8 voxels (195 mm3) or less than 27 voxels (6...

Automated deep learning for detection and measurement of adrenal masses in contrast-enhanced abdominal CT.

OBJECTIVES: Adrenal tumors can be functional or malignant, yet they are often overlooked in abdominal CT. This study aimed to develop and validate a f...

Differentiation of focal liver lesions in contrast-enhanced ultrasound using a heuristic-guided hybrid machine-learning framework.

PURPOSE: Sonazoid contrast-enhanced ultrasound (CEUS) offers valuable diagnostic information on hepatic lesions, but it is time-consuming. In this stu...

Prospective Diagnostic Accuracy and Technical Feasibility of Artificial Intelligence-Assisted Rib Fracture Detection on Chest Radiographs: Observational Study.

BACKGROUND: Rib fractures are present in 10%-15% of thoracic trauma cases but are often missed on chest radiographs, delaying diagnosis and treatment....

Patient-level CAD-RADS scoring from coronary radiomic features.

Synthesizing coronary radiomic data to obtain a single patient-wise Coronary Artery Disease-Reporting and Data System (CAD-RADS) score remains challen...

Integrated predictive model for visceral pleural invasion in small NSCLC with high clinical utility.

This study aims to develop and validate a multi-feature integrated imaging fusion (MIIF) model, incorporating deep learning, radiomics features, and c...

Non-invasive liquid biopsy based on metabolomic profiling improves diagnosis and early warning of severe acute pancreatitis.

To identify novel diagnostic biomarkers for acute pancreatitis (AP) and facilitate the early prediction of severe AP (SAP), this investigation charact...

Multi-omics deep learning improves FDG PET-CT-based long-term prognostication of breast cancer.

[18F] fluorodeoxyglucose positron emission tomography - computed tomography (FDG PET-CT) is increasingly used for staging of breast cancer in the prim...

Benchmark White Matter Hyperintensity Segmentation Methods Fail on Heterogeneous Clinical MRI: A New Dataset and Deep Learning-Based Solutions.

Existing automated methods for white matter hyperintensity (WMH) segmentation often generalize poorly to heterogeneous clinical MRI due to variability...

Handcrafted vs. Deep Radiomics vs. Fusion vs. Deep Learning: A Comprehensive Review of Machine Learning -Based Cancer Outcome Prediction in PET and SPECT Imaging.

Machine learning (ML), particularly deep learning (DL) and radiomics-based approaches, has emerged as a powerful tool for cancer outcome prediction us...

Radiomics-based gradient boosting model on contrast-enhanced MRI for non-invasive prediction of epidermal growth factor receptor expression and therapeutic response to EGFR-targeted antibody-drug conjugates in high-grade glioma organoid models.

BACKGROUND: Epidermal growth factor (EGF) and its receptor EGF(EGFR) play crucial roles in glioblastoma (GBM) prognosis. However, non-invasive assessm...

Artificial intelligence revolution in shoulder magnetic resonance imaging: current evidence and future directions for rotator cuff diagnosis.

BACKGROUND: Rotator cuff tears (RCTs) are a leading cause of shoulder pain. Magnetic resonance imaging (MRI) is the gold standard for diagnosis, but i...

Analysis of Upper Airway Morphology Using Four-Dimensional Dynamic MRI With Active Deep Learning-Based Automatic Segmentation.

BACKGROUND: Upper-airway morphology changes during breathing can be captured with cine 4D MRI. Active-learning nnU-Net reduces manual labeling while m...

Development and Validation of a Machine Learning Model for Dementia Staging in a Heterogeneous Cognitive Impairment Cohort Using Multimodal Features.

RATIONALE AND OBJECTIVES: With the emergence of disease-modifying therapies, precise staging of dementia is urgent. This study aimed to develop a mach...

Novel "No-Lo" imaging techniques to minimize intraoperative radiation exposure in vascular and endovascular surgery.

INTRODUCTION: The rapid expansion in endovascular techniques has placed vascular surgeons among those most exposed to occupational medical radiation. ...

A navigation-guided 3D breast ultrasound scanning and reconstruction system for automated multi-lesion spatial localization and diagnosis.

Handheld ultrasound (HHUS) is indispensable for breast cancer screening but remains compromised by operator-dependent acquisition, subjective 2D inter...

Multimodal sparse fusion transformer network with spatio-temporal decoupling for breast tumor classification.

Accurate analysis of tumor morphology, vascularity, and tissue stiffness under multimodal ultrasound imaging plays a critical role in the diagnosis of...

Radiomics for Differentiating Autoimmune-Related Interstitial Lung Disease from Idiopathic Pulmonary Fibrosis: A Narrative Review.

Interstitial lung disease (ILD) represents a wide variety of lung diseases, commonly resulting in irreversible changes with worsening quality of life ...

Artificial Intelligence analysis of lesion dynamics and brain volume in patients with multiple sclerosis.

BACKGROUND: Quantitative MRI markers increasingly complement conventional clinical assessment in multiple sclerosis (MS). Artificial intelligence (AI)...