Radiology

The impact of scanner domain shift on deep learning performance in medical imaging: an experimental study.

PURPOSE: Medical images acquired using different scanners and protocols can differ substantially in their appearance. This phenomenon, scanner domain shift, can result in a drop in the performance of deep neural networks which are trained on data acquired by one scanner and tested on another. This significant practical issue is well-acknowledged, however, no systematic study of the issue is availa...

Beyond coronary artery calcium score: can lipoprotein(a) and artificial intelligence-coronary computed tomography angiography unmask 'invisible' residual cardiovascular risk?

The Role of Artificial Intelligence Algorithms in Challenging Diagnostic Cases - Between Potential and Real Clinical Support.

INTRODUCTION: Extranodal natural killer/T-cell lymphoma, nasal type (ENKTL), is a rare EBV-associated malignancy characterized by destructive tumors i...

In-hospital electronic monitoring system approaches to epidemiologic investigation and predictive modeling of contrast-induced acute kidney injury.

Contrast-induced acute kidney injury (CI-AKI), the third most common cause of hospital-acquired kidney injury, is associated with poor clinical outcom...

A novel serum metabolite classifier for identifying Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) integrating metabolomics and machine learning.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) presents a growing global health burden, while reliable non-invasive biom...

Radiomics for predicting microsatellite instability-high status in colorectal cancer: a systematic review and meta-analysis.

OBJECTIVE: Microsatellite instability (MSI) has emerged as a key predictive biomarker for chemotherapy and immunotherapy response, and as a prognostic...

A phantom study on the evaluation of the YOLOv8 deep learning model in the detection of tooth ankylosis on cone-beam computed tomography images.

This study aimed to assess the performance of a deep learning algorithm for detecting tooth ankylosis on cone-beam computed tomography (CBCT) scans. A...

The Negative Impact of a Non-Therapeutic Laparotomy in Patients With Inoperable Colorectal Peritoneal Metastases.

BACKGROUND: The peritoneum is the third most prevalent location for metastases of colorectal cancer. In patients with resectable disease, cytoreductiv...

Retinal imaging and supervised learning predict hospitalizations, kidney, and heart-lung damage in sickle cell disease.

Sickle cell disease (SCD) is a single-gene illness which causes painful vaso-occlusion, debilitating organ damage, and early mortality. Its clinical c...

Evaluating the interpretation of neck CT/MRI scans by ChatGPT-4V for detecting primary oropharyngeal squamous cell carcinoma: An exploratory study.

OBJECTIVES: Early and accurate detection of head and neck squamous cell carcinoma and the subset of oropharyngeal squamous cell carcinoma (OPSCC) is e...

Deep Learning for Pretreatment Imaging-Based Tumor and Nodal Classification in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Network Meta-Analysis.

BACKGROUND: Accurate pretreatment assessment of the extent of tumor invasion and status of cervical lymph node metastasis is essential for staging and...

Multi-Institutional Annotated Multiparametric MRI Dataset of Pediatric High-Grade Gliomas.

Pediatric brain tumors are rare and still represent the most common solid tumors in children and the leading cause of cancer-related mortality in the ...

Deep Learning for Survival Prediction in Glioblastoma: Time-dependent Model Interpretability Using MRI, Clinical, and Molecular Data.

Purpose To develop a multimodal model for survival prediction and time-dependent model interpretability in glioblastoma by integrating preoperative MR...

Optimizing Non-invasive Fractional Flow Reserve Estimation with Machine Learning-Enhanced 1D Hemodynamic Modeling.

Patient-specific computational models exhibit strong concordance with invasively measured fractional flow reserve (FFR)-the clinical gold standard for...

Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.

Abnormalities in cardiac wall motion are strong predictors of cardiovascular risk, making their accurate detection essential for early diagnosis and e...

Multimodal AI approach combining deep learning imaging and clinical machine learning for pancreatic cancer detection.

Pancreatic ductal adenocarcinoma (PDAC) has poor prognosis due to late diagnosis, limitations of computed tomography (CT) imaging, and low accuracy of...

Deep learning cone-beam computed tomography image segmentation for the 3D visualization of mandibular infraosseous periodontal defects.

BACKGROUND: The accurate assessment of infraosseous periodontal defects is crucial for effective diagnosis and treatment planning. Cone-beam computed ...

Clinical validation and implementation of mSTOP, a machine learning-based, longitudinal prediction model for the early identification of non-small cell lung cancer patients who not benefit from immune checkpoint inhibitor treatment.

OBJECTIVES: There has been a lot of interest in the field of laboratory medicine regarding the use of machine learning (ML)-based prediction models. T...

The impact of lipoprotein(a) on coronary atherosclerotic plaque phenotype in primary prevention.

AIMS: Lipoprotein(a) (Lp[a]) is a causal risk factor for cardiovascular events. However, the effect of Lp(a) on coronary plaque composition and high-r...

Detection of Interpretable and Fine-Grained Brain Tumor Magnetic Resonance Imaging Based on Progressive Pruning: Machine Learning Model Development and Validation Study.

BACKGROUND: Brain tumor is one of the most malignant diseases of the central nervous system, and early accurate detection is of great significance for...