Lymphoma

Zero-Shot Unsupervised Motion Estimation for Motion-Corrected Cardiac T1 Mapping.

OBJECTIVE: Cardiac quantitative MRI (qMRI) is a powerful imaging technique for diagnosing pathologies such as diffuse myocardial fibrosis. One main challenge is cardiac motion, which requires synchronization of data acquisition with the heartbeat, leading to long scan times. We present a novel deep learning-based image registration method for cardiac qMRI that enables non-rigid motion correction o...

From Pixels to Prediction: Reviewing the Role of Artificial Intelligence in Body Composition Analysis.

Evaluation of body composition (BC) is a set of biomarkers, including fat, muscle and bone, that allows the quantification of an individual's composition at different levels of complexity (whole body, tissues, macroscopic and microscopic). Recently, BC has gained medical interest due to its impact on health outcomes in both oncologic and non-oncologic conditions. BC parameters are also valuable fo...

Prevalence and Predictors of Non-Exclusive Breastfeeding at Hospital Discharge in Uruguay.

BACKGROUND: The prescription of infant formula during postpartum hospitalization is one of several factors that influence breastfeeding. RESEARCH AIMS...

Automated interictal epileptic spike detection from simple and noisy annotations in MEG data.

In drug-resistant epilepsy, presurgical evaluation can be considered for suitable candidates. Magnetoencephalography (MEG) has been shown to be an eff...

Relationship between ileocolic arterial stump length and lymph node yield - a retrospective study using machine learning.

PURPOSE: In right-sided colon cancer surgery, ileocolic artery stump length may reflect the extent of mesenteric resection and lymph node harvest. Thi...

Machine Learning Accelerated Non-Adiabatic Molecular Dynamics Elucidates Local Polarization Effects on Non-radiative Recombination in Halide Perovskites.

Non-radiative recombination is a critical factor limiting the optoelectronic performance of halide perovskites, yet how local polarization induced by ...

Beyond prediction: explainable machine learning for ferritin estimation from complete blood count.

BACKGROUND: Serum ferritin is a primary marker of body iron stores; however, its diagnostic utility is limited by cost and non-specific elevation duri...

Fully automatic left ventricle segmentation in [Formula: see text]Rb PET/CT Using a semi-supervised nnU-net.

BACKGROUND: Quantification of myocardial blood flow (MBF) with [Formula: see text]Rb PET/CT requires accurate delineation of the left ventricle (LV). ...

Prediction of antibody non-specificity using protein language models and biophysical parameters.

The development of therapeutic antibodies requires optimizing target binding affinity and pharmacodynamics, while ensuring high developability potenti...

[MRI-based deep learning model for preoperative prediction of urothelial carcinoma with variant histology of bladder: a retrospective, multicenter study].

Objectives: To develop a deep learning model based on magnetic resonance imaging (MRI) for the preoperative prediction of urothelial carcinoma with va...

Exploring the value of peritumoral brain zone for classification of two malignant brain tumors based on the MRI interpretable models.

OBJECTIVES: This study aims to develop and validate a novel multimodal interpretable artificial intelligence model capable of fusing radiomics feature...

Recent Updates on Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration and Its Clinical Implications.

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is the standard minimally invasive modality for mediastinal staging in no...

Machine learning to predict occult neck node metastasis in early oral squamous cell carcinoma - a comparison of models to risk stratify patients undergoing a sentinel lymph node biopsy.

BACKGROUND: Sentinel node biopsy (SNB) provides pathological staging of the neck in T1/T2 node-negative oral squamous cell carcinoma (OSCC). Up to 85%...

Beyond target lesions: Prognostic value of longitudinal AI-derived whole-body [1⁸F]FDG PET/CT metrics in metastatic melanoma.

PURPOSE: To investigate the prognostic value of an artificial intelligence (AI)-based semi-automated tool for longitudinal whole-body quantification o...

Machine learning-driven evaluation of mechanical and microstructural properties of agro-waste-derived geopolymer concrete.

The growing demand for sustainable construction materials has accelerated research into eco-friendly alternatives to traditional Portland cement. This...

AI-based histopathology analysis predicts checkpoint inhibitor response in advanced melanoma and identifies patterns associated with response.

INTRODUCTION: Readily available predictive biomarkers for immune checkpoint inhibitor (ICI) response in advanced melanoma are limited. This study eval...

Leveraging the non-contrast CT component of PET/CT: an AI-driven delta-radiomics approach to monitor treatment response in metastatic breast cancer.

PURPOSE: 18 F-FDG PET/CT is the standard modality for monitoring treatment response in metastatic breast cancer. This study aims to evaluate the predi...

Uncovering subgroup heterogeneity in dyslexia intervention outcomes using explainable machine learning.

Identifying heterogeneity within literacy intervention outcomes can inform more targeted strategies for dyslexia remediation. Based on prior work that...

Development of a model for design radiation shielding composite aprons using machine learning.

This study presents the development and evaluation of a novel lead-free composite for radiation shielding, designed using an artificial neural network...

Prediction of groundwater level in shallow and complex aquifers using hybrid soft computing models and metaheuristic algorithms.

Predicting groundwater level dynamics in shallow, unconfined aquifers remain a persistent challenge due to their high sensitivity to heterogeneous rec...