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Artificial Intelligence Predicts Multiclass Molecular Signatures and Subtypes Directly From Breast Cancer Histology: a Multicenter Retrospective Study.
Int J Surg
January 2025
Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict classical biomarkers, immunotherapy-associated gene signatures, and prognosis-associated subtypes directly from hematoxylin and eosin stained histopathology images. BBMIL showed the best performance among comparative algorithms on the prediction of classical biomarkers, immunotherapy related gene signatures, and subtypes.
View Article and Find Full Text PDFBackground: Transcranial Electrical Stimulation (TES), Temporal Interference Stimulation (TIS), Electroconvulsive Therapy (ECT) and Tumor Treating Fields (TTFields) are based on the application of electric current patterns to the brain.
Objective: The optimal electrode positions, shapes and alignments for generating a desired current pattern in the brain vary between persons due to anatomical variability. The aim is to develop a flexible and efficient computational approach to determine individually optimal montages based on electric field simulations.
Improving accuracy in the estimation of probable dementia in racially and ethnically diverse groups with penalized regression and transfer learning.
Am J Epidemiol
January 2025
Department of Social Sciences, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
Algorithmic estimations of dementia status are widely used in public health and epidemiological research, however, inadequate algorithm performance across racial/ethnic groups has been a barrier. We present improvements in the accuracy of group-specific "probable dementia" estimation using a transfer learning approach. Transfer learning involves combining models trained on a large "source" dataset with imprecise outcome assessments, alongside models trained on a smaller "target" dataset with high-quality outcome assessments.
View Article and Find Full Text PDFPredicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.
BMC Emerg Med
January 2025
Emergency department, CHR Metz-Thionville, Metz, 57000, France.
Introduction: Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the ED.
Aim: The main objective of this study was to build and test a prediction tool for ED admissions using artificial intelligence.
Aboveground biomass estimation in a grassland ecosystem using Sentinel-2 satellite imagery and machine learning algorithms.
Environ Monit Assess
January 2025
School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, 2000, South Africa.
The grassland ecosystem forms a critical part of the natural ecosystem, covering up to 15-26% of the Earth's land surface. Grassland significantly impacts the carbon cycle and climate regulation by storing carbon dioxide. The organic matter found in grassland biomass, which acts as a carbon source, greatly expands the carbon stock in terrestrial ecosystems.
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