Leukemia

Gaps in Artificial Intelligence Research for Rural Health in the United States: A Scoping Review

Artificial intelligence (AI) has impacted healthcare at urban and academic medical centers globally. The current focus on AI deployments in urban areas and the history of US urban-rural digital divides raises concerns that the promise of AI may not be realized in rural communities. This may exacerbate well-documented health disparities. Without the benefits of AI-driven improvements in patient out...

AI-based Hepatic Steatosis Detection and Integrated Hepatic Assessment from Cardiac CT Attenuation Scans Enhances All-cause Mortality Risk Stratification: A Multi-center Study

Hepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under-diagnosed in patients with suspected or known coronary artery disease. We used artificial intelligence (AI) to automatically quantify hepatic tissue measures for identifying HS from CT attenuation correction (CTAC) scans during myocardial perfusion imaging (MPI) and evaluate their added prognostic value for...

Network-Based Stratification Refines Stratification of Intermediate-Risk Acute Myeloid Leukemia Samples

The European LeukemiaNet (ELN) risk stratification of acute myeloid leukemia (AML) uses genetic and molecular markers to categorize patients. However,...

Personalized, closed-loop deep brain stimulation for chronic pain

Chronic pain is a major healthcare problem associated with maladaptive brain circuit changes - many patients are unresponsive to all available therapi...

Risk Prediction Modelling of 30-day all-cause mortality following percutaneous coronary intervention in an Australian population: Leveraging Machine Learning

Pre-procedural risk prediction of 30-day all-cause mortality after percutaneous coronary intervention (PCI) aids in clinical decision-making and bench...

Development, implementation, and validation of an open-source Federated Learning platform to accelerate innovation and boost personalized medicine in rare and ultra-rare haematological diseases: an initiative by GenoMed4All Consortium

Rare haematological diseases (RHD) pose significant clinical challenges due to their heterogeneity, limited patient populations, and fragmented datase...

Predicting Near-term Mortality in Heart Failure: External Validation of Electronic Health Record-Based Deep Learning Model

The dire consequences of heart failure (HF) patient non-response to guideline directed medical therapy often fuel early, non-selective referral for su...

Acute myeloid leukemia risk stratification in younger and older patients through transcriptomic machine learning models

Acute Myeloid Leukemia (AML) is a genetically and clinically heterogeneous disease that can develop at any age. While AML incidence increases with age...

Development of a dynamic counterfactual risk stratification strategy for newly diagnosed acute myeloid leukemia patients treated with venetoclax and azacitidine

The objective of this study was to develop a flexible risk stratification strategy for Acute Myeloid Leukemia (AML) that is specific for venetoclax pl...

Artificial Intelligence-Guided Molecular Determinants of PI3K Pathway Alterations in Early-Onset Colorectal Cancer Among High-Risk Groups Receiving FOLFOX

Early-onset colorectal cancer (EOCRC), defined as diagnosis before age 50, is rising rapidly and disproportionately affects high-risk populations, par...

Integration of Gene Expression and Digital Histology to Predict Treatment-Specific Responses in Breast Cancer

Deep learning models applied to digital histology can predict gene expression signatures (GES) and offer a low-cost, rapidly available alternative to ...

Optical Microscopy Predictions of Focal Recurrence in Glioblastoma

A hallmark of glioblastoma (GBM) is disease recurrence, which occurs in all patients despite tumor resection, radiation, and chemotherapy. A critical ...

Evaluating Accuracy and Reasoning Capabilities of Large Language Models for Acute Ischemic Stroke Management

Acute ischemic stroke (AIS) management has evolved substantially over the past two decades, with mechanical thrombectomy adding complexity that requir...

Causal Machine Learning Analysis of All-Cause Mortality in Japanese Atomic-Bomb Survivors

The health consequences of ionizing radiation have long been studied, yet significant uncertainties remain, particularly at low doses. In particular, ...

Predicting Acute Cerebrovascular Events in Stroke Alerts Using Large-Language Models and Structured Data

Acute stroke alerts are often activated for non-cerebrovascular conditions, leading to false positives that strain clinical resources and promote diag...

Genomic Classification of Acute Lymphoblastic Leukemia Using AI: Towards Personalized Medicine

Acute lymphoblastic leukemia is a highly heterogeneous hematologic malignancy that poses significant challenges for clinicians in terms of early detec...

Leveraging transfer learning from Acute Lymphoblastic Leukemia (ALL) pretraining to enhance Acute Myeloid Leukemia (AML) prediction

We overcome current limitations in Acute Myeloid Leukemia (AML) diagnosis by leveraging a transfer learning approach from Acute Lymphoblastic Leukemia...

Artificial Intelligence Models for Predicting Molecular Pathway Activity in Spinal Cord Injury: A Systematic Review

Spinal cord injury (SCI) remains a devastating neurological condition with high global incidence and minimal curative options. The pathobiology is mul...

A Multi-Task Deep Learning Model for Pediatric Echocardiography Analysis

Congenital heart defects afflict nearly 1% of all births worldwide. While deep learning algorithms have shown significant promise in automating and im...

Proteomic signatures and machine learning based-prediction models for cardiovascular risk in survivors of myocardial infarction

Survivors of myocardial infarction (MI) are still at risk for adverse long-term outcomes such as all-cause mortality, heart failure (HF), and ischemic...