Latest AI and machine learning research in oncology/hematology for healthcare professionals.
Heterogeneity in chronic malignancies raises an increasing interest for the integration and study of predictive models. This study presents a machine learning model approach to predict outcomes and improve their trustworthiness in multi-factorial diseases with highly heterogeneous outcomes, like Chronic Lymphocytic Leukemia (CLL). We incorporated Conformal Prediction to quantify our models uncerta...
Modern generative artificial intelligence techniques like retrieval-augmented generation (RAG) may be applied in support of precision oncology treatment discussions. Experts routinely review published literature for evidence and recommendations of treatments in a labor-intensive process. A RAG pipeline may help reduce this effort by providing chunks of text from these publications to an off-the-sh...
Introduction: Electrical impedance spectroscopy (EIS) has recently developed as a novel diagnostic device for screening and evaluating cervical dysp...
Sleep staging is critical to assess sleep quality and diagnose disorders. Despite advancements in artificial intelligence enabling automated sleep s...
Many molecular alterations serve as clinically prognostic or therapy-predictive biomarkers, typically detected using single or multi-gene molecular ...
Cancer is a highly heterogeneous disease with significant variability in molecular features and clinical outcomes, making diagnosis and treatment ch...
There is no study that comprehensively evaluates data on the readability and quality of "palliative care" information provided by artificial intellige...
The study presents a novel approach for quantifying cellular interactions in digital pathology using deep learning-based image cytometry. Traditiona...
A grid search, at the cost of training and testing a large number of models, is an effective way to optimize the prediction performance of deep lear...
The application of machine learning to transcriptomics data has led to significant advances in cancer research. However, the high dimensionality and...
Biomedical imaging and RNA sequencing with single-cell resolution improves our understanding of white blood cell diseases like leukemia. By combinin...
CCCTC-binding factor (CTCF) is an insulator protein that binds to a highly conserved DNA motif and facilitates regulation of three-dimensional (3D) nu...
Programmable lipid nanoparticles, or LNPs, represent a breakthrough in the realm of targeted drug delivery, offering precise spatiotemporal control ...
Federated Learning (FL) has emerged as a promising solution to address the limitations of centralised machine learning (ML) in oncology, particularl...
CONTEXT.—: Conventional karyotype analysis, which provides comprehensive cytogenetic information, plays a significant role in the diagnosis and risk s...
BACKGROUND: Aberrant circular RNA (circRNA) acts as an oncogene or suppressor during neoplasm initiation and development. However, the functions of mo...
OBJECTIVES: To identify potential diagnostic markers for small cell lung cancer (SCLC) and investigate the correlation with immune cell infiltration.
Breast cancer (BC) is the most commonly diagnosed cancer in women globally. Natural killer (NK) cells play a vital role in tumour immunosurveillance. ...
The National Cancer Institute and the Department of Energy strategic partnership applies advanced computing and predictive machine learning and deep l...
Background Multiparametric MRI can help identify clinically significant prostate cancer (csPCa) (Gleason score ≥7) but is limited by reader experience...