Leukemia

Opening the Black Box: Spatial Transcriptomics and the Relevance of Artificial Intelligence-Detected Prognostic Regions in High-Grade Serous Carcinoma.

Image-based deep learning models are used to extract new information from standard hematoxylin and e...

Pre-hospital glycemia as a biomarker for in-hospital all-cause mortality in diabetic patients - a pilot study.

BACKGROUND: Type 2 Diabetes Mellitus (T2DM) presents a significant healthcare challenge, with consid...

Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging.

Heart failure (HF) is a leading cause of morbidity and mortality in the United States and worldwide,...

Letter to the Editor Regarding Article "Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset".

The cited article reports on a convolutional neural network trained to predict response to neoadjuva...

Physical performance strongly predicts all-cause mortality risk in a real-world population of older diabetic patients: machine learning approach for mortality risk stratification.

BACKGROUND: Prognostic risk stratification in older adults with type 2 diabetes (T2D) is important f...

AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial.

The early identification of vulnerable patients has the potential to improve outcomes but poses a su...

Preclinical identification of acute coronary syndrome without high sensitivity troponin assays using machine learning algorithms.

Preclinical management of patients with acute chest pain and their identification as candidates for ...

One model to use them all: training a segmentation model with complementary datasets.

PURPOSE: Understanding surgical scenes is crucial for computer-assisted surgery systems to provide i...

All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems.

BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) models trained on multi-centric and multi-de...

A machine learning approach for predicting textbook outcome after cytoreductive surgery and hyperthermic intraperitoneal chemotherapy.

INTRODUCTION: Peritoneal carcinomatosis is considered a late-stage manifestation of neoplastic disea...

Hematologic cancer diagnosis and classification using machine and deep learning: State-of-the-art techniques and emerging research directives.

Hematology is the study of diagnosis and treatment options for blood diseases, including cancer. Can...

Deep-learning model for evaluating histopathology of acute renal tubular injury.

Tubular injury is the most common cause of acute kidney injury. Histopathological diagnosis may help...

Generalized biomolecular modeling and design with RoseTTAFold All-Atom.

Deep-learning methods have revolutionized protein structure prediction and design but are presently ...

Application of parallel artificial membrane permeability assay technique and chemometric modeling for blood-brain barrier permeability prediction of protein kinase inhibitors.

This study aims to investigate the passive diffusion of protein kinase inhibitors through the blood...

Develop and Validate a Nomogram Combining Contrast-Enhanced Spectral Mammography Deep Learning with Clinical-Pathological Features to Predict Neoadjuvant Chemotherapy Response in Patients with ER-Positive/HER2-Negative Breast Cancer.

RATIONALE AND OBJECTIVES: To develop and validate a nomogram that combines contrast-enhanced spectra...

Deep learning-enabled detection of rare circulating tumor cell clusters in whole blood using label-free, flow cytometry.

Metastatic tumors have poor prognoses for progression-free and overall survival for all cancer patie...

A memristive all-inclusive hypernetwork for parallel analog deployment of full search space architectures.

In recent years, there has been a significant advancement in memristor-based neural networks, positi...

Prediction of deep molecular response in chronic myeloid leukemia using supervised machine learning models.

Exploring a specialized programmed-cell death patterns to predict the prognosis and sensitivity of immunotherapy in cutaneous melanoma via machine learning.

The mortality and therapeutic failure in cutaneous melanoma (CM) are mainly caused by wide metastasi...

Machine Learning-Based Prediction of Pathological Responses and Prognosis After Neoadjuvant Chemotherapy for Non-Small-Cell Lung Cancer: A Retrospective Study.

BACKGROUND: Neoadjuvant chemotherapy has variable efficacy in patients with non-small-cell lung canc...

Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube.

This study aimed to evaluate the sensitivity of AI in screening acute leukemia and its capability to...