Transplantation

Artificial Intelligence-Assisted Matching of Human Postmortem Donors to Ocular Research Projects.

The scarcity of human ocular samples with short postmortem intervals (PMIs) is a significant issue i...

Bayesian-optimized deep learning for identifying essential genes of mitophagy and fostering therapies to combat drug resistance in human cancers.

Dysregulated mitophagy is essential for mitochondrial quality control within human cancers. However,...

Establishment and Validation of the Novel Necroptosis-related Genes for Predicting Stemness and Immunity of Hepatocellular Carcinoma Machine-learning Algorithm.

BACKGROUND: Necroptosis, a recently identified mechanism of programmed cell death, exerts significan...

RSNA 2023 Abdominal Trauma AI Challenge: Review and Outcomes.

Purpose To evaluate the performance of the winning machine learning models from the 2023 RSNA Abdomi...

Novel machine learning technique further clarifies unrelated donor selection to optimize transplantation outcomes.

We investigated the impact of donor characteristics on outcomes in allogeneic hematopoietic cell tra...

Predictive Capacities of a Machine Learning Decision Tree Model Created to Analyse Feasibility of an Open or Robotic Kidney Transplant.

BACKGROUND: Machine learning has emerged as a potent tool in healthcare. A decision tree model was b...

MSA-MaxNet: Multi-Scale Attention Enhanced Multi-Axis Vision Transformer Network for Medical Image Segmentation.

Convolutional neural networks (CNNs) are well established in handling local features in visual tasks...

[LORENZO'S OIL AND ADRENOLEUKODYSTROPHY EXAMINING AN ARTIFICIAL INTELLIGENCE TOOL INTENDED FOR CONDUCTING LITERATURE SEARCHES AND ANALYSES].

Adrenoleukodystrophy is a genetic metabolic disorder characterized by a heterogeneous phenotype. Its...

Live-Donor Kidney Transplant Outcome Prediction (L-TOP) using artificial intelligence.

BACKGROUND: Outcome prediction for live-donor kidney transplantation improves clinical and patient d...

High-dimensional Immune Profiles and Machine Learning May Predict Acute Myeloid Leukemia Relapse Early following Transplant.

Identification of early immune signatures associated with acute myeloid leukemia (AML) relapse follo...

Artificial intelligence enabled interpretation of ECG images to predict hematopoietic cell transplantation toxicity.

Artificial intelligence (AI)-enabled interpretation of electrocardiogram (ECG) images (AI-ECGs) can ...

Machine learning-driven in-hospital mortality prediction in HIV/AIDS patients with infection: a single-centred retrospective study.

() is a widely disseminated betaherpesvirus that typically induces latant infections. In immunocom...

Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge.

Deep learning has shown great potential to automate abdominal organ segmentation and quantification....

NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide-human leukocyte antigen complexes.

MOTIVATION: Neoantigens, derived from somatic mutations in cancer cells, can elicit anti-tumor immun...

Identification of novel M2 macrophage-related molecule ATP6V1E1 and its biological role in hepatocellular carcinoma based on machine learning algorithms.

Hepatocellular carcinoma (HCC) remains the most prevalent form of primary liver cancer, characterize...

From organs to algorithms: Redefining cancer classification in the age of artificial intelligence.

Traditional cancer classification based on organ of origin and histology is increasingly at odds wit...

Automatic Tumor Cellularity Measurement: AI-Based Pipeline for Multi-Organ Pathology Imaging.

Tumor Cellularity (TC) is an important metric for assessing organ tumor burden. However, manual cell...

Visualization of Surgical Needle Tips Hidden Inside Organs Using Generative Adversarial Networks.

Postoperative complications in surgery are particularly prevalent in laparoscopic procedures, which ...

C2P-GCN: Cell-to-Patch Graph Convolutional Network for Colorectal Cancer Grading.

Graph-based learning approaches, due to their ability to encode tissue/organ structure information, ...

Predicting Donor Selection and Multi-Organ Transplantation within Organ Procurement Organizations Using Machine Learning.

Organ procurement organizations (OPOs) play a crucial role in the field of organ transplantation, se...

Artificial intelligence for forecasting live donor kidney function.