Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 541 to 550 of 234,501 articles

Clinical significance of intraoperative dissection time and machine learning-based prediction in laparoscopic distal gastrectomy for gastric cancer.

Surgical endoscopy
BACKGROUND: Establishing a clinically meaningful cutoff value for intraoperative dissection time and developing predictive models for this metric among patients with distal gastric cancer. METHODS: This retrospective multicenter study included 849 pa... read more 

The β-cells in type 1 diabetes mellitus: findings from human pancreas studies.

Nature reviews. Endocrinology
Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease characterized by long-term and dynamic interactions between the immune system and pancreatic β-cells, ultimately leading to severe insulin deficiency. A paradigm shift in T1DM research h... read more 

Tryptoline stereoprobe elaboration identifies inhibitors of the GRPEL1-HSPA9 complex.

Nature chemical biology
Activity-based protein profiling has identified hundreds of human proteins from diverse classes that react at cysteine residues with stereochemically defined electrophilic compounds (stereoprobes). The structure-activity relationships underlying thes... read more 

From HPV infection to cervical precancer: Molecular mechanisms, immune microenvironment and emerging technologies.

Cellular and molecular life sciences : CMLS
Cervical cancer remains a leading cause of cancer-related mortality among women globally, with persistent high-risk human papillomavirus (HPV) infection as the primary etiological factor. Cervical precancerous lesions, including high-grade squamous i... read more 

Building and Validating a Data-Governance Workflow for Artificial Intelligence-Based Tuberculosis Detection from Chest Radiographs, and Quantifying the Impact of Data Leakage.

Journal of imaging informatics in medicine
Studies applying artificial intelligence (AI) to tuberculosis (TB) detection from chest radiographs (CXR) focus on models and metrics, yet the quality, provenance and reproducibility of the data workflow decide the reliability of results; duplicate i... read more 

Evaluating Explicit Anatomical Priors Beyond a Mature CT Detector for Six Acute Abdominal Emergencies.

Journal of imaging informatics in medicine
To test whether anatomical priors from a general-purpose organ segmentation model (TotalSegmentator, TS), applied at inference or during training, improve an externally validated YOLOv11 detector for six acute abdominal emergencies. The frozen detect... read more 

Investigation on Automatic Classification of Bone, Lymph Node, and Local Lesions in CT Images of Prostate Cancer Patients.

Journal of imaging informatics in medicine
Accurate assessment of different types of prostate cancer (PCa) lesions is a crucial part of treatment planning. Following a computed tomography (CT) or a positron emission tomography (PET)/CT imaging scan, a significant amount of time is required fr... read more 

A Survey of Computational Color Constancy: Approaches and Emerging Applications in Medical Imaging.

Journal of imaging informatics in medicine
As the evening sets in and natural light fades to artificial light, it is remarkable how the colors around us still appear nearly the same. This phenomenon, known as color constancy, allows us to perceive relatively stable colors even as lighting con... read more 

A Deep Learning Framework for Accurate lncRNA-Protein Interaction Prediction via Multi-Kernel Graph Embeddings and Self-Attention.

Interdisciplinary sciences, computational life sciences
Long non-coding RNAs (lncRNAs) play a pivotal role in regulating biological processes, primarily through lncRNA-Protein Interactions (LPI), which are crucial for understanding disease mechanisms and cellular functions. While computational methods exi... read more 

A topic-based framework for interpretable and sparse semantic representations.

Behavior research methods
In computational approaches to language, semantic representations produced by modern deep learning systems are often encoded as high-dimensional latent vectors that are difficult to interpret. By contrast, cognitive science has emphasized the need fo... read more