AIMC Topic: Humans

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The Future of Surgical Diagnostics: Artificial Intelligence-Enhanced Detection of Ganglion Cells for Hirschsprung Disease.

Laboratory investigation; a journal of technical methods and pathology
Hirschsprung disease, a congenital disease characterized by the absence of ganglion cells, presents significant surgical challenges. Addressing a critical gap in intraoperative diagnostics, we introduce transformative artificial intelligence approach...

GraphCPP: The new state-of-the-art method for cell-penetrating peptide prediction via graph neural networks.

British journal of pharmacology
BACKGROUND AND PURPOSE: Cell-penetrating peptides (CPPs) are short amino acid sequences that can penetrate cell membranes and deliver molecules into cells. Several models have been developed for their discovery, yet these models often face challenges...

A hybrid network for fiber orientation distribution reconstruction employing multi-scale information.

Medical physics
BACKGROUND: Accurate fiber orientation distribution (FOD) is crucial for resolving complex neural fiber structures. However, existing reconstruction methods often fail to integrate both global and local FOD information, as well as the directional inf...

Perioperative risk scores: prediction, pitfalls, and progress.

Current opinion in anaesthesiology
PURPOSE OF REVIEW: Perioperative risk scores aim to risk-stratify patients to guide their evaluation and management. Several scores are established in clinical practice, but often do not generalize well to new data and require ongoing updates to impr...

Integrating machine learning-predicted circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) in metastatic breast cancer: A proof of principle study on endocrine resistance profiling.

Cancer letters
The study explored endocrine resistance by leveraging machine learning to establish the prognostic stratification of predicted Circulating tumor cells (CTCs), assessing its integration with circulating tumor DNA (ctDNA) features and contextually eval...

Machine Learning-Based Prediction of Death and Hospitalization in Patients With Implantable Cardioverter Defibrillators.

Journal of the American College of Cardiology
BACKGROUND: Predicting the clinical trajectory of individual patients with implantable cardioverter-defibrillators (ICDs) is essential to inform clinical care. Machine learning approaches can potentially overcome the limitations of conventional stati...

Artificial intelligence meets the world experts; updates and novel therapies in autoimmunity - The 14th international congress on autoimmunity 2024 (AUTO14), Ljubljana.

Autoimmunity reviews
The bi-annual international congress on autoimmunity is a huge opportunity for the medical community to discuss the latest updates in the field. During the 14th congress 2024 (AUTO14) in Ljubljana, artificial intelligence (AI) occupied special attent...

A Multi-task learning U-Net model for end-to-end HEp-2 cell image analysis.

Artificial intelligence in medicine
Antinuclear Antibody (ANA) testing is pivotal to help diagnose patients with a suspected autoimmune disease. The Indirect Immunofluorescence (IIF) microscopy performed with human epithelial type 2 (HEp-2) cells as the substrate is the reference metho...

Generating synthetic clinical text with local large language models to identify misdiagnosed limb fractures in radiology reports.

Artificial intelligence in medicine
Large language models (LLMs) demonstrate impressive capabilities in generating human-like content and have much potential to improve the performance and efficiency of healthcare. An important application of LLMs is to generate synthetic clinical repo...