AIMC Topic: Humans

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A machine learning predictive model for recurrence of resected distal cholangiocarcinoma: Development and validation of predictive model using artificial intelligence.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
INTRODUCTION: Distal Cholangiocarcinoma (dCCA) represents a challenge in hepatobiliary oncology, that requires nuanced post-resection prognostic modeling. Conventional staging criteria may oversimplify dCCA complexities, prompting the exploration of ...

Real-time coronary artery segmentation in CAG images: A semi-supervised deep learning strategy.

Artificial intelligence in medicine
BACKGROUND: When treating patients with coronary artery disease and concurrent renal concerns, we often encounter a conundrum: how to achieve a clearer view of vascular details while minimizing the contrast and radiation doses during percutaneous cor...

Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study.

La Radiologia medica
PURPOSE: To investigate the feasibility of an artificial intelligence (AI)-based semi-automated segmentation for the extraction of ultrasound (US)-derived radiomics features in the characterization of focal breast lesions (FBLs).

Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data.

Cytometry. Part B, Clinical cytometry
Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artifici...

Identifying Substructures That Facilitate Compounds to Penetrate the Blood-Brain Barrier via Passive Transport Using Machine Learning Explainer Models.

ACS chemical neuroscience
The local interpretable model-agnostic explanation (LIME) method was used to interpret two machine learning models of compounds penetrating the blood-brain barrier. The classification models, Random Forest, ExtraTrees, and Deep Residual Network, were...

CancerGATE: Prediction of cancer-driver genes using graph attention autoencoders.

Computers in biology and medicine
Discovery of the cancer type specific-driver genes is important for understanding the molecular mechanisms of each cancer type and for providing proper treatment. Recently, graph deep learning methods became widely used in finding cancer-driver genes...

An efficient deep neural network for automatic classification of acute intracranial hemorrhages in brain CT scans.

Computers in biology and medicine
BACKGROUND: Recent advancements in deep learning models have demonstrated their potential in the field of medical imaging, achieving remarkable performance surpassing human capabilities in tasks such as classification and segmentation. However, these...