AIMC Topic: Humans

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Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers.

European radiology experimental
High-grade serous ovarian cancer is the most lethal gynaecological malignancy. Detailed molecular studies have revealed marked intra-patient heterogeneity at the tumour microenvironment level, likely contributing to poor prognosis. Despite large quan...

Precision epigenetics provides a scalable pathway for improving coronary heart disease care globally.

Epigenomics
Coronary heart disease (CHD) is the world's leading cause of death. Up to 90% of all CHD deaths are preventable, but effective prevention of this mortality requires more scalable, precise methods for assessing CHD status and monitoring treatment resp...

Validity and reliability of an instrument evaluating the performance of intelligent chatbot: the Artificial Intelligence Performance Instrument (AIPI).

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
OBJECTIVES: To evaluate the reliability and validity of the Artificial Intelligence Performance Instrument (AIPI).

Robot-navigated pedicle screw insertion can reduce intraoperative blood loss and length of hospital stay: analysis of 1,633 patients utilizing propensity score matching.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Navigation and robotic technologies have emerged as an alternative option to conventional freehand techniques for pedicle screw insertion. However, the effectiveness of these technologies in reducing the perioperative complication...

Demonstration of Convolutional Neural Networks to Determine Patch Test Reactivity.

Dermatitis : contact, atopic, occupational, drug
Convolutional neural networks (CNNs) have the potential to assist allergists and dermatologists in the analysis of patch tests. Such models can help reduce interprovider variability and improve consistency of patch test interpretations. Our aim is ...

Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation.

Medical image analysis
The role of fibrillar collagen in the tissue microenvironment is critical in disease contexts ranging from cancers to chronic inflammations, as evidenced by many studies. Quantifying fibrillar collagen organization has become a powerful approach for ...

Noninvasive grading of glioma brain tumors using magnetic resonance imaging and deep learning methods.

Journal of cancer research and clinical oncology
PURPOSE: Convolutional Neural Networks (ConvNets) have quickly become popular machine learning techniques in recent years, particularly in the classification and segmentation of medical images. One of the most prevalent types of brain cancers is glio...

Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology: a retrospective multicentric study.

European journal of cancer (Oxford, England : 1990)
AIM: Gastric cancer (GC) is a tumour entity with highly variant outcomes. Lymph node metastasis is a prognostically adverse biomarker. We hypothesised that GC primary tissue contains information that is predictive of lymph node status and patient pro...

Impact of retraining a deep learning algorithm for improving guideline-compliant aortic diameter measurements on non-gated chest CT.

European journal of radiology
PURPOSE/OBJECTIVE: Reliable detection of thoracic aortic dilatation (TAD) is mandatory in clinical routine. For ECG-gated CT angiography, automated deep learning (DL) algorithms are established for diameter measurements according to current guideline...