AIMC Topic: Humans

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A Deep Learning Approach to Using Wearable Seismocardiography (SCG) for Diagnosing Aortic Valve Stenosis and Predicting Aortic Hemodynamics Obtained by 4D Flow MRI.

Annals of biomedical engineering
In this paper, we explored the use of deep learning for the prediction of aortic flow metrics obtained using 4-dimensional (4D) flow magnetic resonance imaging (MRI) using wearable seismocardiography (SCG) devices. 4D flow MRI provides a comprehensiv...

Deep Learning for Predicting Effect of Neoadjuvant Therapies in Non-Small Cell Lung Carcinomas With Histologic Images.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Neoadjuvant therapies are used for locally advanced non-small cell lung carcinomas, whereby pathologists histologically evaluate the effect using resected specimens. Major pathological response (MPR) has recently been used for treatment evaluation an...

Deep Learning for Detecting BRCA Mutations in High-Grade Ovarian Cancer Based on an Innovative Tumor Segmentation Method From Whole Slide Images.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
BRCA1 and BRCA2 genes play a crucial role in repairing DNA double-strand breaks through homologous recombination. Their mutations represent a significant proportion of homologous recombination deficiency and are a reliable effective predictor of sens...

Traditional Machine Learning Methods versus Deep Learning for Meningioma Classification, Grading, Outcome Prediction, and Segmentation: A Systematic Review and Meta-Analysis.

World neurosurgery
BACKGROUND: Meningiomas are common intracranial tumors. Machine learning (ML) algorithms are emerging to improve accuracy in 4 primary domains: classification, grading, outcome prediction, and segmentation. Such algorithms include both traditional ap...

Learning from the machine: AI assistance is not an effective learning tool for resident education in chest x-ray interpretation.

European radiology
OBJECTIVES: To assess whether a computer-aided detection (CADe) system could serve as a learning tool for radiology residents in chest X-ray (CXR) interpretation.

Enhanced breast mass mammography classification approach based on pre-processing and hybridization of transfer learning models.

Journal of cancer research and clinical oncology
BACKGROUND AND OBJECTIVE: The second most prevalent cause of death among women is now breast cancer, surpassing heart disease. Mammography images must accurately identify breast masses to diagnose early breast cancer, which can significantly increase...

A novel neural network for improved in-hospital mortality prediction with irregular and incomplete multivariate data.

Neural networks : the official journal of the International Neural Network Society
Accurate estimation of in-hospital mortality based on patients' physiological time series data improves the performance of the clinical decision support systems and assists hospital providers in allocating resources. In practice, the data quality iss...

The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision.

Health policy (Amsterdam, Netherlands)
Despite the renewed interest in Artificial Intelligence-based clinical decision support systems (AI-CDS), there is still a lack of empirical evidence supporting their effectiveness. This underscores the need for rigorous and continuous evaluation and...

The seductive allure of spatial biology: accelerating new discoveries in the life sciences.

Immunology and cell biology
Spatial biology is a rapidly developing field which enables the visualization of protein and transcriptomic data while preserving tissue context and architecture. Initially used in discovery, there is growing promise for translational and diagnostic ...