AIMC Topic: Humans

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Charting a path forward for clinical research in artificial intelligence and gastroenterology.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
Gastroenterology has been an early leader in bridging the gap between artificial intelligence (AI) model development and clinical trial validation, and in recent years we have seen the publication of several randomized clinical trials examining the r...

Application of deep learning to establish a diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging.

Clinical imaging
PURPOSE: There are currently few specific artificial intelligence (AI) studies for Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions. This study aimed to establish an AI diagnostic model of breast lesions using two-dimensional gr...

Deep neural network-based generalized sidelobe canceller for dual-channel far-field speech recognition.

Neural networks : the official journal of the International Neural Network Society
The traditional generalized sidelobe canceller (GSC) is a common speech enhancement front end to improve the noise robustness of automatic speech recognition (ASR) systems in the far-field cases. However, the traditional GSC is optimized based on the...

Critical assessment of AI in drug discovery.

Expert opinion on drug discovery
: Artificial Intelligence (AI) has become a component of our everyday lives, with applications ranging from recommendations on what to buy to the analysis of radiology images. Many of the techniques originally developed for other fields such as langu...

Sex-Specific Classification of Drug-Induced Torsade de Pointes Susceptibility Using Cardiac Simulations and Machine Learning.

Clinical pharmacology and therapeutics
Torsade de Pointes (TdP), a rare but lethal ventricular arrhythmia, is a toxic side effect of many drugs. To assess TdP risk, safety regulatory guidelines require quantification of hERG channel block in vitro and QT interval prolongation in vivo for ...

A Machine Learning Approach to First Pass Reperfusion in Mechanical Thrombectomy: Prediction and Feature Analysis.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
INTRODUCTION: Novel machine learning (ML) methods are being investigated across medicine for their predictive capabilities while boasting increased adaptability and generalizability. In our study, we compare logistic regression with machine learning ...

Exploring photoacoustic spectroscopy-based machine learning together with metabolomics to assess breast tumor progression in a xenograft model ex vivo.

Laboratory investigation; a journal of technical methods and pathology
In the current study, a breast tumor xenograft was established in athymic nude mice by subcutaneous injection of the MCF-7 cell line and assessed the tumor progression by photoacoustic spectroscopy combined with machine learning tools. The advancemen...

Three ways of knowing: the integration of clinical expertise, evidence-based medicine, and artificial intelligence in assisted reproductive technologies.

Journal of assisted reproduction and genetics
Decision-making in fertility care is on the cusp of a significant frameshift. Online tools to integrate artificial intelligence into the decision-making process across all aspects of ART are rapidly emerging. These tools have the potential to improve...

Radiomics analysis on CT images for prediction of radiation-induced kidney damage by machine learning models.

Computers in biology and medicine
INTRODUCTION: We aimed to assess the power of radiomic features based on computed tomography to predict risk of chronic kidney disease in patients undergoing radiation therapy of abdominal cancers.

Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning.

Nature biomedical engineering
The clinical application of breast ultrasound for the assessment of cancer risk and of deep learning for the classification of breast-ultrasound images has been hindered by inter-grader variability and high false positive rates and by deep-learning m...