AIMC Topic: Databases, Factual

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Assessing the robustness of clinical trials by estimating Jadad's score using artificial intelligence approaches.

Computers in biology and medicine
BACKGROUND: Clinical trials are essential in medical science and are currently the most robust strategy for evaluating the effectiveness of a treatment. However, some of these studies are less reliable than others due to flaws in their design. Assess...

Classification of multi-lead ECG with deep residual convolutional neural networks.

Physiological measurement
. Automatic electrocardiogram (ECG) interpretation based on deep learning methods is attracting increasing attention. In this study, we propose a novel method to accurately classify multi-lead ECGs using deep residual neural networks.. ECG recordings...

A multi-birth metric learning framework based on binary constraints.

Neural networks : the official journal of the International Neural Network Society
Multi-metric learning plays a significant role in improving the generalization of algorithms related to distance metrics since using a single metric is sometimes insufficient to handle complex data. Metric learning can adjust automatically the distan...

Intelligent Face Recognition System Based on Universal Design Concept.

Computational intelligence and neuroscience
The rapid development of science and technology, i.e., integrated modules that are actuators and sensors, promotes the comprehensive popularization of intelligent products in people's life. More particularly, with the advent of the hybrid of the Inte...

MetaAge: Meta-Learning Personalized Age Estimators.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Different people age in different ways. Learning a personalized age estimator for each person is a promising direction for age estimation given that it better models the personalization of aging processes. However, most existing personalized methods ...

Data governance functions to support responsible data stewardship in pediatric radiology research studies using artificial intelligence.

Pediatric radiology
The integration of human and machine intelligence promises to profoundly change the practice of medicine. The rapidly increasing adoption of artificial intelligence (AI) solutions highlights its potential to streamline physician work and optimize cli...

Deep Learning-driven classification of external DICOM studies for PACS archiving.

European radiology
OBJECTIVES: Over the course of their treatment, patients often switch hospitals, requiring staff at the new hospital to import external imaging studies to their local database. In this study, the authors present MOdality Mapping and Orchestration (MO...

Machine learning for improving high-dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature.

Pharmacoepidemiology and drug safety
PURPOSE: Supplementing investigator-specified variables with large numbers of empirically identified features that collectively serve as 'proxies' for unspecified or unmeasured factors can often improve confounding control in studies utilizing admini...

A Modified Deep Learning Framework for Arrhythmia Disease Analysis in Medical Imaging Using Electrocardiogram Signal.

BioMed research international
Arrhythmias are anomalies in the heartbeat rhythm that occur occasionally in people's lives. These arrhythmias can lead to potentially deadly consequences, putting your life in jeopardy. As a result, arrhythmia identification and classification are a...

Segmentation of laser induced retinal lesions using deep learning (December 2021).

Lasers in surgery and medicine
OBJECTIVE: Detection of retinal laser lesions is necessary in both the evaluation of the extent of damage from high power laser sources, and in validating treatments involving the placement of laser lesions. However, such lesions are difficult to det...