AIMC Topic: Machine Learning

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Artificial intelligence / machine-learning tool for post-market surveillance of in vitro diagnostic assays.

New biotechnology
The study compares an artificial intelligence technology with traditional manual search of literature databases to assess the accuracy and efficiency of retrieving relevant articles for post-market surveillance of in vitro diagnostic and medical devi...

Machine learning models for early prediction of mortality risk in patients with burns: A single center experience.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS
Mortality rate is considered as the most important outcome measure for assessing the severity of burn injury. A scale or model that accurately predicts burn mortality can be useful to determine the clinical course of burn injuries, discuss treatment ...

Virtual plates: Getting the best out of high content screens.

SLAS discovery : advancing life sciences R & D
High content screening (HCS) is becoming widely adopted as a high throughput screening modality, using hundred-of-thousands compounds library. The use of machine learning and artificial intelligence in image analysis is amplifying this trend. Another...

Assessment of inspiration and technical quality in anteroposterior thoracic radiographs using machine learning.

Radiography (London, England : 1995)
INTRODUCTION: Chest radiographs are the most performed radiographic procedure, but suboptimal technical factors can impact clinical interpretation. A deep learning model was developed to assess technical and inspiratory adequacy of anteroposterior ch...

Beyond spiking networks: The computational advantages of dendritic amplification and input segregation.

Proceedings of the National Academy of Sciences of the United States of America
The brain can efficiently learn a wide range of tasks, motivating the search for biologically inspired learning rules for improving current artificial intelligence technology. Most biological models are composed of point neurons and cannot achieve st...

Identifying Explainable Machine Learning Models and a Novel SFRP2 Fibroblast Signature as Predictors for Precision Medicine in Ovarian Cancer.

International journal of molecular sciences
Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functional subsets in the tumor microenvironment are essential to the development of patient management st...

Predicting Tacit Coordination Success Using Electroencephalogram Trajectories: The Impact of Task Difficulty.

Sensors (Basel, Switzerland)
In this study, we aim to develop a machine learning model to predict the level of coordination between two players in tacit coordination games by analyzing the similarity of their spatial EEG features. We present an analysis, demonstrating the model'...

Deep Learning Approaches for Automatic Quality Assurance of Magnetic Resonance Images Using ACR Phantom.

BMC medical imaging
BACKGROUND: In recent years, there has been a growing trend towards utilizing Artificial Intelligence (AI) and machine learning techniques in medical imaging, including for the purpose of automating quality assurance. In this research, we aimed to de...

Machine Learning Web Application for Predicting Functional Outcomes in Patients With Traumatic Spinal Cord Injury Following Inpatient Rehabilitation.

Journal of neurotrauma
Accurately predicting functional outcomes in patients with spinal cord injury (SCI) helps clinicians set realistic functional recovery goals and improve the home environment after discharge. The present study aimed to develop and validate machine lea...

eXplainable Artificial Intelligence (XAI) in aging clock models.

Ageing research reviews
XAI is a rapidly progressing field of machine learning, aiming to unravel the predictions of complex models. XAI is especially required in sensitive applications, e.g. in health care, when diagnosis, recommendations and treatment choices might rely o...