AIMC Topic: Humans

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Development and Evaluation of a Machine Learning Prediction Model for Flap Failure in Microvascular Breast Reconstruction.

Annals of surgical oncology
BACKGROUND: Despite high success rates, flap failure remains an inherent risk in microvascular breast reconstruction. Identifying patients who are at high risk for flap failure would enable us to recommend alternative reconstructive techniques. Howev...

Advanced robotic surgical systems in ophthalmology.

Eye (London, England)
In this paper, an overview of advanced robotic surgical systems in ophthalmology is provided. The systems are introduced as representative examples of the degree of human vs. robotic control during surgical procedures. The details are presented on ea...

Application of Artificial Neural Network for Prediction of Risk of Multiple Sclerosis Based on Single Nucleotide Polymorphism Genotypes.

Journal of molecular neuroscience : MN
The artificial neural network (ANN) is a sort of machine learning method which has been used in determination of risk of human disorders. In the current investigation, we have created an ANN and trained it based on the genetic data of 401 multiple sc...

Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models.

Dento maxillo facial radiology
OBJECTIVES: This study aimed to develop five different supervised machine learning (ML) classifier models using artificial intelligence (AI) techniques and to compare their performance for cervical vertebral maturation (CVM) analysis. A clinical deci...

Unsupervised Machine Learning in Pathology: The Next Frontier.

Surgical pathology clinics
Applications of artificial intelligence and particularly deep learning to aid pathologists in carrying out laborious and qualitative tasks in histopathologic image analysis have now become ubiquitous. We introduce and illustrate how unsupervised mach...

Deep neural networks with a set of node-wise varying activation functions.

Neural networks : the official journal of the International Neural Network Society
In this study, we present deep neural networks with a set of node-wise varying activation functions. The feature-learning abilities of the nodes are affected by the selected activation functions, where the nodes with smaller indices become increasing...

Probabilistic inference of binary Markov random fields in spiking neural networks through mean-field approximation.

Neural networks : the official journal of the International Neural Network Society
Recent studies have suggested that the cognitive process of the human brain is realized as probabilistic inference and can be further modeled by probabilistic graphical models like Markov random fields. Nevertheless, it remains unclear how probabilis...

Comprehensive Molecular and Pathologic Evaluation of Transitional Mesothelioma Assisted by Deep Learning Approach: A Multi-Institutional Study of the International Mesothelioma Panel from the MESOPATH Reference Center.

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
INTRODUCTION: Histologic subtypes of malignant pleural mesothelioma are a major prognostic indicator and decision denominator for all therapeutic strategies. In an ambiguous case, a rare transitional mesothelioma (TM) pattern may be diagnosed by path...

A Noninvasive Glucose Monitoring SoC Based on Single Wavelength Photoplethysmography.

IEEE transactions on biomedical circuits and systems
Conventional glucose monitoring methods for the growing numbers of diabetic patients around the world are invasive, painful, costly and, time-consuming. Complications aroused due to the abnormal blood sugar levels in diabetic patients have created th...

Molecular and DNA Artificial Neural Networks via Fractional Coding.

IEEE transactions on biomedical circuits and systems
This article considers implementation of artificial neural networks (ANNs) using molecular computing and DNA based on fractional coding. Prior work had addressed molecular two-layer ANNs with binary inputs and arbitrary weights. In prior work using f...