P.09.25 MACHINE LEARNING APPROACHES FOR NON-INVASIVE ULTRASOUND-BASED QUANTITATIVE ASSESSMENT OF LIVER STEATOSIS

2018 ◽  
Vol 50 (2) ◽  
pp. e225-e226
Author(s):  
A. Salvati ◽  
N. Di Lascio ◽  
C. Avigo ◽  
N. Martini ◽  
M. Ragucci ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantitative Assessment ◽  
Liver Steatosis ◽  
Learning Approaches ◽  
Non Invasive

Machine learning approaches for non-invasive ultrasound. Based quantitative assessment of liver steatosis

2018 ◽  
Vol 68 ◽  
pp. S575-S576
Author(s):  
N.D. Lascio ◽  
C. Avigo ◽  
A. Salvati ◽  
N. Martini ◽  
M. Ragucci ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantitative Assessment ◽  
Liver Steatosis ◽  
Learning Approaches ◽  
Non Invasive

scholarly journals Non-invasive health prediction from visually observable features

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 918
Author(s):  
Fan Yi Khong ◽  
Tee Connie ◽  
Michael Kah Ong Goh ◽  
Li Pei Wong ◽  
Pin Shen Teh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cost Effective ◽  
Healthcare Services ◽  
Skin Surface ◽  
Health Condition ◽  
Machine Learning Techniques ◽  
Disease Spread ◽  
Computational Time ◽  
Learning Approaches ◽  
Non Invasive

Background: The unprecedented development of Artificial Intelligence has revolutionised the healthcare industry. In the next generation of healthcare systems, self-diagnosis will be pivotal to personalised healthcare services. During the COVID-19 pandemic, new screening and diagnostic approaches like mobile health are well-positioned to reduce disease spread and overcome geographical barriers. This paper presents a non-invasive screening approach to predict the health of a person from visually observable features using machine learning techniques. Images like face and skin surface of the patients are acquired using camera or mobile devices and analysed to derive clinical reasoning and prediction of the person’s health. Methods: In specific, a two-level classification approach is presented. The proposed hierarchical model chooses a class by training a binary classifier at the node of the hierarchy. Prediction is then made using a set of class-specific reduced feature set. Results: Testing accuracies of 86.87% and 76.84% are reported for the first and second-level classification. Empirical results demonstrate that the proposed approach yields favourable prediction results while greatly reduces the computational time. Conclusions: The study suggests that it is possible to predict the health condition of a person based on his/her face appearance using cost-effective machine learning approaches.

scholarly journals Diagnostic classification based on DNA methylation profiles using sequential machine learning approaches

2021 ◽  
Author(s):  
M. W. Wojewodzic ◽  
J. P. Lavender
Keyword(s):  
Machine Learning ◽  
Dna Methylation ◽  
Machine Learning Algorithms ◽  
Aberrant Methylation ◽  
Learning Approaches ◽  
Tissue Samples ◽  
Non Invasive ◽  
Genome Wide ◽  
Cancer Types ◽  
Methylation Patterns

AbstractAberrant methylation patterns in human DNA have great potential for the discovery of novel diagnostic and disease progression biomarkers. In this paper, we used machine learning algorithms to identify promising methylation sites for diagnosing cancerous tissue and to classify patients based on methylation values at these sites.We used genome-wide DNA methylation patterns from both cancerous and normal tissue samples, obtained from the Genomic Data Commons consortium and trialled our methods on three types of urological cancer. A decision tree was used to identify the methylation sites most useful for diagnosis.The identified locations were then used to train a neural network to classify samples as either cancerous or non-cancerous. Using this two-step approach we found strong indicative biomarker panels for each of the three cancer types.These methods could likely be translated to other cancers and improved by using non-invasive liquid methods such as blood instead of biopsy tissue.

scholarly journals Histopathological distinction of non-invasive and invasive bladder cancers using machine learning approaches

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Peng-Nien Yin ◽  
Kishan KC ◽  
Shishi Wei ◽  
Qi Yu ◽  
Rui Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Non Invasive

scholarly journals The DecNef collection, fMRI data from closed-loop decoded neurofeedback experiments

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Aurelio Cortese ◽  
Saori C. Tanaka ◽  
Kaoru Amano ◽  
Ai Koizumi ◽  
Hakwan Lau ◽  
...  
Keyword(s):  
Machine Learning ◽  
Closed Loop ◽  
Brain Dynamics ◽  
Learning Approaches ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Research Groups ◽  
Large Dataset ◽  
Non Invasive ◽  
Public Dataset

AbstractDecoded neurofeedback (DecNef) is a form of closed-loop functional magnetic resonance imaging (fMRI) combined with machine learning approaches, which holds some promises for clinical applications. Yet, currently only a few research groups have had the opportunity to run such experiments; furthermore, there is no existing public dataset for scientists to analyse and investigate some of the factors enabling the manipulation of brain dynamics. We release here the data from published DecNef studies, consisting of 5 separate fMRI datasets, each with multiple sessions recorded per participant. For each participant the data consists of a session that was used in the main experiment to train the machine learning decoder, and several (from 3 to 10) closed-loop fMRI neural reinforcement sessions. The large dataset, currently comprising more than 60 participants, will be useful to the fMRI community at large and to researchers trying to understand the mechanisms underlying non-invasive modulation of brain dynamics. Finally, the data collection size will increase over time as data from newly run DecNef studies will be added.

scholarly journals Diagnostic classification based on DNA methylation profiles using sequential machine learning approaches.

2021 ◽  
Author(s):  
Marcin W. Wojewodzic ◽  
Jan P. Lavender
Keyword(s):  
Machine Learning ◽  
Dna Methylation ◽  
Machine Learning Algorithms ◽  
Aberrant Methylation ◽  
Learning Approaches ◽  
Tissue Samples ◽  
Non Invasive ◽  
Genome Wide ◽  
Cancer Types ◽  
Methylation Patterns

Abstract Aberrant methylation patterns in human DNA have great potential for the discovery of novel diagnostic and disease progression biomarkers. In this paper, we used machine learning algorithms to identify promising methylation sites for diagnosing cancerous tissue and to classify patients based on methylation values at these sites. We used genome-wide DNA methylation patterns from both cancerous and normal tissue samples, obtained from the Genomic Data Commons consortium and trialled our methods on three types of urological cancer. A decision tree was used to identify the methylation sites most useful for diagnosis. The identified locations were then used to train a neural network to classify samples as either cancerous or non-cancerous. Using this two-step approach we found strong indicative biomarker panels for each of the three cancer types. These methods could likely be translated to other cancers and improved by using non-invasive liquid methods such as blood instead of biopsy tissue.

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