Artificial intelligence applied to neuroimaging data in Parkinsonian syndromes: Actuality and expectations

Vitale, Alejandro José; Villa, Rossella; Ugga, Lorenzo; Romeo, Valeria; Stanzione, Arnaldo; Cuocolo, Renato

doi:10.3934/mbe.2021091

Cited by 9 publications

(2 citation statements)

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“…To do this, they used a feature decomposition and kernel discriminant analysis (KDA) applying it to information from MR brain images from 830 subjects comprising 198 AD patients [51]. Other more recent studies have also carried out a methodological approach more similar to ours, having used strategies based on artificial intelligence for the detection of neurodegenerative diseases, although also based on clinical or neuroimaging information [52,53]. However, very few investigations use this methodology for the design of diagnostic algorithms based on information from molecular studies.…”

Section: Similarities and Differences With Previously Published Researchmentioning

confidence: 99%

Neural Network Aided Detection of Huntington Disease

Perez

Caballero-Villarraso

2022

JCM

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Huntington Disease (HD) is a degenerative neurological disease that causes a significant impact on the quality of life of the patient and eventually death. In this paper we present an approach to create a biomarker using as an input DNA CpG methylation data to identify HD patients. DNA CpG methylation is a well-known epigenetic marker for disease state. Technological advances have made it possible to quickly analyze hundreds of thousands of CpGs. This large amount of information might introduce noise as potentially not all DNA CpG methylation levels will be related to the presence of the illness. In this paper, we were able to reduce the number of CpGs considered from hundreds of thousands to 237 using a non-linear approach. It will be shown that using only these 237 CpGs and non-linear techniques such as artificial neural networks makes it possible to accurately differentiate between control and HD patients. An underlying assumption in this paper is that there are no indications suggesting that the process is linear and therefore non-linear techniques, such as artificial neural networks, are a valid tool to analyze this complex disease. The proposed approach is able to accurately distinguish between control and HD patients using DNA CpG methylation data as an input and non-linear forecasting techniques. It should be noted that the dataset analyzed is relatively small. However, the results seem relatively consistent and the analysis can be repeated with larger data-sets as they become available.

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Section: Similarities and Differences With Previously Published Researchmentioning

confidence: 99%

Neural Network Aided Detection of Huntington Disease

Perez

Caballero-Villarraso

2022

JCM

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“…According to previous research, there is a high probability that patients with PD develop cognitive impairment that may affect their quality of life; this impairment predominantly involves the cognitive domains of attention, executive function, and visuospatial skills [ 2 , 3 , 4 ]. Biomarkers obtained mainly from neuroimaging data were extensively discussed for finding predictors of cognitive dysfunction in Parkinson’s disease in a literature survey [ 5 , 6 ]. Indeed, it is crucial to identify the factors influencing cognitive decline that affect clinical prognosis and require early intervention [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Cognitive Degeneration in Parkinson’s Disease Patients Using a Machine Learning Method

et al. 2022

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This study developed a predictive model for cognitive degeneration in patients with Parkinson’s disease (PD) using a machine learning method. The clinical data, plasma biomarkers, and neuropsychological test results of patients with PD were collected and utilized as model predictors. Machine learning methods comprising support vector machines (SVMs) and principal component analysis (PCA) were applied to obtain a cognitive classification model. Using 32 comprehensive predictive parameters, the PCA-SVM classifier reached 92.3% accuracy and 0.929 area under the receiver operating characteristic curve (AUC). Furthermore, the accuracy could be increased to 100% and the AUC to 1.0 in a PCA-SVM model using only 13 carefully chosen features.

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