Machine learning and deep learning for clinical data and PET/SPECT imaging in Parkinson's disease: a review

Khachnaoui, Hajer; Mabrouk, Rostom; Khlifa, Nawrès

doi:10.1049/iet-ipr.2020.1048

Cited by 27 publications

(16 citation statements)

References 123 publications

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“…e overall size of each calculation is n � 40, and the dimension of each function is m � 2. e maximum number of iterations is international thermonuclear fusion test reactor � 1500, and the maximum optimization accuracy is to l � 1.0 × 10 − 20, and when each calculation has completed the specified number of iterations, the final convergence accuracy will reach 1.0 × 10 − 20, which is considered a successful optimization. e parameter settings of BAS-CS are step size step � 10, distance between two whiskers d0 � 5, discovery probability a p � 0.25, step attenuation coefficient eta � 0.95, and the acceleration factor of the PSO algorithm is set to 1C � 2C � 1.5 (15).…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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Application Analysis of Artificial Intelligence Algorithms in Image Processing

Feng

2022

Mathematical Problems in Engineering

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Due to the rapid development of information, the image recognition method based on the artificial intelligence algorithm has been applied to many application fields. As the product of modern information technology, the image recognition method based on the artificial intelligence algorithm is also a significant and pioneering research topic. With the development of digital image processing technology and the updating of computer electronic products, digital image processing technology has been applied to various fields and has made great contributions to the progress of science and technology and the development of productivity. Image processing methods cover a wide range of application fields, including image conversion, restoration, separation, enhancement and matching, and classification. In order to explore the application of the AI algorithm in image processing, on the basis of the latest BAS algorithm discovered recently, we have established a brand-new hybrid intelligent algorithm BAS based on C as computing, which is applied to multiple image processing fields and achieves good optimization results. Studying the problems and future development directions in the application is conducive to promoting such development of image recognition technology and is conducive to applying image recognition technology to more fields.

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Section: Experimental Results and Analysismentioning

confidence: 99%

“…Work. In recent years, the computer has become a new science and technology, which has attracted the attention of all walks of life [15]. As machine intelligence, how does artificial intelligence help humans [16]?…”

Section: E Concept Of Artificial Intelligence and Image Processingmentioning

confidence: 99%

Application Analysis of Artificial Intelligence Algorithms in Image Processing

Feng

2022

Mathematical Problems in Engineering

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“…Even though ML methods in previous literature have presented high classification accuracy for PD detection, still, there are some obstacles related to feature extraction and selection which need to be addressed [ 104 ]. The utilization of several features can increase the computation time [ 105 , 106 ].…”

Section: Discussionmentioning

confidence: 99%

Predicting Parkinson’s Disease Progression: Evaluation of Ensemble Methods in Machine Learning

Nilashi

Abumalloh

Minaei‐Bidgoli

et al. 2022

Journal of Healthcare Engineering

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Parkinson’s disease (PD) is a complex neurodegenerative disease. Accurate diagnosis of this disease in the early stages is crucial for its initial treatment. This paper aims to present a comparative study on the methods developed by machine learning techniques in PD diagnosis. We rely on clustering and prediction learning approaches to perform the comparative study. Specifically, we use different clustering techniques for PD data clustering and support vector regression ensembles to predict Motor-UPDRS and Total-UPDRS. The results are then compared with the other prediction learning approaches, multiple linear regression, neurofuzzy, and support vector regression techniques. The comparative study is performed on a real-world PD dataset. The prediction results of data analysis on a PD real-world dataset revealed that expectation-maximization with the aid of SVR ensembles can provide better prediction accuracy in relation to decision trees, deep belief network, neurofuzzy, and support vector regression combined with other clustering techniques in the prediction of Motor-UPDRS and Total-UPDRS.

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“…Many studies have been carried out to cover AI techniques that have been used to mitigate and prevent PD [ 11 , 12 , 13 , 14 ]. These approaches are conducted in reviews or surveys that generally focus on artificial intelligence (AI) applications such as patient diagnosis, epidemiological monitoring, and drug and vaccine discovery [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

et al. 2021

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Background: Parkinson’s Disease (PD) is a chronic neurodegenerative disorder that has been ranked second after Alzheimer’s disease worldwide. Early diagnosis of PD is crucial to combat against PD to allow patients to deal with it properly. However, there is no medical test(s) available to diagnose PD conclusively. Therefore, computer-aided diagnosis (CAD) systems offered a better solution to make the necessary data-driven decisions and assist the physician. Numerous studies were conducted to propose CAD to diagnose PD in the early stages. No comprehensive reviews have been conducted to summarize the role of AI tools to combat PD. Objective: The study aimed to explore and summarize the applications of neural networks to diagnose PD. Methods: PRISMA Extension for Scoping Reviews (PRISMA-ScR) was followed to conduct this scoping review. To identify the relevant studies, both medical databases (e.g., PubMed) and technical databases (IEEE) were searched. Three reviewers carried out the study selection and extracted the data from the included studies independently. Then, the narrative approach was adopted to synthesis the extracted data. Results: Out of 1061 studies, 91 studies satisfied the eligibility criteria in this review. About half of the included studies have implemented artificial neural networks to diagnose PD. Numerous studies included focused on the freezing of gait (FoG). Biomedical voice and signal datasets were the most commonly used data types to develop and validate these models. However, MRI- and CT-scan images were also utilized in the included studies. Conclusion: Neural networks play an integral and substantial role in combating PD. Many possible applications of neural networks were identified in this review, however, most of them are limited up to research purposes.

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Machine learning and deep learning for clinical data and PET/SPECT imaging in Parkinson's disease: a review

Cited by 27 publications

References 123 publications

Application Analysis of Artificial Intelligence Algorithms in Image Processing

Application Analysis of Artificial Intelligence Algorithms in Image Processing

Predicting Parkinson’s Disease Progression: Evaluation of Ensemble Methods in Machine Learning

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

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