A Systematic Review of Artificial Intelligence (AI) Based Approaches for the Diagnosis of Parkinson’s Disease

Saravanan, S.; Ramkumar, K.; Adalarasu, K.; Venkatesh, S.; Kumar, Sanjay; Stalin, S.; Amirtharajan, Rengarajan

doi:10.1007/s11831-022-09710-1

Cited by 45 publications

(14 citation statements)

References 99 publications

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“…Although previous studies have reviewed the use of machine learning in the diagnosis and assessment of PD, they were limited to the analysis of motor symptoms, kinematics, and wearable sensor data as well as the utilization of traditional ML techniques [47], [48]). Recently, [49] provided a comprehensive analysis of the influence of machine learning and deep learning approaches applied towards Parkinson's disease diagnosis on the development of new research areas based on neuroimaging techniques as well as physiological signals obtained from speech, gait and handwriting. In addition, this research investigates the existing status and potential applications of data-driven AI technologies in the diagnosis of Parkinson's disease.…”

Section: Related Workmentioning

confidence: 99%

Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review

Skaramagkas

Pentari

Kefalopoulou

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Parkinson's Disease (PD) is among the most frequent neurological disorders. Approaches that employ artificial intelligence and notably deep learning, have been extensively embraced with promising outcomes. This study dispenses an exhaustive review between 2016 and January 2023 on deep learning techniques used in the prognosis and evolution of symptoms and characteristics of the disease based on gait, upper limb movement, speech and facial expression-related information as well as the fusion of more than one of the aforementioned modalities. The search resulted in the selection of 87 original research publications, of which we have summarized the relevant information regarding the utilized learning and development process, demographic information, primary outcomes, and sensory equipment related information. Various deep learning algorithms and frameworks have attained state-of-theart performance in many PD-related tasks by outperforming conventional machine learning approaches, according to the research reviewed. In the meanwhile, we identify significant drawbacks in the existing research, including a lack of data availability and interpretability of models. The fast advancements in deep learning and the rise in accessible data provide the opportunity to address these difficulties in the near future and for the broad application of this technology in clinical settings.

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Section: Related Workmentioning

confidence: 99%

Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review

Skaramagkas

Pentari

Kefalopoulou

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…A large number of existing studies in the broader literature have examined many ML and deep learning (DL) models for predicting PD [28]. In 2022, Singh et al used various ML algorithms for predicting PD based on voice recordings, and the results were acceptable [29].…”

Section: Related Workmentioning

confidence: 99%

Early and High‐Accuracy Diagnosis of Parkinson’s Disease: Outcomes of a New Model

Doumari

Berahmand

Ebadi

2023

Computational and Mathematical Methods in Medicine

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Parkinson’s disease (PD) is one of the significant common neurological disorders of the current age that causes uncontrollable movements like shaking, stiffness, and difficulty. The early clinical diagnosis of this disease is essential for preventing the progression of PD. Hence, an innovative method is proposed here based on combining the crow search algorithm and decision tree (CSADT) for the early PD diagnosis. This approach is used on four crucial Parkinson’s datasets, including meander, spiral, voice, and speech-Sakar. Using the presented method, PD is effectively diagnosed by evaluating each dataset’s critical features and extracting the primary practical outcomes. The used algorithm was compared with other machine learning algorithms of k-nearest neighbor (KNN), support vector machine (SVM), naive Baye (NB), multilayer perceptron (MLP), decision tree (DT), random tree, logistic regression, support vector machine of radial base functions (SVM of RBFs), and combined classifier in terms of accuracy, recall, and combination measure F1. The analytical results emphasize the used algorithm’s superiority over the other selected ones. The proposed model yields nearly 100% accuracy through various trials on the datasets. Notably, a high detection speed achieved the lowest detection time of 2.6 seconds. The main novelty of this paper is attributed to the accuracy of the presented PD diagnosis method, which is much higher than its counterparts.

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“…The performance analysis of some PD identification approaches is depicted in Table 1 . On the other hand, a systematic review of AI-based approaches for the diagnosis of PD is presented by Saravanan et al [ 33 ].…”

Section: Severity Identification Of Parkinson’s Diseasementioning

confidence: 99%

Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains

et al. 2023

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Disease severity identification using computational intelligence-based approaches is gaining popularity nowadays. Artificial intelligence and deep-learning-assisted approaches are proving to be significant in the rapid and accurate diagnosis of several diseases. In addition to disease identification, these approaches have the potential to identify the severity of a disease. The problem of disease severity identification can be considered multi-class classification, where the class labels are the severity levels of the disease. Plenty of computational intelligence-based solutions have been presented by researchers for severity identification. This paper presents a comprehensive review of recent approaches for identifying disease severity levels using computational intelligence-based approaches. We followed the PRISMA guidelines and compiled several works related to the severity identification of multidisciplinary diseases of the last decade from well-known publishers, such as MDPI, Springer, IEEE, Elsevier, etc. This article is devoted toward the severity identification of two main diseases, viz. Parkinson’s Disease and Diabetic Retinopathy. However, severity identification of a few other diseases, such as COVID-19, autonomic nervous system dysfunction, tuberculosis, sepsis, sleep apnea, psychosis, traumatic brain injury, breast cancer, knee osteoarthritis, and Alzheimer’s disease, was also briefly covered. Each work has been carefully examined against its methodology, dataset used, and the type of disease on several performance metrics, accuracy, specificity, etc. In addition to this, we also presented a few public repositories that can be utilized to conduct research on disease severity identification. We hope that this review not only acts as a compendium but also provides insights to the researchers working on disease severity identification using computational intelligence-based approaches.

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A Systematic Review of Artificial Intelligence (AI) Based Approaches for the Diagnosis of Parkinson’s Disease

Cited by 45 publications

References 99 publications

Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review

Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review

Early and High‐Accuracy Diagnosis of Parkinson’s Disease: Outcomes of a New Model

Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains

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