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Gambosi B, Jamal Sheiban F, Biasizzo M, Antonietti A, D'angelo E, Mazzoni A, Pedrocchi A. A Model with Dopamine Depletion in Basal Ganglia and Cerebellum Predicts Changes in Thalamocortical Beta Oscillations. Int J Neural Syst 2024; 34:2450045. [PMID: 38886870 DOI: 10.1142/s012906572450045x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Parkinsonism is presented as a motor syndrome characterized by rigidity, tremors, and bradykinesia, with Parkinson's disease (PD) being the predominant cause. The discovery that those motor symptoms result from the death of dopaminergic cells in the substantia nigra led to focus most of parkinsonism research on the basal ganglia (BG). However, recent findings point to an active involvement of the cerebellum in this motor syndrome. Here, we have developed a multiscale computational model of the rodent brain's BG-cerebellar network. Simulations showed that a direct effect of dopamine depletion on the cerebellum must be taken into account to reproduce the alterations of neural activity in parkinsonism, particularly the increased beta oscillations widely reported in PD patients. Moreover, dopamine depletion indirectly impacted spike-time-dependent plasticity at the parallel fiber-Purkinje cell synapses, degrading associative motor learning as observed in parkinsonism. Overall, these results suggest a relevant involvement of cerebellum in parkinsonism associative motor symptoms.
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Affiliation(s)
- Benedetta Gambosi
- NearLab, Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy
| | - Francesco Jamal Sheiban
- NearLab, Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy
| | - Marco Biasizzo
- Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna, Pisa, Italy
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Information Engineering (DIE), University of Pisa, Pisa, Italy
| | - Alberto Antonietti
- NearLab, Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy
| | - Egidio D'angelo
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Digital Neuroscience Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Alberto Mazzoni
- Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna, Pisa, Italy
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Alessandra Pedrocchi
- NearLab, Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy
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Martinez-Murcia FJ, Arco JE, Jimenez-Mesa C, Segovia F, Illan IA, Ramirez J, Gorriz JM. Bridging Imaging and Clinical Scores in Parkinson's Progression via Multimodal Self-Supervised Deep Learning. Int J Neural Syst 2024; 34:2450043. [PMID: 38770651 DOI: 10.1142/s0129065724500436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Neurodegenerative diseases pose a formidable challenge to medical research, demanding a nuanced understanding of their progressive nature. In this regard, latent generative models can effectively be used in a data-driven modeling of different dimensions of neurodegeneration, framed within the context of the manifold hypothesis. This paper proposes a joint framework for a multi-modal, common latent generative model to address the need for a more comprehensive understanding of the neurodegenerative landscape in the context of Parkinson's disease (PD). The proposed architecture uses coupled variational autoencoders (VAEs) to joint model a common latent space to both neuroimaging and clinical data from the Parkinson's Progression Markers Initiative (PPMI). Alternative loss functions, different normalization procedures, and the interpretability and explainability of latent generative models are addressed, leading to a model that was able to predict clinical symptomatology in the test set, as measured by the unified Parkinson's disease rating scale (UPDRS), with R2 up to 0.86 for same-modality and 0.441 cross-modality (using solely neuroimaging). The findings provide a foundation for further advancements in the field of clinical research and practice, with potential applications in decision-making processes for PD. The study also highlights the limitations and capabilities of the proposed model, emphasizing its direct interpretability and potential impact on understanding and interpreting neuroimaging patterns associated with PD symptomatology.
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Affiliation(s)
- Francisco J Martinez-Murcia
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Center for Advanced Studies, Ludwig-Maximilien Universität München, München, Germany
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Juan Eloy Arco
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Carmen Jimenez-Mesa
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Fermin Segovia
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Ignacio A Illan
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Javier Ramirez
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Juan Manuel Gorriz
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Center for Advanced Studies, Ludwig-Maximilien Universität München, München, Germany
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
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Redhya M, Sathesh Kumar K. Refining PD classification through ensemble bionic machine learning architecture with adaptive threshold based image denoising. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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4
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Junaid M, Ali S, Eid F, El-Sappagh S, Abuhmed T. Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 234:107495. [PMID: 37003039 DOI: 10.1016/j.cmpb.2023.107495] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 02/23/2023] [Accepted: 03/17/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND AND OBJECTIVES Parkinson's Disease (PD) is a devastating chronic neurological condition. Machine learning (ML) techniques have been used in the early prediction of PD progression. Fusion of heterogeneous data modalities proved its capability to improve the performance of ML models. Time series data fusion supports the tracking of the disease over time. In addition, the trustworthiness of the resulting models is improved by adding model explainability features. The literature on PD has not sufficiently explored these three points. METHODS In this work, we proposed an ML pipeline for predicting the progression of PD that is both accurate and explainable. We explore the fusion of different combinations of five time series modalities from the Parkinson's Progression Markers Initiative (PPMI) real-world dataset, including patient characteristics, biosamples, medication history, motor, and non-motor function data. Each patient has six visits. The problem has been formulated in two ways: ❶ a three-class based progression prediction with 953 patients in each time series modality, and ❷ a four-class based progression prediction with 1,060 patients in each time series modality. The statistical features of these six visits were calculated from each modality and diverse feature selection methods were applied to select the most informative feature sets. The extracted features were used to train a set of well-known ML models including Support vector machines (SVM), random forests (RF), extra tree classifier (ETC), light gradient boosting machines (LGBM), and stochastic gradient descent (SGD). We examined a number of data-balancing strategies in the pipeline with different combinations of modalities. ML models have been optimized using the Bayesian optimizer. A comprehensive evaluation of various ML methods has been conducted, and the best models have been extended to provide different explainability features. RESULTS We compare the performance of ML models before and after optimization and using and without using feature selection. In the three-class experiment and with various modality fusions, the LGBM model produced the most accurate results with a 10-fold cross-validation (10-CV) accuracy of 90.73% using non-motor function modality. RF produced the best results in the four-class experiment with various modality fusions with a 10-CV accuracy of 94.57% using non-motor modality. With the fused dataset of non-motor and motor function modalities, the LGBM model outperformed the other ML models in both the 3-class and 4-class experiments (i.e., 10-CV accuracy of 94.89% and 93.73%, respectively). Using the Shapely Additive Explanations (SHAP) framework, we employed global and instance-based explanations to explain the behavior of each ML classifier. Moreover, we extended the explainability by implementing the LIME and SHAPASH local explainers. The consistency of these explainers has been explored. The resultant classifiers were accurate, explainable, and thus medically more relevant and applicable. CONCLUSIONS The select modalities and feature sets were confirmed by the literature and medical experts. The various explainers suggest that the bradykinesia (NP3BRADY) feature was the most dominant and consistent. By providing thorough insights into the influence of multiple modalities on the disease risk, the suggested approach is expected to help improve the clinical knowledge of PD progression processes.
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Affiliation(s)
- Muhammad Junaid
- Information Laboratory (InfoLab), Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, South Korea.
| | - Sajid Ali
- Information Laboratory (InfoLab), Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, South Korea.
| | - Fatma Eid
- Technology Management, Stony Brook University, New York 11794, USA.
| | - Shaker El-Sappagh
- Information Laboratory (InfoLab), College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, South Korea; Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt; Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha, 13518, Egypt.
| | - Tamer Abuhmed
- Information Laboratory (InfoLab), College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, South Korea.
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Kumar M, Nguyen TPN, Kaur J, Singh TG, Soni D, Singh R, Kumar P. Opportunities and challenges in application of artificial intelligence in pharmacology. Pharmacol Rep 2023; 75:3-18. [PMID: 36624355 PMCID: PMC9838466 DOI: 10.1007/s43440-022-00445-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/23/2022] [Accepted: 12/25/2022] [Indexed: 01/11/2023]
Abstract
Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.
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Affiliation(s)
- Mandeep Kumar
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
| | - T P Nhung Nguyen
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
- Department of Pharmacy, Da Nang University of Medical Technology and Pharmacy, Da Nang, Vietnam
| | - Jasleen Kaur
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Lucknow, Uttar Pradesh, 226002, India
| | | | - Divya Soni
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Randhir Singh
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Puneet Kumar
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India.
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6
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Leveraging Computational Intelligence Techniques for Diagnosing Degenerative Nerve Diseases: A Comprehensive Review, Open Challenges, and Future Research Directions. Diagnostics (Basel) 2023; 13:diagnostics13020288. [PMID: 36673100 PMCID: PMC9858227 DOI: 10.3390/diagnostics13020288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/28/2022] [Accepted: 01/10/2023] [Indexed: 01/13/2023] Open
Abstract
Degenerative nerve diseases such as Alzheimer's and Parkinson's diseases have always been a global issue of concern. Approximately 1/6th of the world's population suffers from these disorders, yet there are no definitive solutions to cure these diseases after the symptoms set in. The best way to treat these disorders is to detect them at an earlier stage. Many of these diseases are genetic; this enables machine learning algorithms to give inferences based on the patient's medical records and history. Machine learning algorithms such as deep neural networks are also critical for the early identification of degenerative nerve diseases. The significant applications of machine learning and deep learning in early diagnosis and establishing potential therapies for degenerative nerve diseases have motivated us to work on this review paper. Through this review, we covered various machine learning and deep learning algorithms and their application in the diagnosis of degenerative nerve diseases, such as Alzheimer's disease and Parkinson's disease. Furthermore, we also included the recent advancements in each of these models, which improved their capabilities for classifying degenerative nerve diseases. The limitations of each of these methods are also discussed. In the conclusion, we mention open research challenges and various alternative technologies, such as virtual reality and Big data analytics, which can be useful for the diagnosis of degenerative nerve diseases.
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Nassibi A, Papavassiliou C, Atashzar SF. Depression diagnosis using machine intelligence based on spatiospectrotemporal analysis of multi-channel EEG. Med Biol Eng Comput 2022; 60:3187-3202. [DOI: 10.1007/s11517-022-02647-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 07/28/2022] [Indexed: 11/30/2022]
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8
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Automatic Diagnosis of Mild Cognitive Impairment Based on Spectral, Functional Connectivity, and Nonlinear EEG-Based Features. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2014001. [PMID: 35991131 PMCID: PMC9388263 DOI: 10.1155/2022/2014001] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 05/21/2022] [Accepted: 07/30/2022] [Indexed: 11/18/2022]
Abstract
Accurate and early diagnosis of mild cognitive impairment (MCI) is necessary to prevent the progress of Alzheimer's and other kinds of dementia. Unfortunately, the symptoms of MCI are complicated and may often be misinterpreted as those associated with the normal ageing process. To address this issue, many studies have proposed application of machine learning techniques for early MCI diagnosis based on electroencephalography (EEG). In this study, a machine learning framework for MCI diagnosis is proposed in this study, which extracts spectral, functional connectivity, and nonlinear features from EEG signals. The sequential backward feature selection (SBFS) algorithm is used to select the best subset of features. Several classification models and different combinations of feature sets are measured to identify the best ones for the proposed framework. A dataset of 16 and 18 EEG data of normal and MCI subjects is used to validate the proposed system. Metrics including accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR) are evaluated using 10-fold crossvalidation. An average AC of 99.4%, SE of 98.8%, SP of 100%, F1 of 99.4%, and FDR of 0% have been provided by the best performance of the proposed framework using the linear support vector machine (LSVM) classifier and the combination of all feature sets. The acquired results confirm that the proposed framework provides an accurate and robust performance for recognizing MCI cases and outperforms previous approaches. Based on the obtained results, it is possible to be developed in order to use as a computer-aided diagnosis (CAD) tool for clinical purposes.
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9
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Manickam P, Mariappan SA, Murugesan SM, Hansda S, Kaushik A, Shinde R, Thipperudraswamy SP. Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare. BIOSENSORS 2022; 12:bios12080562. [PMID: 35892459 PMCID: PMC9330886 DOI: 10.3390/bios12080562] [Citation(s) in RCA: 65] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 05/05/2023]
Abstract
Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.
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Affiliation(s)
- Pandiaraj Manickam
- Electrodics and Electrocatalysis Division, CSIR-Central Electrochemical Research Institute (CECRI), Karaikudi, Sivagangai 630003, Tamil Nadu, India; (S.A.M.); (S.M.M.)
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India; (S.H.); (S.P.T.)
- Correspondence:
| | - Siva Ananth Mariappan
- Electrodics and Electrocatalysis Division, CSIR-Central Electrochemical Research Institute (CECRI), Karaikudi, Sivagangai 630003, Tamil Nadu, India; (S.A.M.); (S.M.M.)
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India; (S.H.); (S.P.T.)
| | - Sindhu Monica Murugesan
- Electrodics and Electrocatalysis Division, CSIR-Central Electrochemical Research Institute (CECRI), Karaikudi, Sivagangai 630003, Tamil Nadu, India; (S.A.M.); (S.M.M.)
| | - Shekhar Hansda
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India; (S.H.); (S.P.T.)
- Corrosion and Materials Protection Division, CSIR-Central Electrochemical Research Institute (CECRI), Karaikudi, Sivagangai 630003, Tamil Nadu, India
| | - Ajeet Kaushik
- School of Engineering, University of Petroleum and Energy Studies (UPES), Dehradun 248001, Uttarakhand, India;
- NanoBioTech Laboratory, Department of Environmental Engineering, Florida Polytechnic University, Lakeland, FL 33805-8531, USA
| | - Ravikumar Shinde
- Department of Zoology, Shri Pundlik Maharaj Mahavidyalaya Nandura, Buldana 443404, Maharashtra, India;
| | - S. P. Thipperudraswamy
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India; (S.H.); (S.P.T.)
- Central Instrument Facility, CSIR-Central Electrochemical Research Institute, Karaikudi, Sivagangai 630003, Tamil Nadu, India
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A major depressive disorder diagnosis approach based on EEG signals using dictionary learning and functional connectivity features. Phys Eng Sci Med 2022; 45:705-719. [DOI: 10.1007/s13246-022-01135-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 05/03/2022] [Indexed: 10/18/2022]
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Sarica A, Quattrone A, Quattrone A. Explainable machine learning with pairwise interactions for the classification of Parkinson's disease and SWEDD from clinical and imaging features. Brain Imaging Behav 2022; 16:2188-2198. [PMID: 35614327 PMCID: PMC9132761 DOI: 10.1007/s11682-022-00688-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/09/2022] [Indexed: 12/11/2022]
Abstract
Scans without evidence of dopaminergic deficit (SWEDD) refers to patients who mimics motor and non-motor symptoms of Parkinson's disease (PD) but showing integrity of dopaminergic system. For this reason, the differential diagnosis between SWEDD and PD patients is often not possible in absence of dopamine imaging. Machine Learning (ML) showed optimal performance in automatically distinguishing these two diseases from clinical and imaging data. However, the most common applied ML algorithms provide high accuracy at expense of findings intelligibility. In this work, a novel ML glass-box model, the Explainable Boosting Machine (EBM), based on Generalized Additive Models plus interactions (GA2Ms), was employed to obtain interpretability in classifying PD and SWEDD while still providing optimal performance. Dataset (168 healthy controls, HC; 396 PD; 58 SWEDD) was obtained from PPMI database and consisted of 178 among clinical and imaging features. Six binary EBM classifiers were trained on feature space with (SBR) and without (noSBR) dopaminergic striatal specific binding ratio: HC-PDSBR, HC-SWEDDSBR, PD-SWEDDSBR and HC-PDnoSBR, HC-SWEDDnoSBR, PD-SWEDDnoSBR. Excellent AUC-ROC (1) was reached in classifying HC from PD and SWEDD, both with and without SBR, and by PD-SWEDDSBR (0.986), while PD-SWEDDnoSBR showed lower AUC-ROC (0.882). Apart from optimal accuracies, EBM algorithm was able to provide global and local explanations, revealing that the presence of pairwise interactions between UPSIT Booklet #1 and Epworth Sleepiness Scale item 3 (ESS3), MDS-UPDRS-III pronation-supination movements right hand (NP3PRSPR) and MDS-UPDRS-III rigidity left upper limb (NP3RIGLU) could provide good performance in predicting PD and SWEDD also without imaging features.
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Affiliation(s)
- Alessia Sarica
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, viale Europa, 88100, Catanzaro, Germaneto, Italy.
| | - Andrea Quattrone
- Institute of Neurology, Department of Medical and Surgical Sciences, Magna Graecia University, 88100, Catanzaro, Italy
| | - Aldo Quattrone
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, viale Europa, 88100, Catanzaro, Germaneto, Italy.,Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology, National Research Council, 88100, Catanzaro, Italy
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12
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Parkinson’s disease diagnosis using neural networks: Survey and comprehensive evaluation. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102909] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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13
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Castillo-Barnes D, Jimenez-Mesa C, Martinez-Murcia FJ, Salas-Gonzalez D, Ramírez J, Górriz JM. Quantifying Differences Between Affine and Nonlinear Spatial Normalization of FP-CIT Spect Images. Int J Neural Syst 2022; 32:2250019. [PMID: 35313792 DOI: 10.1142/s0129065722500198] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Spatial normalization helps us to compare quantitatively two or more input brain scans. Although using an affine normalization approach preserves the anatomical structures, the neuroimaging field is more common to find works that make use of nonlinear transformations. The main reason is that they facilitate a voxel-wise comparison, not only when studying functional images but also when comparing MRI scans given that they fit better to a reference template. However, the amount of bias introduced by the nonlinear transformations can potentially alter the final outcome of a diagnosis especially when studying functional scans for neurological disorders like Parkinson's Disease. In this context, we have tried to quantify the bias introduced by the affine and the nonlinear spatial registration of FP-CIT SPECT volumes of healthy control subjects and patients with PD. For that purpose, we calculated the deformation fields of each participant and applied these deformation fields to a 3D-grid. As the space between the edges of small cubes comprising the grid change, we can quantify which parts from the brain have been enlarged, compressed or just remain the same. When the nonlinear approach is applied, scans from PD patients show a region near their striatum very similar in shape to that of healthy subjects. This artificially increases the interclass separation between patients with PD and healthy subjects as the local intensity is decreased in the latter region, and leads machine learning systems to biased results due to the artificial information introduced by these deformations.
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Affiliation(s)
- Diego Castillo-Barnes
- Department of Signal Theory, Telematics and Communications, University of Granada, Periodista Daniel Saucedo Aranda, 18071 Granada, Spain
| | - Carmen Jimenez-Mesa
- Department of Signal Theory, Telematics and Communications, University of Granada, Periodista Daniel Saucedo Aranda, 18071 Granada, Spain
| | - Francisco J Martinez-Murcia
- Department of Signal Theory, Telematics and Communications, University of Granada, Periodista Daniel Saucedo Aranda, 18071 Granada, Spain
| | - Diego Salas-Gonzalez
- Department of Signal Theory, Telematics and Communications, University of Granada, Periodista Daniel Saucedo Aranda, 18071 Granada, Spain
| | - Javier Ramírez
- Department of Signal Theory, Telematics and Communications, University of Granada, Periodista Daniel Saucedo Aranda, 18071 Granada, Spain
| | - Juan M Górriz
- Department of Signal Theory, Telematics and Communications, University of Granada, Periodista Daniel Saucedo Aranda, 18071 Granada, Spain.,Department of Psychiatry, University of Cambridge, Herchel Smith Buidling for Brain & Mind Sciences, Forvie Site Robinson Way, Cambridge CB2 0SZ, UK
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14
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Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson's disease. NPJ Parkinsons Dis 2022; 8:13. [PMID: 35064123 PMCID: PMC8783003 DOI: 10.1038/s41531-021-00266-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 12/10/2021] [Indexed: 12/14/2022] Open
Abstract
Parkinson’s disease (PD) is a common, progressive, and currently incurable neurodegenerative movement disorder. The diagnosis of PD is challenging, especially in the differential diagnosis of parkinsonism and in early PD detection. Due to the advantages of machine learning such as learning complex data patterns and making inferences for individuals, machine-learning techniques have been increasingly applied to the diagnosis of PD, and have shown some promising results. Machine-learning-based imaging applications have made it possible to help differentiate parkinsonism and detect PD at early stages automatically in a number of neuroimaging studies. Comparative studies have shown that machine-learning-based SPECT image analysis applications in PD have outperformed conventional semi-quantitative analysis in detecting PD-associated dopaminergic degeneration, performed comparably well as experts’ visual inspection, and helped improve PD diagnostic accuracy of radiologists. Using combined multi-modal (imaging and clinical) data in these applications may further enhance PD diagnosis and early detection. To integrate machine-learning-based diagnostic applications into clinical systems, further validation and optimization of these applications are needed to make them accurate and reliable. It is anticipated that machine-learning techniques will further help improve differential diagnosis of parkinsonism and early detection of PD, which may reduce the error rate of PD diagnosis and help detect PD at pre-motor stage to make it possible for early treatments (e.g., neuroprotective treatment) to slow down PD progression, prevent severe motor symptoms from emerging, and relieve patients from suffering.
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15
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Arco JE, Ortiz A, Ramírez J, Zhang YD, Górriz JM. Tiled Sparse Coding in Eigenspaces for Image Classification. Int J Neural Syst 2021; 32:2250007. [PMID: 34967705 DOI: 10.1142/s0129065722500071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches. blackThese alternatives usually obtain a large performance based on complex solutions, leading to a high computational cost and the need of having large databases. In this work, we propose a classification framework based on sparse coding. Images are blackfirst partitioned into different tiles, and a dictionary is built after applying PCA to these tiles. The original signals are then transformed as a linear combination of the elements of the dictionary. blackThen, they are reconstructed by iteratively deactivating the elements associated with each component. Classification is finally performed employing as features the subsequent reconstruction errors. Performance is evaluated in a real context where distinguishing between four different pathologies: control versus bacterial pneumonia versus viral pneumonia versus COVID-19. blackOur system differentiates between pneumonia patients and controls with an accuracy of 97.74%, whereas in the 4-class context the accuracy is 86.73%. The excellent results and the pioneering use of sparse coding in this scenario evidence that our proposal can assist clinicians when their workload is high.
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Affiliation(s)
- Juan E Arco
- Department of Signal Theory, Networking and Communications, University of Granada 18010, Spain.,Andalusian Research Institute in Data, Science and Computational Intelligence, Spain
| | - Andrés Ortiz
- Department of Communications Engineering, University of Malaga 29010, Spain.,Andalusian Research Institute in Data, Science and Computational Intelligence, Spain
| | - Javier Ramírez
- Department of Signal Theory, Networking and Communications, University of Granada 18010, Spain.,Andalusian Research Institute in Data, Science and Computational Intelligence, Spain
| | - Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester LE1 7RH, UK
| | - Juan M Górriz
- Department of Signal Theory, Networking and Communications, University of Granada 18010, Spain.,Andalusian Research Institute in Data, Science and Computational Intelligence, Spain
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16
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Song C, Zhao W, Jiang H, Liu X, Duan Y, Yu X, Yu X, Zhang J, Kui J, Liu C, Tang Y. Stability Evaluation of Brain Changes in Parkinson's Disease Based on Machine Learning. Front Comput Neurosci 2021; 15:735991. [PMID: 34795570 PMCID: PMC8594429 DOI: 10.3389/fncom.2021.735991] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 09/24/2021] [Indexed: 02/05/2023] Open
Abstract
Structural MRI (sMRI) has been widely used to examine the cerebral changes that occur in Parkinson's disease (PD). However, previous studies have aimed for brain changes at the group level rather than at the individual level. Additionally, previous studies have been inconsistent regarding the changes they identified. It is difficult to identify which brain regions are the true biomarkers of PD. To overcome these two issues, we employed four different feature selection methods [ReliefF, graph-theory, recursive feature elimination (RFE), and stability selection] to obtain a minimal set of relevant features and nonredundant features from gray matter (GM) and white matter (WM). Then, a support vector machine (SVM) was utilized to learn decision models from selected features. Based on machine learning technique, this study has not only extended group level statistical analysis with identifying group difference to individual level with predicting patients with PD from healthy controls (HCs), but also identified most informative brain regions with feature selection methods. Furthermore, we conducted horizontal and vertical analyses to investigate the stability of the identified brain regions. On the one hand, we compared the brain changes found by different feature selection methods and considered these brain regions found by feature selection methods commonly as the potential biomarkers related to PD. On the other hand, we compared these brain changes with previous findings reported by conventional statistical analysis to evaluate their stability. Our experiments have demonstrated that the proposed machine learning techniques achieve satisfactory and robust classification performance. The highest classification performance was 92.24% (specificity), 92.42% (sensitivity), 89.58% (accuracy), and 89.77% (AUC) for GM and 71.93% (specificity), 74.87% (sensitivity), 71.18% (accuracy), and 71.82% (AUC) for WM. Moreover, most brain regions identified by machine learning were consistent with previous findings, which means that these brain regions are related to the pathological brain changes characteristic of PD and can be regarded as potential biomarkers of PD. Besides, we also found the brain abnormality of superior frontal gyrus (dorsolateral, SFGdor) and lingual gyrus (LING), which have been confirmed in other studies of PD. This further demonstrates that machine learning models are beneficial for clinicians as a decision support system in diagnosing PD.
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Affiliation(s)
- Chenggang Song
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, Chengdu, China
- College of Computer, Chengdu University, Chengdu, China
| | - Weidong Zhao
- College of Computer, Chengdu University, Chengdu, China
| | - Hong Jiang
- Department of Neurosurgery, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoju Liu
- Department of Abdominal Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yumei Duan
- Department of Computer and Software, Chengdu Jincheng College, Chengdu, China
| | - Xiaodong Yu
- College of Computer, Chengdu University, Chengdu, China
| | - Xi Yu
- College of Computer, Chengdu University, Chengdu, China
| | - Jian Zhang
- School of Physics and Electronic Engineering, Sichuan Normal University, Chengdu, China
| | - Jingyue Kui
- Department of Urology, Tonghai County People's Hospital, Yuxi, China
| | - Chang Liu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, Chengdu, China
- College of Computer, Chengdu University, Chengdu, China
| | - Yiqian Tang
- College of Computer, Chengdu University, Chengdu, China
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17
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Loh HW, Hong W, Ooi CP, Chakraborty S, Barua PD, Deo RC, Soar J, Palmer EE, Acharya UR. Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021). SENSORS 2021; 21:s21217034. [PMID: 34770340 PMCID: PMC8587636 DOI: 10.3390/s21217034] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/07/2021] [Accepted: 10/19/2021] [Indexed: 12/18/2022]
Abstract
Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
| | - Wanrong Hong
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
| | - Subrata Chakraborty
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Ravinesh C Deo
- School of Sciences, University of Southern Queensland, Springfield, QLD 4300, Australia
| | - Jeffrey Soar
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Elizabeth E Palmer
- Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick, NSW 2031, Australia
- School of Women's and Children's Health, University of New South Wales, Randwick, NSW 2031, Australia
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 413, Taiwan
- Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
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18
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Zou R, Lv X, Zhao J. SPChain: Blockchain-based medical data sharing and privacy-preserving eHealth system. Inf Process Manag 2021. [DOI: 10.1016/j.ipm.2021.102604] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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19
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Mirbabaie M, Hofeditz L, Frick NRJ, Stieglitz S. Artificial intelligence in hospitals: providing a status quo of ethical considerations in academia to guide future research. AI & SOCIETY 2021; 37:1361-1382. [PMID: 34219989 PMCID: PMC8238382 DOI: 10.1007/s00146-021-01239-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 05/25/2021] [Indexed: 12/23/2022]
Abstract
The application of artificial intelligence (AI) in hospitals yields many advantages but also confronts healthcare with ethical questions and challenges. While various disciplines have conducted specific research on the ethical considerations of AI in hospitals, the literature still requires a holistic overview. By conducting a systematic discourse approach highlighted by expert interviews with healthcare specialists, we identified the status quo of interdisciplinary research in academia on ethical considerations and dimensions of AI in hospitals. We found 15 fundamental manuscripts by constructing a citation network for the ethical discourse, and we extracted actionable principles and their relationships. We provide an agenda to guide academia, framed under the principles of biomedical ethics. We provide an understanding of the current ethical discourse of AI in clinical environments, identify where further research is pressingly needed, and discuss additional research questions that should be addressed. We also guide practitioners to acknowledge AI-related benefits in hospitals and to understand the related ethical concerns. Supplementary Information The online version contains supplementary material available at 10.1007/s00146-021-01239-4.
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Affiliation(s)
- Milad Mirbabaie
- Faculty of Business Administration and Economics, Paderborn University, Paderborn, Germany
| | - Lennart Hofeditz
- Professional Communication in Electronic Media / Social Media, University of Duisburg-Essen, Duisburg, Germany
| | - Nicholas R. J. Frick
- Professional Communication in Electronic Media / Social Media, University of Duisburg-Essen, Duisburg, Germany
| | - Stefan Stieglitz
- Professional Communication in Electronic Media / Social Media, University of Duisburg-Essen, Duisburg, Germany
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20
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Delfino E, Pastore A, Zucchini E, Cruz MFP, Ius T, Vomero M, D'Ausilio A, Casile A, Skrap M, Stieglitz T, Fadiga L. Prediction of Speech Onset by Micro-Electrocorticography of the Human Brain. Int J Neural Syst 2021; 31:2150025. [PMID: 34130614 DOI: 10.1142/s0129065721500258] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Recent technological advances show the feasibility of offline decoding speech from neuronal signals, paving the way to the development of chronically implanted speech brain computer interfaces (sBCI). Two key steps that still need to be addressed for the online deployment of sBCI are, on the one hand, the definition of relevant design parameters of the recording arrays, on the other hand, the identification of robust physiological markers of the patient's intention to speak, which can be used to online trigger the decoding process. To address these issues, we acutely recorded speech-related signals from the frontal cortex of two human patients undergoing awake neurosurgery for brain tumors using three different micro-electrocorticographic ([Formula: see text]ECoG) devices. First, we observed that, at the smallest investigated pitch (600[Formula: see text][Formula: see text]m), neighboring channels are highly correlated, suggesting that more closely spaced electrodes would provide some redundant information. Second, we trained a classifier to recognize speech-related motor preparation from high-gamma oscillations (70-150[Formula: see text]Hz), demonstrating that these neuronal signals can be used to reliably predict speech onset. Notably, our model generalized both across subjects and recording devices showing the robustness of its performance. These findings provide crucial information for the design of future online sBCI.
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Affiliation(s)
- Emanuela Delfino
- Center for Translational Neurophysiology, Istituto Italiano di Tecnologia, Via Fossato di Mortara 17-19, Ferrara 44121, Italy.,Section of Physiology, University of Ferrara, Via Fossato di Mortara 17-19, Ferrara 44121, Italy
| | - Aldo Pastore
- Center for Translational Neurophysiology, Istituto Italiano di Tecnologia, Via Fossato di Mortara 17-19, Ferrara 44121, Italy.,Section of Physiology, University of Ferrara, Via Fossato di Mortara 17-19, Ferrara 44121, Italy
| | - Elena Zucchini
- Center for Translational Neurophysiology, Istituto Italiano di Tecnologia, Via Fossato di Mortara 17-19, Ferrara 44121, Italy.,Section of Physiology, University of Ferrara, Via Fossato di Mortara 17-19, Ferrara 44121, Italy
| | - Maria Francisca Porto Cruz
- Center for Translational Neurophysiology, Istituto Italiano di Tecnologia, Via Fossato di Mortara 17-19, Ferrara 44121, Italy.,Section of Physiology, University of Ferrara, Via Fossato di Mortara 17-19, Ferrara 44121, Italy.,Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering (IMTEK), University of Freiburg, Georges-Köhler-Allee 102, Freiburg im Breisgau 79110, Germany
| | - Tamara Ius
- Struttura Complessa di Neurochirurgia, Azienda Ospedaliero-Universitaria Santa Maria, della Misericordia, Piazzale Santa Maria, della Misericordia 15, Udine 33100, Italy
| | - Maria Vomero
- Bioelectronic Systems Laboratory, Columbia University, 500 West 120th Street, New York, NY 10027, USA
| | - Alessandro D'Ausilio
- Center for Translational Neurophysiology, Istituto Italiano di Tecnologia, Via Fossato di Mortara 17-19, Ferrara 44121, Italy.,Section of Physiology, University of Ferrara, Via Fossato di Mortara 17-19, Ferrara 44121, Italy
| | - Antonino Casile
- Center for Translational Neurophysiology, Istituto Italiano di Tecnologia, Via Fossato di Mortara 17-19, Ferrara 44121, Italy
| | - Miran Skrap
- Struttura Complessa di Neurochirurgia, Azienda Ospedaliero-Universitaria Santa Maria, della Misericordia, Piazzale Santa Maria, della Misericordia 15, Udine 33100, Italy
| | - Thomas Stieglitz
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering (IMTEK), University of Freiburg, Georges-Köhler-Allee 102, Freiburg im Breisgau 79110, Germany.,BrainLinks-BrainTools Center, University of Freiburg, Georges-Köhler-Allee 80, Freiburg im Breisgau 79110, Germany
| | - Luciano Fadiga
- Center for Translational Neurophysiology, Istituto Italiano di Tecnologia, Via Fossato di Mortara 17-19, Ferrara 44121, Italy.,Section of Physiology, University of Ferrara, Via Fossato di Mortara 17-19, Ferrara 44121, Italy
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21
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Application of Genetic Algorithms for the Selection of Neural Network Architecture in the Monitoring System for Patients with Parkinson’s Disease. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11125470] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This article describes an approach for collecting and pre-processing phone owner data, including their voice, in order to classify their condition using data mining methods. The most important research results presented in this article are the developed approaches for the processing of patient voices and the use of genetic algorithms to select the architecture of the neural network in the monitoring system for patients with Parkinson’s disease. The process used to pre-process a person’s voice is described in order to determine the main parameters that can be used in assessing a person’s condition. It is shown that the efficiency of using genetic algorithms for constructing neural networks depends on the composition of the data. As a result, the best result in the accuracy of assessing the patient’s condition can be obtained by a hybrid approach, where a part of the neural network architecture is selected analytically manually, while the other part is built automatically.
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22
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Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-021-00555-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
AbstractThe diagnosis of diseases is decisive for planning proper treatment and ensuring the well-being of patients. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. While the current literature has examined various approaches to diagnosing various diseases, an overview of fields in which AI has been applied, including their performance aiming to identify emergent digitalized healthcare services, has not yet been adequately realized in extant research. By conducting a critical review, we portray the AI landscape in diagnostics and provide a snapshot to guide future research. This paper extends academia by proposing a research agenda. Practitioners understand the extent to which AI improves diagnostics and how healthcare benefits from it. However, several issues need to be addressed before successful application of AI in disease diagnostics can be achieved.
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23
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Mei J, Desrosiers C, Frasnelli J. Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature. Front Aging Neurosci 2021; 13:633752. [PMID: 34025389 PMCID: PMC8134676 DOI: 10.3389/fnagi.2021.633752] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 03/22/2021] [Indexed: 12/26/2022] Open
Abstract
Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.
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Affiliation(s)
- Jie Mei
- Chemosensory Neuroanatomy Lab, Department of Anatomy, Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, QC, Canada
| | - Christian Desrosiers
- Laboratoire d'Imagerie, de Vision et d'Intelligence Artificielle (LIVIA), Department of Software and IT Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | - Johannes Frasnelli
- Chemosensory Neuroanatomy Lab, Department of Anatomy, Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, QC, Canada
- Centre de Recherche de l'Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l'Île-de-Montréal (CIUSSS du Nord-de-l'Île-de-Montréal), Montreal, QC, Canada
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24
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Movahed RA, Jahromi GP, Shahyad S, Meftahi GH. A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis. J Neurosci Methods 2021; 358:109209. [PMID: 33957158 DOI: 10.1016/j.jneumeth.2021.109209] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 04/22/2021] [Accepted: 04/26/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is a prevalent mental illness that is diagnosed through questionnaire-based approaches; however, these methods may not lead to an accurate diagnosis. In this regard, many studies have focused on using electroencephalogram (EEG) signals and machine learning techniques to diagnose MDD. NEW METHOD This paper proposes a machine learning framework for MDD diagnosis, which uses different types of EEG-derived features. The features are extracted using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis methods. The sequential backward feature selection (SBFS) algorithm is also employed to perform feature selection. Various classifier models are utilized to select the best one for the proposed framework. RESULTS The proposed method is validated with a public EEG dataset, including the EEG data of 34 MDD patients and 30 healthy subjects. The evaluation of the proposed framework is conducted using 10-fold cross-validation, providing the metrics such as accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR). The best performance of the proposed method has provided an average AC of 99%, SE of 98.4%, SP of 99.6%, F1 of 98.9%, and FDR of 0.4% using the support vector machine with RBF kernel (RBFSVM) classifier. COMPARISON WITH EXISTING METHODS The obtained results demonstrate that the proposed method outperforms other approaches for MDD classification based on EEG signals. CONCLUSIONS According to the obtained results, a highly accurate MDD diagnosis would be provided using the proposed method, while it can be utilized to develop a computer-aided diagnosis (CAD) tool for clinical purposes.
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Affiliation(s)
- Reza Akbari Movahed
- Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Gila Pirzad Jahromi
- Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran.
| | - Shima Shahyad
- Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
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25
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Tăuţan AM, Ionescu B, Santarnecchi E. Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques. Artif Intell Med 2021; 117:102081. [PMID: 34127244 DOI: 10.1016/j.artmed.2021.102081] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/21/2021] [Accepted: 04/26/2021] [Indexed: 10/21/2022]
Abstract
Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.
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Affiliation(s)
- Alexandra-Maria Tăuţan
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Bogdan Ionescu
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Harvard Medical School, 330 Brookline Avenue, Boston, United States.
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26
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Probabilistic Neural Network-based Model for Identification of Parkinson’s Disease by using Voice Profile and Personal Data. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-020-05080-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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27
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Sharma G, Parashar A, Joshi AM. DepHNN: A novel hybrid neural network for electroencephalogram (EEG)-based screening of depression. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102393] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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28
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Li Y, Zhang X, Wang P, Zhang X, Liu Y. Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson's disease. Neural Comput Appl 2021; 33:9733-9750. [PMID: 33584015 PMCID: PMC7871026 DOI: 10.1007/s00521-021-05741-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 01/16/2021] [Indexed: 11/26/2022]
Abstract
Speech diagnosis of Parkinson’s disease (PD) as a non-invasive and simple diagnosis method is particularly worth exploring. However, the number of samples of speech-based PD is relatively small, and there exist discrepancies in the distribution between subjects. In order to solve the two problems, a novel unsupervised two-step sparse transfer learning is proposed in this paper to tackle with PD speech diagnosis. In the first step, convolution sparse coding with the coordinate selection of samples and features is designed to learn speech structure from the source domain to replenish sample information of the target domain. In the second step, joint local structure distribution alignment is designed to maintain the neighbor relationship between the respective samples of the training set and test set, and reduce the distribution difference between the two domains at the same time. Two representative public PD speech datasets and one real-world PD speech dataset were exploited to verify the proposed method on PD speech diagnosis. Experimental results demonstrate that each step of the proposed method has a positive effect on the PD speech classification results, and it also delivers superior performance over the existing relative methods.
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Affiliation(s)
- Yongming Li
- School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400030 China
| | - Xinyue Zhang
- School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400030 China
| | - Pin Wang
- School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400030 China
| | - Xiaoheng Zhang
- School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400030 China
- Chongqing Radio and TV University, Chongqing, 400052 China
| | - Yuchuan Liu
- School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400030 China
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Tremblay C, Mei J, Frasnelli J. Olfactory bulb surroundings can help to distinguish Parkinson's disease from non-parkinsonian olfactory dysfunction. NEUROIMAGE-CLINICAL 2020; 28:102457. [PMID: 33068873 PMCID: PMC7567959 DOI: 10.1016/j.nicl.2020.102457] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 09/19/2020] [Accepted: 09/27/2020] [Indexed: 12/19/2022]
Abstract
BACKGROUND The olfactory bulb is one of the first regions of insult in Parkinson's disease (PD), consistent with the early onset of olfactory dysfunction. Investigations of the olfactory bulb may, therefore, help early pre-motor diagnosis. We aimed to investigate olfactory bulb and its surrounding regions in PD-related olfactory dysfunction when specifically compared to other forms of non-parkinsonian olfactory dysfunction (NPOD) and healthy controls. METHODS We carried out MRI-based olfactory bulb volume measurements from T2-weighted imaging in scans from 15 patients diagnosed with PD, 15 patients with either post-viral or sinonasal NPOD and 15 control participants. Further, we applied a deep learning model (convolutional neural network; CNN) to scans of the olfactory bulb and its surrounding area to classify PD-related scans from NPOD-related scans. RESULTS Compared to controls, both PD and NPOD patients had smaller olfactory bulbs, when measured manually (both p < .001) whereas no difference was found between PD and NPOD patients. In contrast, when a CNN was used to differentiate between PD patients and NPOD patients, an accuracy of 88.3% was achieved. The cortical area above the olfactory bulb which stretches around and into the olfactory sulcus appears to be a region of interest in the differentiation between PD and NPOD patients. CONCLUSION Measures from and around the olfactory bulb in combination with the use of a deep learning model may help differentiate PD patients from patients with NPOD, which may be used to develop early diagnostic tools based on olfactory dysfunction.
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Affiliation(s)
- Cécilia Tremblay
- Department of Anatomy, Université du Québec à Trois-Rivières, 3351 Boul. des Forges, Trois-Rivières, Québec G9A 5H7, Canada.
| | - Jie Mei
- Department of Anatomy, Université du Québec à Trois-Rivières, 3351 Boul. des Forges, Trois-Rivières, Québec G9A 5H7, Canada
| | - Johannes Frasnelli
- Department of Anatomy, Université du Québec à Trois-Rivières, 3351 Boul. des Forges, Trois-Rivières, Québec G9A 5H7, Canada; Research Center, Sacré-Coeur Hospital of Montreal, 5400 boul. Gouin Ouest, Montréal, Québec H4J 1C5, Canada
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Avuçlu E, Elen A. Evaluation of train and test performance of machine learning algorithms and Parkinson diagnosis with statistical measurements. Med Biol Eng Comput 2020; 58:2775-2788. [PMID: 32920727 DOI: 10.1007/s11517-020-02260-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 08/29/2020] [Indexed: 01/23/2023]
Abstract
Parkinson's disease is a neurological disorder that causes partial or complete loss of motor reflexes and speech and affects thinking, behavior, and other vital functions affecting the nervous system. Parkinson's disease causes impaired speech and motor abilities (writing, balance, etc.) in about 90% of patients and is often seen in older people. Some signs (deterioration of vocal cords) in medical voice recordings from Parkinson's patients are used to diagnose this disease. The database used in this study contains biomedical speech voice from 31 people of different age and sex related to this disease. The performance comparison of the machine learning algorithms k-Nearest Neighborhood (k-NN), Random Forest, Naive Bayes, and Support Vector Machine classifiers was performed with the used database. Moreover, the best classifier was determined for the diagnosis of Parkinson's disease. Eleven different training and test data (45 × 55, 50 × 50, 55 × 45, 60 × 40, 65 × 35, 70 × 30, 75 × 25, 80 × 20, 85 × 15, 90 × 10, 95 × 5) were processed separately. The data obtained from these training and tests were compared with statistical measurements. The training results of the k-NN classification algorithm were generally 100% successful. The best test result was obtained from Random Forest classifier with 85.81%. All statistical results and measured values are given in detail in the experimental studies section.Graphical abstract.
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Affiliation(s)
- Emre Avuçlu
- Department of Computer Technology, Aksaray University, Aksaray, Turkey.
| | - Abdullah Elen
- Department of Computer Technology, Karabuk University, Karabuk, Turkey
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Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network. J Med Syst 2020; 44:176. [DOI: 10.1007/s10916-020-01639-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 08/05/2020] [Indexed: 11/26/2022]
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Shi Y, Yang W, Thung KH, Wang H, Gao Y, Pan Y, Zhang L, Shen D. Learning-Based Computer-Aided Prescription Model for Parkinson's Disease: A Data-Driven Perspective. IEEE J Biomed Health Inform 2020; 25:3258-3269. [PMID: 32750966 DOI: 10.1109/jbhi.2020.3010946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In this article, we study a novel problem: "automatic prescription recommendation for PD patients." To realize this goal, we first build a dataset by collecting 1) symptoms of PD patients, and 2) their prescription drug provided by neurologists. Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug. Finally, for the new coming patients, we could recommend (predict) suitable prescription drug on their observed symptoms by our prescription model. From the methodology part, our proposed model, namely Prescription viA Learning lAtent Symptoms (PALAS), could recommend prescription using the multi-modality representation of the data. In PALAS, a latent symptom space is learned to better model the relationship between symptoms and prescription drug, as there is a large semantic gap between them. Moreover, we present an efficient alternating optimization method for PALAS. We evaluated our method using the data collected from 136 PD patients at Nanjing Brain Hospital, which can be regarded as a large dataset in PD research community. The experimental results demonstrate the effectiveness and clinical potential of our method in this recommendation task, if compared with other competing methods.
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Hussain AA, Bouachir O, Al-Turjman F, Aloqaily M. AI Techniques for COVID-19. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:128776-128795. [PMID: 34976554 PMCID: PMC8545328 DOI: 10.1109/access.2020.3007939] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 07/04/2020] [Indexed: 05/18/2023]
Abstract
Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the existing AI techniques in clinical data analysis, including neural systems, classical SVM, and edge significant learning. Also, an emphasis has been made on regions that utilize AI-oriented cloud computing in combating various similar viruses to COVID-19. This survey study is an attempt to benefit medical practitioners and medical researchers in overpowering their faced difficulties while handling COVID-19 big data. The investigated techniques put forth advances in medical data analysis with an exactness of up to 90%. We further end up with a detailed discussion about how AI implementation can be a huge advantage in combating various similar viruses.
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Affiliation(s)
- Adedoyin Ahmed Hussain
- Department of Computer EngineeringNear East University99138NicosiaMersin 10Turkey
- Research Centre for AI and IoTDepartment of Artificial Intelligence EngineeringNear East University99138NicosiaMersin 10Turkey
| | - Ouns Bouachir
- Department of Computer EngineeringZayed UniversityDubaiUnited Arab Emirates
- College of Technological InnovationZayed UniversityDubaiUnited Arab Emirates
| | - Fadi Al-Turjman
- Research Centre for AI and IoTDepartment of Artificial Intelligence EngineeringNear East University99138NicosiaMersin 10Turkey
| | - Moayad Aloqaily
- College of EngineeringAl Ain UniversityAl AinUnited Arab Emirates
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EANM practice guideline/SNMMI procedure standard for dopaminergic imaging in Parkinsonian syndromes 1.0. Eur J Nucl Med Mol Imaging 2020; 47:1885-1912. [PMID: 32388612 PMCID: PMC7300075 DOI: 10.1007/s00259-020-04817-8] [Citation(s) in RCA: 121] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 04/06/2020] [Indexed: 02/05/2023]
Abstract
Purpose This joint practice guideline or procedure standard was developed collaboratively by the European Association of Nuclear Medicine (EANM) and the Society of Nuclear Medicine and Molecular Imaging (SNMMI). The goal of this guideline is to assist nuclear medicine practitioners in recommending, performing, interpreting, and reporting the results of dopaminergic imaging in parkinsonian syndromes. Methods Currently nuclear medicine investigations can assess both presynaptic and postsynaptic function of dopaminergic synapses. To date both EANM and SNMMI have published procedural guidelines for dopamine transporter imaging with single photon emission computed tomography (SPECT) (in 2009 and 2011, respectively). An EANM guideline for D2 SPECT imaging is also available (2009). Since the publication of these previous guidelines, new lines of evidence have been made available on semiquantification, harmonization, comparison with normal datasets, and longitudinal analyses of dopamine transporter imaging with SPECT. Similarly, details on acquisition protocols and simplified quantification methods are now available for dopamine transporter imaging with PET, including recently developed fluorinated tracers. Finally, [18F]fluorodopa PET is now used in some centers for the differential diagnosis of parkinsonism, although procedural guidelines aiming to define standard procedures for [18F]fluorodopa imaging in this setting are still lacking. Conclusion All these emerging issues are addressed in the present procedural guidelines for dopaminergic imaging in parkinsonian syndromes.
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Assessment of the Status of Patients with Parkinson's Disease Using Neural Networks and Mobile Phone Sensors. Diagnostics (Basel) 2020; 10:diagnostics10040214. [PMID: 32290633 PMCID: PMC7235735 DOI: 10.3390/diagnostics10040214] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 04/09/2020] [Accepted: 04/11/2020] [Indexed: 11/16/2022] Open
Abstract
Parkinson's disease (PD) is one of the most common chronic neurological diseases and one of the significant causes of disability for middle-aged and elderly people. Monitoring the patient's condition and its compliance is the key to the success of the correction of the main clinical manifestations of PD, including the almost inevitable modification of the clinical picture of the disease against the background of prolonged dopaminergic therapy. In this article, we proposed an approach to assessing the condition of patients with PD using deep recurrent neural networks, trained on data measured using mobile phones. The data was received in two modes: background (data from the phone's sensors) and interactive (data directly entered by the user). For the classification of the patient's condition, we built various models of the neural network. Testing of these models showed that the most efficient was a recurrent network with two layers. The results of the experiment show that with a sufficient amount of the training sample, it is possible to build a neural network that determines the condition of the patient according to the data from the mobile phone sensors with a high probability.
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Li M, Lin F, Xu G. A TrAdaBoost Method for Detecting Multiple Subjects' N200 and P300 Potentials Based on Cross-Validation and an Adaptive Threshold. Int J Neural Syst 2020; 30:2050009. [PMID: 32116091 DOI: 10.1142/s0129065720500094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Traditional training methods need to collect a large amount of data for every subject to train a subject-specific classifier, which causes subjects fatigue and training burden. This study proposes a novel training method, TrAdaBoost based on cross-validation and an adaptive threshold (CV-T-TAB), to reduce the amount of data required for training by selecting and combining multiple subjects' classifiers that perform well on a new subject to train a classifier. This method adopts cross-validation to extend the amount of the new subject's training data and sets an adaptive threshold to select the optimal combination of the classifiers. Twenty-five subjects participated in the N200- and P300-based brain-computer interface. The study compares CV-T-TAB to five traditional training methods by testing them on the training of a support vector machine. The accuracy, information transfer rate, area under the curve, recall and precision are used to evaluate the performances under nine conditions with different amounts of data. CV-T-TAB outperforms the other methods and retains a high accuracy even when the amount of data is reduced to one-third of the original amount. The results imply that CV-T-TAB is effective in improving the performance of a subject-specific classifier with a small amount of data by adopting multiple subjects' classifiers, which reduces the training cost.
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Affiliation(s)
- Mengfan Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, P. R. China
| | - Fang Lin
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, P. R. China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, P. R. China
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Darbin O, Hatanaka N, Takara S, Kaneko N, Chiken S, Naritoku D, Martino A, Nambu A. Parkinsonism Differently Affects the Single Neuronal Activity in the Primary and Supplementary Motor Areas in Monkeys: An Investigation in Linear and Nonlinear Domains. Int J Neural Syst 2020; 30:2050010. [PMID: 32019380 DOI: 10.1142/s0129065720500100] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The changes in neuronal firing activity in the primary motor cortex (M1) and supplementary motor area (SMA) were compared in monkeys rendered parkinsonian by treatment with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. The neuronal dynamic was characterized using mathematical tools defined in different frameworks (rate, oscillations or complex patterns). Then, and for each cortical area, multivariate and discriminate analyses were further performed on these features to identify those important to differentiate between the normal and the pathological neuronal activity. Our results show a different order in the importance of the features to discriminate the pathological state in each cortical area which suggests that the M1 and the SMA exhibit dissimilarities in their neuronal alterations induced by parkinsonism. Our findings highlight the need for multiple mathematical frameworks to best characterize the pathological neuronal activity related to parkinsonism. Future translational studies are warranted to investigate the causal relationships between cortical region-specificities, dominant pathological hallmarks and symptoms.
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Affiliation(s)
- Olivier Darbin
- Department of Neurology, University South Alabama, 307 University Blvd, Mobile, AL 36688, USA
| | - Nobuhiko Hatanaka
- Division of System Neurophysiology, National Institute for Physiological Sciences and Department of Physiological Sciences, SOKENDAI (Graduate University for Advanced Studies), Okazaki, Aichi 444-8585, Japan
| | - Sayuki Takara
- Division of System Neurophysiology, National Institute for Physiological Sciences and Department of Physiological Sciences, SOKENDAI (Graduate University for Advanced Studies), Okazaki, Aichi 444-8585, Japan
| | - Nobuya Kaneko
- Division of System Neurophysiology, National Institute for Physiological Sciences and Department of Physiological Sciences, SOKENDAI (Graduate University for Advanced Studies), Okazaki, Aichi 444-8585, Japan
| | - Satomi Chiken
- Division of System Neurophysiology, National Institute for Physiological Sciences and Department of Physiological Sciences, SOKENDAI (Graduate University for Advanced Studies), Okazaki, Aichi 444-8585, Japan
| | - Dean Naritoku
- Department of Neurology, University South Alabama, 307 University Blvd, Mobile, AL 36688, USA
| | - Anthony Martino
- Department of Neurology, University South Alabama, 307 University Blvd, Mobile, AL 36688, USA
| | - Atsushi Nambu
- Division of System Neurophysiology, National Institute for Physiological Sciences and Department of Physiological Sciences, SOKENDAI (Graduate University for Advanced Studies), Okazaki, Aichi 444-8585, Japan
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Esposti F, Banfi G. Fighting healthcare rocketing costs with value-based medicine: the case of stroke management. BMC Health Serv Res 2020; 20:75. [PMID: 32007089 PMCID: PMC6995121 DOI: 10.1186/s12913-020-4925-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 01/22/2020] [Indexed: 12/21/2022] Open
Abstract
Value-Based Medicine (VBM) is imposing itself as 'a new paradigm in healthcare management and medical practice.In this perspective paper, we discuss the role of VBM in dealing with the large productivity issue of the healthcare industry and examine some of the worldwide industrial and technological trends linked with VBM introduction. To clarify the points, we discuss examples of VBM management of stroke patients.In our conclusions, we support the idea of VBM as a strategic aid to manage rising costs in healthcare, and we explore the idea that VBM, by establishing value-generating networks among different healthcare stakeholders, can serve as the long sought-after redistributive mechanism that compensate patients for the industrial exploitation of their personal medical records.
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Affiliation(s)
- Federico Esposti
- Università Vita-Salute San Raffaele, via Olgettina 58, Milan, Italy
- IRCCS Ospedale San Raffaele, via Olgettina 60, Milan, Italy
| | - Giuseppe Banfi
- Università Vita-Salute San Raffaele, via Olgettina 58, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, via Galeazzi 4, Milan, Italy
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Nguyen AA, Maia PD, Gao X, Damasceno PF, Raj A. Dynamical Role of Pivotal Brain Regions in Parkinson Symptomatology Uncovered with Deep Learning. Brain Sci 2020; 10:E73. [PMID: 32019067 PMCID: PMC7071401 DOI: 10.3390/brainsci10020073] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 01/22/2020] [Accepted: 01/26/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The release of a broad, longitudinal anatomical dataset by the Parkinson's Progression Markers Initiative promoted a surge of machine-learning studies aimed at predicting disease onset and progression. However, the excessive number of features used in these models often conceals their relationship to the Parkinsonian symptomatology. OBJECTIVES The aim of this study is two-fold: (i) to predict future motor and cognitive impairments up to four years from brain features acquired at baseline; and (ii) to interpret the role of pivotal brain regions responsible for different symptoms from a neurological viewpoint. METHODS We test several deep-learning neural network configurations, and report our best results obtained with an autoencoder deep-learning model, run on a 5-fold cross-validation set. Comparison with Existing Methods: Our approach improves upon results from standard regression and others. It also includes neuroimaging biomarkers as features. RESULTS The relative contributions of pivotal brain regions to each impairment change over time, suggesting a dynamical reordering of culprits as the disease progresses. Specifically, the Putamen is initially the most critical region accounting for the overall cognitive state, only being surpassed by the Substantia Nigra in later years. The Pallidum is the first region to influence motor scores, followed by the parahippocampal and ambient gyri, and the anterior orbital gyrus. CONCLUSIONS While the causal link between regional brain atrophy and Parkinson symptomatology is poorly understood, our methods demonstrate that the contributions of pivotal regions to cognitive and motor impairments are more dynamical than generally appreciated.
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Affiliation(s)
- Alex A. Nguyen
- Department of Radiology and Biomedical Imaging, UC San Francisco, San Francisco, CA 94107, USA; (A.A.N.); (X.G.); (P.F.D.)
| | - Pedro D. Maia
- Department of Radiology and Biomedical Imaging, UC San Francisco, San Francisco, CA 94107, USA; (A.A.N.); (X.G.); (P.F.D.)
| | - Xiao Gao
- Department of Radiology and Biomedical Imaging, UC San Francisco, San Francisco, CA 94107, USA; (A.A.N.); (X.G.); (P.F.D.)
| | - Pablo F. Damasceno
- Department of Radiology and Biomedical Imaging, UC San Francisco, San Francisco, CA 94107, USA; (A.A.N.); (X.G.); (P.F.D.)
- Bakar Computational Health Sciences Institute, UC San Francisco, San Francisco, CA 94158, USA
- Center for Intelligent Imaging, UC San Francisco, San Francisco, CA 94107, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, UC San Francisco, San Francisco, CA 94107, USA; (A.A.N.); (X.G.); (P.F.D.)
- Bakar Computational Health Sciences Institute, UC San Francisco, San Francisco, CA 94158, USA
- Center for Intelligent Imaging, UC San Francisco, San Francisco, CA 94107, USA
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Rafiei MH, Kelly KM, Borstad AL, Adeli H, Gauthier LV. Predicting Improved Daily Use of the More Affected Arm Poststroke Following Constraint-Induced Movement Therapy. Phys Ther 2019; 99:1667-1678. [PMID: 31504952 PMCID: PMC7105113 DOI: 10.1093/ptj/pzz121] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 03/02/2019] [Accepted: 04/24/2019] [Indexed: 01/01/2023]
Abstract
BACKGROUND Constraint-induced movement therapy (CI therapy) produces, on average, large and clinically meaningful improvements in the daily use of a more affected upper extremity in individuals with hemiparesis. However, individual responses vary widely. OBJECTIVE The study objective was to investigate the extent to which individual characteristics before treatment predict improved use of the more affected arm following CI therapy. DESIGN This study was a retrospective analysis of 47 people who had chronic (> 6 months) mild to moderate upper extremity hemiparesis and were consecutively enrolled in 2 CI therapy randomized controlled trials. METHODS An enhanced probabilistic neural network model predicted whether individuals showed a low, medium, or high response to CI therapy, as measured with the Motor Activity Log, on the basis of the following baseline assessments: Wolf Motor Function Test, Semmes-Weinstein Monofilament Test of touch threshold, Motor Activity Log, and Montreal Cognitive Assessment. Then, a neural dynamic classification algorithm was applied to improve prognostic accuracy using the most accurate combination obtained in the previous step. RESULTS Motor ability and tactile sense predicted improvement in arm use for daily activities following intensive upper extremity rehabilitation with an accuracy of nearly 100%. Complex patterns of interaction among these predictors were observed. LIMITATIONS The fact that this study was a retrospective analysis with a moderate sample size was a limitation. CONCLUSIONS Advanced machine learning/classification algorithms produce more accurate personalized predictions of rehabilitation outcomes than commonly used general linear models.
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Affiliation(s)
- Mohammad H Rafiei
- Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Kristina M Kelly
- Department of Neurology, The Ohio State University, Columbus, Ohio
| | - Alexandra L Borstad
- Department of Physical Therapy, The College of St Scholastica, Duluth, Minnesota
| | - Hojjat Adeli
- Department of Biomedical Informatics, Department of Neurology, Department of Neuroscience, The Ohio State University
| | - Lynne V Gauthier
- Department of Physical Therapy and Kinesiology, University of Massachusetts Lowell, 3 Solomon Way, Weed Hall 218D, Lowell, MA 01854 (USA)
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Wu Y, Jiang JH, Chen L, Lu JY, Ge JJ, Liu FT, Yu JT, Lin W, Zuo CT, Wang J. Use of radiomic features and support vector machine to distinguish Parkinson's disease cases from normal controls. ANNALS OF TRANSLATIONAL MEDICINE 2019; 7:773. [PMID: 32042789 DOI: 10.21037/atm.2019.11.26] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background Parkinson's disease (PD) is an irreversible neurodegenerative disease. The diagnosis of PD based on neuroimaging is usually with low-level or deep learning features, which results in difficulties in achieving precision classification or interpreting the clinical significance. Herein, we aimed to extract high-order features by using radiomics approach and achieve acceptable diagnosis accuracy in PD. Methods In this retrospective multicohort study, we collected 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images and clinical scale [the Unified Parkinson's Disease Rating Scale (UPDRS) and Hoehn & Yahr scale (H&Y)] from two cohorts. One cohort from Huashan Hospital had 91 normal controls (NC) and 91 PD patients (UPDRS: 22.7±11.7, H&Y: 1.8±0.8), and the other cohort from Wuxi 904 Hospital had 26 NC and 22 PD patients (UPDRS: 20.9±11.6, H&Y: 1.7±0.9). The Huashan cohort was used as the training and test sets by 5-fold cross-validation and the Wuxi cohort was used as another separate test set. After identifying regions of interests (ROIs) based on the atlas-based method, radiomic features were extracted and selected by using autocorrelation and fisher score algorithm. A support vector machine (SVM) was trained to classify PD and NC based on selected radiomic features. In the comparative experiment, we compared our method with the traditional voxel values method. To guarantee the robustness, above processes were repeated in 500 times. Results Twenty-six brain ROIs were identified. Six thousand one hundred and ten radiomic features were extracted in total. Among them 30 features were remained after feature selection. The accuracies of the proposed method achieved 90.97%±4.66% and 88.08%±5.27% in Huashan and Wuxi test sets, respectively. Conclusions This study showed that radiomic features and SVM could be used to distinguish between PD and NC based on 18F-FDG PET images.
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Affiliation(s)
- Yue Wu
- Department of Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
| | - Jie-Hui Jiang
- Department of Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
| | - Li Chen
- Department of Medical Ultrasound, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jia-Ying Lu
- Department of PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jing-Jie Ge
- Department of PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Feng-Tao Liu
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jin-Tai Yu
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Wei Lin
- Department of Neurosurgery, 904 Hospital of PLA, Anhui Medical University, Wuxi 214000, China
| | - Chuan-Tao Zuo
- Department of PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jian Wang
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
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Lo Giudice P, Mammone N, Morabito FC, Pizzimenti RG, Ursino D, Virgili L. Leveraging network analysis to support experts in their analyses of subjects with MCI and AD. Med Biol Eng Comput 2019; 57:1961-1983. [PMID: 31301007 DOI: 10.1007/s11517-019-02004-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 06/09/2019] [Indexed: 11/25/2022]
Abstract
In this paper, we propose a network analysis-based approach to help experts in their analyses of subjects with mild cognitive impairment (hereafter, MCI) and Alzheimer's disease (hereafter, AD) and to investigate the evolution of these subjects over time. The inputs of our approach are the electroencephalograms (hereafter, EEGs) of the patients to analyze, performed at a certain time and, again, 3 months later. Given an EEG of a subject, our approach constructs a network with nodes that represent the electrodes and edges that denote connections between electrodes. Then, it applies several network-based techniques allowing the investigation of subjects with MCI and AD and the analysis of their evolution over time. (i) A connection coefficient, supporting experts to distinguish patients with MCI from patients with AD; (ii) A conversion coefficient, supporting experts to verify if a subject with MCI is converting to AD; (iii) Some network motifs, i.e., network patterns very frequent in one kind of patient and absent, or very rare, in the other. Patients with AD, just by the very nature of their condition, cannot be forced to stay motionless while undergoing examinations for a long time. EEG is a non-invasive examination that can be easily done on them. Since AD and MCI, if prodromal to AD, are associated with a loss of cortical connections, the adoption of network analysis appears suitable to investigate the effects of the progression of the disease on EEG. This paper confirms the suitability of this idea Graphical Abstract Ability of our proposed model to distinguish a control subject from a patient with MCI and a patient with AD. Blue edges represent strong connections among the corresponding brain areas; red edges denote middle connections, whereas green edges indicate weak connections. In the control subject (at the top), most connections are blue. In the patient with MCI (at the middle), most connections are red and green. In the patient with AD (at the bottom), most connections are either absent or green. .
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Affiliation(s)
- Paolo Lo Giudice
- DIIES, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy
| | - Nadia Mammone
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy
| | | | | | | | - Luca Virgili
- DII, Polytechnic University of Marche, Ancona, Italy
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A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer’s disease using EEG signals. J Neurosci Methods 2019; 322:88-95. [DOI: 10.1016/j.jneumeth.2019.04.013] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 04/27/2019] [Accepted: 04/27/2019] [Indexed: 11/20/2022]
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45
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Cao S, Zhang G, Liu P, Zhang X, Neri F. Cloud-assisted secure eHealth systems for tamper-proofing EHR via blockchain. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2019.02.038] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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46
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A survey on computer-assisted Parkinson's Disease diagnosis. Artif Intell Med 2019; 95:48-63. [DOI: 10.1016/j.artmed.2018.08.007] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 06/14/2018] [Accepted: 08/25/2018] [Indexed: 12/28/2022]
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47
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Pereira DR, Piteri MA, Souza AN, Papa JP, Adeli H. FEMa: a finite element machine for fast learning. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04146-4] [Citation(s) in RCA: 125] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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48
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Tang J, Yang B, Adams MP, Shenkov NN, Klyuzhin IS, Fotouhi S, Davoodi-Bojd E, Lu L, Soltanian-Zadeh H, Sossi V, Rahmim A. Artificial Neural Network–Based Prediction of Outcome in Parkinson’s Disease Patients Using DaTscan SPECT Imaging Features. Mol Imaging Biol 2019; 21:1165-1173. [DOI: 10.1007/s11307-019-01334-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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49
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Gómez-Vilda P, Gómez-Rodellar A, Vicente JMF, Mekyska J, Palacios-Alonso D, Rodellar-Biarge V, Álvarez-Marquina A, Eliasova I, Kostalova M, Rektorova I. Neuromechanical Modelling of Articulatory Movements from Surface Electromyography and Speech Formants. Int J Neural Syst 2019; 29:1850039. [DOI: 10.1142/s0129065718500399] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Speech articulation is produced by the movements of muscles in the larynx, pharynx, mouth and face. Therefore speech shows acoustic features as formants which are directly related with neuromotor actions of these muscles. The first two formants are strongly related with jaw and tongue muscular activity. Speech can be used as a simple and ubiquitous signal, easy to record and process, either locally or on e-Health platforms. This fact may open a wide set of applications in the study of functional grading and monitoring neurodegenerative diseases. A relevant question, in this sense, is how far speech correlates and neuromotor actions are related. This preliminary study is intended to find answers to this question by using surface electromyographic recordings on the masseter and the acoustic kinematics related with the first formant. It is shown in the study that relevant correlations can be found among the surface electromyographic activity (dynamic muscle behavior) and the positions and first derivatives of the first formant (kinematic variables related to vertical velocity and acceleration of the joint jaw and tongue biomechanical system). As an application example, it is shown that the probability density function associated to these kinematic variables is more sensitive than classical features as Vowel Space Area (VSA) or Formant Centralization Ratio (FCR) in characterizing neuromotor degeneration in Parkinson’s Disease.
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Affiliation(s)
- Pedro Gómez-Vilda
- Neuromorphic Speech Processing Lab, Center for Biomedical Technology, Universidad Politécnica de, Madrid Campus de Montegancedo, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Andrés Gómez-Rodellar
- Neuromorphic Speech Processing Lab, Center for Biomedical Technology, Universidad Politécnica de, Madrid Campus de Montegancedo, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - José M. Ferrández Vicente
- Universidad Politécnica de Cartagena, Campus Universitario Muralla del Mar, Pza. Hospital 1, 30202 Cartagena, Spain
| | - Jiri Mekyska
- Department of Telecommunications, Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
| | - Daniel Palacios-Alonso
- Neuromorphic Speech Processing Lab, Center for Biomedical Technology, Universidad Politécnica de, Madrid Campus de Montegancedo, 28223 Pozuelo de Alarcón, Madrid, Spain
- Escuela Técnica Superior de Ingeniería Informática - Universidad Rey Juan Carlos, Campus de Móstoles, Tulipán s/n, 28933 Móstoles, Madrid, Spain
| | - Victoria Rodellar-Biarge
- Neuromorphic Speech Processing Lab, Center for Biomedical Technology, Universidad Politécnica de, Madrid Campus de Montegancedo, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Agustín Álvarez-Marquina
- Neuromorphic Speech Processing Lab, Center for Biomedical Technology, Universidad Politécnica de, Madrid Campus de Montegancedo, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Ilona Eliasova
- First Department of Neurology, Faculty of Medicine and St. Anne’s University Hospital, Masaryk University, Pekarska 53, 656 91 Brno, Czech Republic
- Applied Neuroscience Research Group, Central European Institute of Technology, CEITEC, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
| | - Milena Kostalova
- Applied Neuroscience Research Group, Central European Institute of Technology, CEITEC, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
- Department of Neurology, Faculty Hospital and Masaryk University, Jihlavska 20, 63900 Brno, Czech Republic
| | - Irena Rektorova
- First Department of Neurology, Faculty of Medicine and St. Anne’s University Hospital, Masaryk University, Pekarska 53, 656 91 Brno, Czech Republic
- Applied Neuroscience Research Group, Central European Institute of Technology, CEITEC, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
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Sakai K, Yamada K. Machine learning studies on major brain diseases: 5-year trends of 2014–2018. Jpn J Radiol 2018; 37:34-72. [DOI: 10.1007/s11604-018-0794-4] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 11/14/2018] [Indexed: 12/17/2022]
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