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Majhi B, Kashyap A, Mohanty SS, Dash S, Mallik S, Li A, Zhao Z. An improved method for diagnosis of Parkinson's disease using deep learning models enhanced with metaheuristic algorithm. BMC Med Imaging 2024; 24:156. [PMID: 38910241 PMCID: PMC11194992 DOI: 10.1186/s12880-024-01335-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 06/14/2024] [Indexed: 06/25/2024] Open
Abstract
Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtained safely and non-invasively using medical imaging techniques like magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT). For accurate diagnosis of PD, powerful machine learning and deep learning models as well as the effectiveness of medical imaging tools for assessing neurological health are required. This study proposes four deep learning models with a hybrid model for the early detection of PD. For the simulation study, two standard datasets are chosen. Further to improve the performance of the models, grey wolf optimization (GWO) is used to automatically fine-tune the hyperparameters of the models. The GWO-VGG16, GWO-DenseNet, GWO-DenseNet + LSTM, GWO-InceptionV3 and GWO-VGG16 + InceptionV3 are applied to the T1,T2-weighted and SPECT DaTscan datasets. All the models performed well and obtained near or above 99% accuracy. The highest accuracy of 99.94% and AUC of 99.99% is achieved by the hybrid model (GWO-VGG16 + InceptionV3) for T1,T2-weighted dataset and 100% accuracy and 99.92% AUC is recorded for GWO-VGG16 + InceptionV3 models using SPECT DaTscan dataset.
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Affiliation(s)
- Babita Majhi
- Department of CSIT, Central University, Guru Ghasidas Vishwavidyalaya, Bilaspur, Chhattisgarh, 495009, India
| | - Aarti Kashyap
- Department of CSIT, Central University, Guru Ghasidas Vishwavidyalaya, Bilaspur, Chhattisgarh, 495009, India
| | - Siddhartha Suprasad Mohanty
- Department of CSIT, Central University, Guru Ghasidas Vishwavidyalaya, Bilaspur, Chhattisgarh, 495009, India
| | - Sujata Dash
- Department of Information Technology, Nagaland University, Dimapur, Nagaland, India
| | - Saurav Mallik
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
| | - Aimin Li
- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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Abumalloh RA, Nilashi M, Samad S, Ahmadi H, Alghamdi A, Alrizq M, Alyami S. Parkinson's disease diagnosis using deep learning: A bibliometric analysis and literature review. Ageing Res Rev 2024; 96:102285. [PMID: 38554785 DOI: 10.1016/j.arr.2024.102285] [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] [Received: 08/03/2023] [Revised: 03/20/2024] [Accepted: 03/24/2024] [Indexed: 04/02/2024]
Abstract
Parkinson's Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis.
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Affiliation(s)
- Rabab Ali Abumalloh
- Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar
| | - Mehrbakhsh Nilashi
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam; School of Computer Science, Duy Tan University, Da Nang, Vietnam; UCSI Graduate Business School, UCSI University, No. 1 Jalan Menara Gading, UCSI Heights, Cheras, Kuala Lumpur 56000, Malaysia; Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, Penang 11800, Malaysia.
| | - Sarminah Samad
- Faculty of Business, UNITAR International University, Tierra Crest, Jalan SS6/3, Petaling Jaya, Selangor 47301, Malaysia
| | - Hossein Ahmadi
- Centre for Health Technology, Faculty of Health, University of Plymouth, Plymouth PL4 8AA, UK
| | - Abdullah Alghamdi
- Information Systems Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia; AI Lab, Scientific and Engineering Research Center (SERC), Najran University, Najran, Saudi Arabia
| | - Mesfer Alrizq
- Information Systems Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia; AI Lab, Scientific and Engineering Research Center (SERC), Najran University, Najran, Saudi Arabia
| | - Sultan Alyami
- AI Lab, Scientific and Engineering Research Center (SERC), Najran University, Najran, Saudi Arabia; Computer Science Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
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3
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Camacho M, Wilms M, Almgren H, Amador K, Camicioli R, Ismail Z, Monchi O, Forkert ND. Exploiting macro- and micro-structural brain changes for improved Parkinson's disease classification from MRI data. NPJ Parkinsons Dis 2024; 10:43. [PMID: 38409244 PMCID: PMC10897162 DOI: 10.1038/s41531-024-00647-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 01/22/2024] [Indexed: 02/28/2024] Open
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data. The model was trained using one of the largest collections of T1-weighted and diffusion-tensor magnetic resonance imaging (MRI) datasets. A total of 1264 datasets from eight different studies were collected, including 611 PD patients and 653 healthy controls (HC). These datasets were pre-processed and non-linearly registered to the MNI PD25 atlas. Six imaging maps describing the macro- and micro-structural integrity of brain tissues complemented with age and sex parameters were used to train a convolutional neural network (CNN) to classify PD/HC subjects. Explainability of the model's decision-making was achieved using SmoothGrad saliency maps, highlighting important brain regions. The CNN was trained using a 75%/10%/15% train/validation/test split stratified by diagnosis, sex, age, and study, achieving a ROC-AUC of 0.89, accuracy of 80.8%, specificity of 82.4%, and sensitivity of 79.1% on the test set. Saliency maps revealed that diffusion tensor imaging data, especially fractional anisotropy, was more important for the classification than T1-weighted data, highlighting subcortical regions such as the brainstem, thalamus, amygdala, hippocampus, and cortical areas. The proposed model, trained on a large multimodal MRI database, can classify PD patients and HC subjects with high accuracy and clinically reasonable explanations, suggesting that micro-structural brain changes play an essential role in the disease course.
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Affiliation(s)
- Milton Camacho
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada.
- Department of Radiology, University of Calgary, Calgary, AB, Canada.
| | - Matthias Wilms
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
- Department of Pediatrics and Community Health Sciences, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Hannes Almgren
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Kimberly Amador
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Richard Camicioli
- Neuroscience and Mental Health Institute and Department of Medicine (Neurology), University of Alberta, Edmonton, AB, Canada
| | - Zahinoor Ismail
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada
- College of Medicine and Health, University of Exeter, Exeter, UK
| | - Oury Monchi
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Department of Radiology, Radio-oncology and Nuclear Medicine, Université de Montréal, Montréal, QC, Canada
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montréal, QC, Canada
| | - Nils D Forkert
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
- Department of Pediatrics and Community Health Sciences, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
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Gore S, Dhole A, Kumbhar S, Jagtap J. Radiomics for Parkinson's disease classification using advanced texture-based biomarkers. MethodsX 2023; 11:102359. [PMID: 37791007 PMCID: PMC10543659 DOI: 10.1016/j.mex.2023.102359] [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: 08/21/2023] [Accepted: 08/30/2023] [Indexed: 10/05/2023] Open
Abstract
Parkinson's disease (PD) is one of the neurodegenerative diseases and its manual diagnosis leads to time-consuming process. MRI-based computer-aided diagnosis helps medical experts to diagnose PD more precisely and fast. Texture-based radiomic analysis is carried out on 3D MRI scans of T1 weighted and resting-state modalities. 43 subjects from Neurocon and 40 subjects from Tao-Wu dataset were examined, which consisted of 36 scans of healthy controls and 47 scans of Parkinson's patients. Total 360 2D MRI images are selected among around 17000 slices of T1-weighted and resting scans of selected 72 subjects. Local binary pattern (LBP) method was applied with custom variants to acquire advanced textural biomarkers from MRI images. LBP histogram helped to learn discriminative local patterns to detect and classify Parkinson's disease. Using recursive feature elimination, data dimensions of around 150-300 LBP histogram features were reduced to 13-21 most significant features based on score, and important features were analysed using SVM and random forest algorithms. Variant-I of LBP has performed well with highest test accuracy of 83.33%, precision of 84.62%, recall of 91.67%, and f1-score of 88%. Classification accuracies were obtained from 61.11% to 83.33% and AUC-ROC values range from 0.43 to 0.86 using four variants of LBP.•Parkinson's classification is carried out using an advanced biomedical texture feature. Texture extraction using four variants of uniform, rotation invariant LBP method is performed for radiomic analysis of Parkinson's disorder.•Proposed method with support vector machine classifier is experimented and an accuracy of 83.33% is achieved with 10-fold cross validation for detection of Parkinson's patients from MRI-based radiomic analysis.•The proposed predictive model has proved the potential of textures of extended version of LBP, which have demonstrated subtle variations in local appearance for Parkinson's detection.
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Affiliation(s)
- Sonal Gore
- Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra, India
| | - Aniket Dhole
- Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra, India
| | - Shrishail Kumbhar
- Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra, India
| | - Jayant Jagtap
- Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University), (SIU), Lavale, Pune, Maharashtra, India
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Mallik S, Majhi B, Kashyap A, Mohanty S, Dash S, Li A, Zhao Z. An Improved Method for Diagnosis of Parkinson's Disease using Deep Learning Models Enhanced with Metaheuristic Algorithm. RESEARCH SQUARE 2023:rs.3.rs-3387953. [PMID: 37886464 PMCID: PMC10602096 DOI: 10.21203/rs.3.rs-3387953/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Accurate diagnosis of Parkinson's disease (PD) at an early stage is challenging for clinicians as its progression is very slow. Currently many machine learning and deep learning approaches are used for detection of PD and they are popular too. This study proposes four deep learning models and a hybrid model for the early detection of PD. Further to improve the performance of the models, grey wolf optimization (GWO) is used to automatically fine-tune the hyperparameters of the models. The simulation study is carried out using two standard datasets, T1,T2-weighted and SPECT DaTscan. The metaherustic enhanced deep learning models used are GWO-VGG16, GWO-DenseNet, GWO-DenseNet + LSTM, GWO-InceptionV3 and GWO-VGG16 + InceptionV3. Simulation results demonstrated that all the models perform well and obtained near above 99% of accuracy. The AUC-ROC score of 99.99 is achieved by the GWO-VGG16 + InceptionV3 and GWO-DenseNet models for T1, T2-weighted dataset. Similarly, the GWO-DenseNet, GWO-InceptionV3 and GWO-VGG16 + InceptionV3 models result an AUC-ROC score of 100 for SPECT DaTscan dataset.
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Affiliation(s)
| | | | | | | | | | - Aimin Li
- Xi'an University of Science and Technology
| | - Zhongming Zhao
- The University of Texas Health Science Center at Houston
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Göker H. Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model. Phys Eng Sci Med 2023; 46:1163-1174. [PMID: 37245195 DOI: 10.1007/s13246-023-01284-x] [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] [Received: 07/26/2022] [Accepted: 05/18/2023] [Indexed: 05/29/2023]
Abstract
Parkinson's disease (PD) is characterized by slowed movements, speech disorders, an inability to control muscle movements, and tremors in the hands and feet. In the early stages of PD, the changes in these motor signs are very vague, so an objective and accurate diagnosis is difficult. The disease is complex, progressive, and very common. There are more than 10 million people worldwide suffering from PD. In this study, an EEG-based deep learning model was proposed for the automatic detection of PD to support experts. The EEG dataset comprises signals recorded by the University of Iowa from 14 PD patients and 14 healthy controls. First of all, the power spectral density values (PSDs) of the frequencies between 1 and 49 Hz of the EEG signals were calculated separately using periodogram, welch, and multitaper spectral analysis methods. 49 feature vectors were extracted for each of the three different experiments. Then, the performances of support vector machine, random forest, k-nearest neighbor, and bidirectional long-short-term memory (BiLSTM) algorithms were compared using the PSDs feature vectors. After the comparison, the model integrating welch spectral analysis and the BiLSTM algorithm showed the highest performance as a result of the experiments. The deep learning model achieved satisfactory performance with 0.965 specificity, 0.994 sensitivity, 0.964 precision, 0.978 f1-score, 0.958 Matthews correlation coefficient, and 97.92% accuracy. The study is a promising attempt to detect PD from EEG signals and it also provides evidence that deep learning algorithms are more effective than machine learning algorithms for EEG signal analysis.
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Affiliation(s)
- Hanife Göker
- Health Services Vocational College, Gazi University, 06830, Ankara, Turkey.
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7
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Luo Y, Chen H, Gui M. Radiomics and Hybrid Models Based on Machine Learning to Predict Levodopa-Induced Dyskinesia of Parkinson's Disease in the First 6 Years of Levodopa Treatment. Diagnostics (Basel) 2023; 13:2511. [PMID: 37568874 PMCID: PMC10417024 DOI: 10.3390/diagnostics13152511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/23/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Current research on the prediction of movement complications associated with levodopa therapy in Parkinson's disease (PD) is limited. levodopa-induced dyskinesia (LID) is a movement complication that seriously affects the life quality of PD patients. One-third of PD patients develop LID within 1 to 6 years of levodopa treatment. This study aimed to construct models based on radiomics and machine learning to predict early LID in PD. METHODS We extracted radiomics features from the T1-weighted MRI obtained in the baseline of 49 PD control and 54 PD with LID in the first 6 years of levodopa therapy. Six brain regions related to the onset of PD were segmented as regions of interest (ROIs). The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Using the machine learning methods of support vector machine (SVM), random forest (RF), and AdaBoost, we constructed radiomics models and hybrid models. The hybrid models combined the radiomics features and the Unified Parkinson's Disease Rating Scale part III (UPDRS III) total score. The five-fold cross-validation was performed and repeated 20 times to validate the stability of the classifiers. We used sensitivity, specificity, accuracy, receiver operating characteristic (ROC) curves, and area under the ROC curve (AUC) for model validation. RESULTS We selected 33 out of 6138 radiomics features. In the testing set of the radiomics model, the AUC values of the SVM, RF, and AdaBoost classifiers were 0.905, 0.808, and 0.778, respectively, and the accuracies were 0.839, 0.742, and 0.710. The hybrid models had better prediction performance. In the testing set, the AUC values of SVM, RF, and AdaBoost classifiers were 0.958, 0.861, and 0.832, respectively, and the accuracies were 0.903, 0.806, and 0.774. CONCLUSIONS Our results indicate that T1-weighted MRI is valuable in predicting early LID in PD. This work demonstrates that the combination of radiomics features and clinical features has good potential and value for identifying early LID in PD.
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Affiliation(s)
- Yang Luo
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410083, China;
| | - Huiqin Chen
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410083, China;
| | - Mingzhen Gui
- School of Automation, Central South University, Changsha 410083, China
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Garcia Santa Cruz B, Husch A, Hertel F. Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions. Front Aging Neurosci 2023; 15:1216163. [PMID: 37539346 PMCID: PMC10394631 DOI: 10.3389/fnagi.2023.1216163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/28/2023] [Indexed: 08/05/2023] Open
Abstract
Parkinson's disease (PD) is a progressive and complex neurodegenerative disorder associated with age that affects motor and cognitive functions. As there is currently no cure, early diagnosis and accurate prognosis are essential to increase the effectiveness of treatment and control its symptoms. Medical imaging, specifically magnetic resonance imaging (MRI), has emerged as a valuable tool for developing support systems to assist in diagnosis and prognosis. The current literature aims to improve understanding of the disease's structural and functional manifestations in the brain. By applying artificial intelligence to neuroimaging, such as deep learning (DL) and other machine learning (ML) techniques, previously unknown relationships and patterns can be revealed in this high-dimensional data. However, several issues must be addressed before these solutions can be safely integrated into clinical practice. This review provides a comprehensive overview of recent ML techniques analyzed for the automatic diagnosis and prognosis of PD in brain MRI. The main challenges in applying ML to medical diagnosis and its implications for PD are also addressed, including current limitations for safe translation into hospitals. These challenges are analyzed at three levels: disease-specific, task-specific, and technology-specific. Finally, potential future directions for each challenge and future perspectives are discussed.
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Affiliation(s)
| | - Andreas Husch
- Imaging AI Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Frank Hertel
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg
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Cui X, Chen N, Zhao C, Li J, Zheng X, Liu C, Yang J, Li X, Yu C, Liu J, Liu X. An Adaptive Weighted Attention-Enhanced Deep Convolutional Neural Network for Classification of MRI images of Parkinson's Disease. J Neurosci Methods 2023:109884. [PMID: 37207799 DOI: 10.1016/j.jneumeth.2023.109884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 05/05/2023] [Accepted: 05/16/2023] [Indexed: 05/21/2023]
Abstract
BACKGROUND Parkinson's disease (PD) is the second prevalent neurological diseases with a significant growth rate in incidence. Convolutional neural networks using structural magnetic resonance images (sMRI) are widely used for PD classification. However, the areas of change in the patient's MRI images are small and unfixed. Thus, capturing the features of the areas accurately where the lesions changed became a problem. METHOD We propose a deep learning framework that combines multi-scale attention guidance and multi-branch feature processing modules to diagnose PD by learning sMRI T2 slice features. In this scheme, firstly, to achieve effective feature transfer and gradient descent, a deep convolutional neural network framework based on dense block is designed. Next, an Adaptive Weighted Attention algorithm is proposed, whose pursers is to extract multi branch and even diverse features. Finally, Dropout layer and SoftMax layer are added to the network structure to obtain good classification results and rich and diverse feature information. The Dropout layer is used to reduce the number of intermediate features to increase the orthogonality between features of each layer. The activation function SoftMax increases the flexibility of the neural network by increasing the degree of fitting to the training set and converting linear to nonlinear. RESULTS The best performance of the proposed method an accuracy of 92%, a sensitivity of 94%, specificity of 90% and a F1 score of 95% respectively for identifying PD and HC. CONCLUSION Experiments show that the proposed method can successfully distinguish PD and NC. Good classification results were obtained in PD diagnosis classification task and compared with advanced research methods.
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Affiliation(s)
- Xinchun Cui
- School of Computer Science, Qufu Normal University @ Rizhao, 276826, Rizhao, China; School of Foundational Education, University of Health and Rehabilitation Sciences, 266071, Qingdao, China; Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, 266011, Qingdao, China; Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology@Huajiang, 541004, Guilin, China
| | - Ningning Chen
- School of Computer Science, Qufu Normal University @ Rizhao, 276826, Rizhao, China
| | - Chao Zhao
- School of Computer Science, Qufu Normal University @ Rizhao, 276826, Rizhao, China; Department of Neurosurgery, the Affiliated Rizhao People's Hospital of Jining Medical University, 276800, Rizhao, Shandong, China
| | - Jianlong Li
- School of Computer Science, Qufu Normal University @ Rizhao, 276826, Rizhao, China; Department of Radiology, The Affiliated Rizhao People's Hospital of Jining Medical University, 276800, Rizhao, Shandong, China
| | - Xiangwei Zheng
- School of Information Science and Engineering, Shandong Normal University, 250358, Ji'nan, China
| | - Caixia Liu
- Nursing Department, Zhejiang Hospital,310013, Hangzhou, China.
| | - Jiahu Yang
- Department of Radiology, Zhejiang Hospital,310013, Hangzhou, China.
| | - Xiuli Li
- School of Foundational Education, University of Health and Rehabilitation Sciences, 266071, Qingdao, China
| | - Chao Yu
- School of Foundational Education, University of Health and Rehabilitation Sciences, 266071, Qingdao, China
| | - Jinxing Liu
- School of Computer Science, Qufu Normal University @ Rizhao, 276826, Rizhao, China
| | - Xiaoli Liu
- Department of Neurology, Zhejiang Hospital,310013, Hangzhou, China.
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10
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Qian J, Li H, Wang J, He L. Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging. Diagnostics (Basel) 2023; 13:1571. [PMID: 37174962 PMCID: PMC10178221 DOI: 10.3390/diagnostics13091571] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/29/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image diagnoses and prognoses. However, most of the DL models are considered as "black boxes". There is an unmet need to demystify DL models so domain experts can trust these high-performance DL models. This has resulted in a sub-domain of AI research called explainable artificial intelligence (XAI). In the last decade, many experts have dedicated their efforts to developing novel XAI methods that are competent at visualizing and explaining the logic behind data-driven DL models. However, XAI techniques are still in their infancy for medical MRI image analysis. This study aims to outline the XAI applications that are able to interpret DL models for MRI data analysis. We first introduce several common MRI data modalities. Then, a brief history of DL models is discussed. Next, we highlight XAI frameworks and elaborate on the principles of multiple popular XAI methods. Moreover, studies on XAI applications in MRI image analysis are reviewed across the tissues/organs of the human body. A quantitative analysis is conducted to reveal the insights of MRI researchers on these XAI techniques. Finally, evaluations of XAI methods are discussed. This survey presents recent advances in the XAI domain for explaining the DL models that have been utilized in MRI applications.
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Affiliation(s)
- Jinzhao Qian
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Computer Science, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Radiology, College of Medicine, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Junqi Wang
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Computer Science, University of Cincinnati, Cincinnati, OH 45221, USA
- Department of Radiology, College of Medicine, University of Cincinnati, Cincinnati, OH 45221, USA
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11
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Camacho M, Wilms M, Mouches P, Almgren H, Souza R, Camicioli R, Ismail Z, Monchi O, Forkert ND. Explainable classification of Parkinson's disease using deep learning trained on a large multi-center database of T1-weighted MRI datasets. Neuroimage Clin 2023; 38:103405. [PMID: 37079936 PMCID: PMC10148079 DOI: 10.1016/j.nicl.2023.103405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 02/13/2023] [Accepted: 04/11/2023] [Indexed: 04/22/2023]
Abstract
INTRODUCTION Parkinson's disease (PD) is a severe neurodegenerative disease that affects millions of people. Early diagnosis is important to facilitate prompt interventions to slow down disease progression. However, accurate PD diagnosis can be challenging, especially in the early disease stages. The aim of this work was to develop and evaluate a robust explainable deep learning model for PD classification trained from one of the largest collections of T1-weighted magnetic resonance imaging datasets. MATERIALS AND METHODS A total of 2,041 T1-weighted MRI datasets from 13 different studies were collected, including 1,024 datasets from PD patients and 1,017 datasets from age- and sex-matched healthy controls (HC). The datasets were skull stripped, resampled to isotropic resolution, bias field corrected, and non-linearly registered to the MNI PD25 atlas. The Jacobian maps derived from the deformation fields together with basic clinical parameters were used to train a state-of-the-art convolutional neural network (CNN) to classify PD and HC subjects. Saliency maps were generated to display the brain regions contributing the most to the classification task as a means of explainable artificial intelligence. RESULTS The CNN model was trained using an 85%/5%/10% train/validation/test split stratified by diagnosis, sex, and study. The model achieved an accuracy of 79.3%, precision of 80.2%, specificity of 81.3%, sensitivity of 77.7%, and AUC-ROC of 0.87 on the test set while performing similarly on an independent test set. Saliency maps computed for the test set data highlighted frontotemporal regions, the orbital-frontal cortex, and multiple deep gray matter structures as most important. CONCLUSION The developed CNN model, trained on a large heterogenous database, was able to differentiate PD patients from HC subjects with high accuracy with clinically feasible classification explanations. Future research should aim to investigate the combination of multiple imaging modalities with deep learning and on validating these results in a prospective trial as a clinical decision support system.
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Affiliation(s)
- Milton Camacho
- Biomedical Engineering Program, University of Calgary, Canada; Department of Radiology, University of Calgary, Canada.
| | - Matthias Wilms
- Department of Radiology, University of Calgary, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Canada
| | - Pauline Mouches
- Biomedical Engineering Program, University of Calgary, Canada; Department of Radiology, University of Calgary, Canada
| | - Hannes Almgren
- Department of Clinical Neurosciences, University of Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Canada
| | - Raissa Souza
- Biomedical Engineering Program, University of Calgary, Canada; Department of Radiology, University of Calgary, Canada
| | - Richard Camicioli
- Neuroscience and Mental Health Institute and Department of Medicine (Neurology), University of Alberta, Edmonton, Alberta, Canada
| | - Zahinoor Ismail
- Department of Clinical Neurosciences, University of Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Canada; Department of Psychiatry, University of Calgary, Canada
| | - Oury Monchi
- Department of Clinical Neurosciences, University of Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Canada; Department of Radiology, Radio-oncology and Nuclear Medicine, Université de Montréal, Quebec, Canada; Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Québec, Canada
| | - Nils D Forkert
- Department of Radiology, University of Calgary, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Canada; Department of Clinical Neurosciences, University of Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Canada; Department of Electrical and Software Engineering, University of Calgary, Canada
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Nazir S, Dickson DM, Akram MU. Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks. Comput Biol Med 2023; 156:106668. [PMID: 36863192 DOI: 10.1016/j.compbiomed.2023.106668] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 01/12/2023] [Accepted: 02/10/2023] [Indexed: 02/21/2023]
Abstract
Artificial Intelligence (AI) techniques of deep learning have revolutionized the disease diagnosis with their outstanding image classification performance. In spite of the outstanding results, the widespread adoption of these techniques in clinical practice is still taking place at a moderate pace. One of the major hindrance is that a trained Deep Neural Networks (DNN) model provides a prediction, but questions about why and how that prediction was made remain unanswered. This linkage is of utmost importance for the regulated healthcare domain to increase the trust in the automated diagnosis system by the practitioners, patients and other stakeholders. The application of deep learning for medical imaging has to be interpreted with caution due to the health and safety concerns similar to blame attribution in the case of an accident involving autonomous cars. The consequences of both a false positive and false negative cases are far reaching for patients' welfare and cannot be ignored. This is exacerbated by the fact that the state-of-the-art deep learning algorithms comprise of complex interconnected structures, millions of parameters, and a 'black box' nature, offering little understanding of their inner working unlike the traditional machine learning algorithms. Explainable AI (XAI) techniques help to understand model predictions which help develop trust in the system, accelerate the disease diagnosis, and meet adherence to regulatory requirements. This survey provides a comprehensive review of the promising field of XAI for biomedical imaging diagnostics. We also provide a categorization of the XAI techniques, discuss the open challenges, and provide future directions for XAI which would be of interest to clinicians, regulators and model developers.
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Affiliation(s)
- Sajid Nazir
- Department of Computing, Glasgow Caledonian University, Glasgow, UK.
| | - Diane M Dickson
- Department of Podiatry and Radiography, Research Centre for Health, Glasgow Caledonian University, Glasgow, UK
| | - Muhammad Usman Akram
- Computer and Software Engineering Department, National University of Sciences and Technology, Islamabad, Pakistan
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Yoon S, Kim M, Lee WW. Long Short-Term Memory-Based Deep Learning Models for Screening Parkinson's Disease Using Sequential Diagnostic Codes. J Clin Neurol 2023; 19:270-279. [PMID: 36647230 PMCID: PMC10169913 DOI: 10.3988/jcn.2022.0160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 10/24/2022] [Accepted: 10/26/2022] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND AND PURPOSE It is challenging to detect Parkinson's disease (PD) in its early stages, which has prompted researchers to develop techniques based on machine learning methods for detecting PD. However, previous studies did not fully incorporate the slow progression of PD over a long period of time nor consider that its symptoms occur in a time-sequential manner. Contributing to the literature on PD, which has relied heavily on cross-sectional data, this study aimed to develop a method for detecting PD early that can process time-series information using the long short-term memory (LSTM) algorithm. METHODS We sampled 926 patients with PD and 9,260 subjects without PD using medical-claims data. The LSTM algorithm was tested using diagnostic histories, which contained the diagnostic codes and their respective time information. We compared the prediction power of the 12-month diagnostic codes under two different settings over the 4 years prior to the first PD diagnosis. RESULTS The model that was trained using the most-recent 12-month diagnostic codes had the best performance, with an accuracy of 94.25%, a sensitivity of 82.91%, and a specificity of 95.26%. The other three models (12-month codes from 2, 3, and 4 years prior) were found to have comparable performances, with accuracies of 92.27%, 91.86%, and 91.81%, respectively. The areas under the curve from our data settings ranged from 0.839 to 0.923. CONCLUSIONS We explored the possibility that PD specialists could benefit from our proposed machine learning method as an early detection method for PD.
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Affiliation(s)
- Seokjoon Yoon
- College of Business, Korea Advanced Institute of Science and Technology, Seoul, Korea
| | - Minki Kim
- College of Business, Korea Advanced Institute of Science and Technology, Seoul, Korea
| | - Woong-Woo Lee
- Department of Neurology, Nowon Eulji Medical Center, Eulji University, Seoul, Korea.,Department of Neurology, Eulji University College of Medicine, Daejeon, Korea.
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14
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Kabir T, Lee CT, Chen L, Jiang X, Shams S. A comprehensive artificial intelligence framework for dental diagnosis and charting. BMC Oral Health 2022; 22:480. [PMID: 36352390 PMCID: PMC9647924 DOI: 10.1186/s12903-022-02514-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/18/2022] [Indexed: 11/11/2022] Open
Abstract
Background The aim of this study was to develop artificial intelligence (AI) guided framework to recognize tooth numbers in panoramic and intraoral radiographs (periapical and bitewing) without prior domain knowledge and arrange the intraoral radiographs into a full mouth series (FMS) arrangement template. This model can be integrated with different diseases diagnosis models, such as periodontitis or caries, to facilitate clinical examinations and diagnoses. Methods The framework utilized image segmentation models to generate the masks of bone area, tooth, and cementoenamel junction (CEJ) lines from intraoral radiographs. These masks were used to detect and extract teeth bounding boxes utilizing several image analysis methods. Then, individual teeth were matched with a patient’s panoramic images (if available) or tooth repositories for assigning tooth numbers using the multi-scale matching strategy. This framework was tested on 1240 intraoral radiographs different from the training and internal validation cohort to avoid data snooping. Besides, a web interface was designed to generate a report for different dental abnormalities with tooth numbers to evaluate this framework’s practicality in clinical settings. Results The proposed method achieved the following precision and recall via panoramic view: 0.96 and 0.96 (via panoramic view) and 0.87 and 0.87 (via repository match) by handling tooth shape variation and outperforming other state-of-the-art methods. Additionally, the proposed framework could accurately arrange a set of intraoral radiographs into an FMS arrangement template based on positions and tooth numbers with an accuracy of 95% for periapical images and 90% for bitewing images. The accuracy of this framework was also 94% in the images with missing teeth and 89% with restorations. Conclusions The proposed tooth numbering model is robust and self-contained and can also be integrated with other dental diagnosis modules, such as alveolar bone assessment and caries detection. This artificial intelligence-based tooth detection and tooth number assignment in dental radiographs will help dentists with enhanced communication, documentation, and treatment planning accurately. In addition, the proposed framework can correctly specify detailed diagnostic information associated with a single tooth without human intervention. Supplementary Information The online version contains supplementary material available at 10.1186/s12903-022-02514-6.
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15
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Thakur M, Kuresan H, Dhanalakshmi S, Lai KW, Wu X. Soft Attention Based DenseNet Model for Parkinson’s Disease Classification Using SPECT Images. Front Aging Neurosci 2022; 14:908143. [PMID: 35912076 PMCID: PMC9326232 DOI: 10.3389/fnagi.2022.908143] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 05/05/2022] [Indexed: 11/25/2022] Open
Abstract
Objective Deep learning algorithms have long been involved in the diagnosis of severe neurological disorders that interfere with patients’ everyday tasks, such as Parkinson’s disease (PD). The most effective imaging modality for detecting the condition is DaTscan, a variety of single-photon emission computerized tomography (SPECT) imaging method. The goal is to create a convolutional neural network that can specifically identify the region of interest following feature extraction. Methods The study comprised a total of 1,390 DaTscan imaging groups with PD and normal classes. The architecture of DenseNet-121 is leveraged with a soft-attention block added before the final classification layer. For visually analyzing the region of interest (ROI) from the images after classification, Soft Attention Maps and feature map representation are used. Outcomes The model obtains an overall accuracy of 99.2% and AUC-ROC score 99%. A sensitivity of 99.2%, specificity of 99.4% and f1-score of 99.1% is achieved that surpasses all prior research findings. Soft-attention map and feature map representation aid in highlighting the ROI, with a specific attention on the putamen and caudate regions. Conclusion With the deep learning framework adopted, DaTscan images reveal the putamen and caudate areas of the brain, which aid in the distinguishing of normal and PD cohorts with high accuracy and sensitivity.
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Affiliation(s)
- Mahima Thakur
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India
| | - Harisudha Kuresan
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India
| | - Samiappan Dhanalakshmi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India
- *Correspondence: Samiappan Dhanalakshmi,
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Khin Wee Lai,
| | - Xiang Wu
- School of Medical Information Engineering, Xuzhou Medical University, Xuzhou, China
- Xiang Wu,
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16
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van der Velden BH, Kuijf HJ, Gilhuijs KG, Viergever MA. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med Image Anal 2022; 79:102470. [DOI: 10.1016/j.media.2022.102470] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 03/15/2022] [Accepted: 05/02/2022] [Indexed: 12/11/2022]
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17
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Deep learning architectures for Parkinson's disease detection by using multi-modal features. Comput Biol Med 2022; 146:105610. [DOI: 10.1016/j.compbiomed.2022.105610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 05/06/2022] [Accepted: 05/09/2022] [Indexed: 11/15/2022]
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18
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Wang Q, Li L, Qiao L, Liu M. Adaptive Multimodal Neuroimage Integration for Major Depression Disorder Detection. Front Neuroinform 2022; 16:856175. [PMID: 35571867 PMCID: PMC9100686 DOI: 10.3389/fninf.2022.856175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
Major depressive disorder (MDD) is one of the most common mental health disorders that can affect sleep, mood, appetite, and behavior of people. Multimodal neuroimaging data, such as functional and structural magnetic resonance imaging (MRI) scans, have been widely used in computer-aided detection of MDD. However, previous studies usually treat these two modalities separately, without considering their potentially complementary information. Even though a few studies propose integrating these two modalities, they usually suffer from significant inter-modality data heterogeneity. In this paper, we propose an adaptive multimodal neuroimage integration (AMNI) framework for automated MDD detection based on functional and structural MRIs. The AMNI framework consists of four major components: (1) a graph convolutional network to learn feature representations of functional connectivity networks derived from functional MRIs, (2) a convolutional neural network to learn features of T1-weighted structural MRIs, (3) a feature adaptation module to alleviate inter-modality difference, and (4) a feature fusion module to integrate feature representations extracted from two modalities for classification. To the best of our knowledge, this is among the first attempts to adaptively integrate functional and structural MRIs for neuroimaging-based MDD analysis by explicitly alleviating inter-modality heterogeneity. Extensive evaluations are performed on 533 subjects with resting-state functional MRI and T1-weighted MRI, with results suggesting the efficacy of the proposed method.
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Affiliation(s)
- Qianqian Wang
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Long Li
- Taian Tumor Prevention and Treatment Hospital, Taian, China
| | - Lishan Qiao
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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Neurodegenerative diseases-Caps: a capsule network based early screening system for the classification of neurodegenerative diseases. Cogn Neurodyn 2022; 16:1361-1377. [DOI: 10.1007/s11571-022-09787-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 12/16/2021] [Accepted: 01/21/2022] [Indexed: 11/03/2022] Open
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20
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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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Deep Learning-Based Prediction of Paresthesia after Third Molar Extraction: A Preliminary Study. Diagnostics (Basel) 2021; 11:diagnostics11091572. [PMID: 34573914 PMCID: PMC8469771 DOI: 10.3390/diagnostics11091572] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 08/25/2021] [Accepted: 08/28/2021] [Indexed: 01/04/2023] Open
Abstract
The purpose of this study was to determine whether convolutional neural networks (CNNs) can predict paresthesia of the inferior alveolar nerve using panoramic radiographic images before extraction of the mandibular third molar. The dataset consisted of a total of 300 preoperative panoramic radiographic images of patients who had planned mandibular third molar extraction. A total of 100 images taken of patients who had paresthesia after tooth extraction were classified as Group 1, and 200 images taken of patients without paresthesia were classified as Group 2. The dataset was randomly divided into a training and validation set (n = 150 [50%]), and a test set (n = 150 [50%]). CNNs of SSD300 and ResNet-18 were used for deep learning. The average accuracy, sensitivity, specificity, and area under the curve were 0.827, 0.84, 0.82, and 0.917, respectively. This study revealed that CNNs can assist in the prediction of paresthesia of the inferior alveolar nerve after third molar extraction using panoramic radiographic images.
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Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [ 18F]FP-CIT Positron Emission Tomography. Diagnostics (Basel) 2021; 11:diagnostics11091557. [PMID: 34573899 PMCID: PMC8467049 DOI: 10.3390/diagnostics11091557] [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: 06/21/2021] [Revised: 08/14/2021] [Accepted: 08/25/2021] [Indexed: 11/17/2022] Open
Abstract
The aim of this study was to compare the performance of a deep-learning convolutional neural network (Faster R-CNN) model to detect imaging findings suggestive of idiopathic Parkinson's disease (PD) based on [18F]FP-CIT PET maximum intensity projection (MIP) images versus that of nuclear medicine (NM) physicians. The anteroposterior MIP images of the [18F]FP-CIT PET scan of 527 patients were classified as having PD (139 images) or non-PD (388 images) patterns according to the final diagnosis. Non-PD patterns were classified as overall-normal (ONL, 365 images) and vascular parkinsonism with definite defects or prominently decreased dopamine transporter binding (dVP, 23 images) patterns. Faster R-CNN was trained on 120 PD, 320 ONL, and 16 dVP pattern images and tested on the 19 PD, 45 ONL, and seven dVP patterns images. The performance of the Faster R-CNN and three NM physicians was assessed using receiver operating characteristics curve analysis. The difference in performance was assessed using Cochran's Q test, and the inter-rater reliability was calculated. Faster R-CNN showed high accuracy in differentiating PD from non-PD patterns and also from dVP patterns, with results comparable to those of NM physicians. There were no significant differences in the area under the curve and performance. The inter-rater reliability among Faster R-CNN and NM physicians showed substantial to almost perfect agreement. The deep-learning model accurately differentiated PD from non-PD patterns on MIP images of [18F]FP-CIT PET, and its performance was comparable to that of NM physicians.
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Lee KH, Choi ST, Lee GY, Ha YJ, Choi SI. Method for Diagnosing the Bone Marrow Edema of Sacroiliac Joint in Patients with Axial Spondyloarthritis Using Magnetic Resonance Image Analysis Based on Deep Learning. Diagnostics (Basel) 2021; 11:diagnostics11071156. [PMID: 34202607 PMCID: PMC8303557 DOI: 10.3390/diagnostics11071156] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 06/11/2021] [Accepted: 06/18/2021] [Indexed: 11/17/2022] Open
Abstract
Axial spondyloarthritis (axSpA) is a chronic inflammatory disease of the sacroiliac joints. In this study, we develop a method for detecting bone marrow edema by magnetic resonance (MR) imaging of the sacroiliac joints and a deep-learning network. A total of 815 MR images of the sacroiliac joints were obtained from 60 patients diagnosed with axSpA and 19 healthy subjects. Gadolinium-enhanced fat-suppressed T1-weighted oblique coronal images were used for deep learning. Active sacroiliitis was defined as bone marrow edema, and the following processes were performed: setting the region of interest (ROI) and normalizing it to a size suitable for input to a deep-learning network, determining bone marrow edema using a convolutional-neural-network-based deep-learning network for individual MR images, and determining sacroiliac arthritis in subject examinations based on the classification results of individual MR images. About 70% of the patients and normal subjects were randomly selected for the training dataset, and the remaining 30% formed the test dataset. This process was repeated five times to calculate the average classification rate of the five-fold sets. The gradient-weighted class activation mapping method was used to validate the classification results. In the performance analysis of the ResNet18-based classification network for individual MR images, use of the ROI showed excellent detection performance of bone marrow edema with 93.55 ± 2.19% accuracy, 92.87 ± 1.27% recall, and 94.69 ± 3.03% precision. The overall performance was additionally improved using a median filter to reflect the context information. Finally, active sacroiliitis was diagnosed in individual subjects with 96.06 ± 2.83% accuracy, 100% recall, and 94.84 ± 3.73% precision. This is a pilot study to diagnose bone marrow edema by deep learning based on MR images, and the results suggest that MR analysis using deep learning can be a useful complementary means for clinicians to diagnose bone marrow edema.
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Affiliation(s)
- Kang Hee Lee
- Department of Computer Science and Engineering, Dankook University, Yongin-si 16890, Korea;
| | - Sang Tae Choi
- Division of Rheumatology, Department of Internal Medicine, Chung-Ang University College of Medicine, Seoul 06973, Korea;
| | - Guen Young Lee
- Department of Radiology, Chung-Ang University College of Medicine, Seoul 06973, Korea;
| | - You Jung Ha
- Division of Rheumatology, Department of Internal Medicine, Seoul National University Bundang Hospital, Yongin-si 13620, Korea;
| | - Sang-Il Choi
- Department of Computer Engineering, Dankook University, Yongin-si 16890, Korea
- Correspondence: ; Tel.: +82-31-8005-3657
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Alzubaidi MS, Shah U, Dhia Zubaydi H, Dolaat K, Abd-Alrazaq AA, Ahmed A, Househ M. The Role of Neural Network for the Detection of Parkinson's Disease: A Scoping Review. Healthcare (Basel) 2021; 9:healthcare9060740. [PMID: 34208654 PMCID: PMC8235532 DOI: 10.3390/healthcare9060740] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 05/26/2021] [Accepted: 05/26/2021] [Indexed: 11/16/2022] Open
Abstract
Background: Parkinson’s Disease (PD) is a chronic neurodegenerative disorder that has been ranked second after Alzheimer’s disease worldwide. Early diagnosis of PD is crucial to combat against PD to allow patients to deal with it properly. However, there is no medical test(s) available to diagnose PD conclusively. Therefore, computer-aided diagnosis (CAD) systems offered a better solution to make the necessary data-driven decisions and assist the physician. Numerous studies were conducted to propose CAD to diagnose PD in the early stages. No comprehensive reviews have been conducted to summarize the role of AI tools to combat PD. Objective: The study aimed to explore and summarize the applications of neural networks to diagnose PD. Methods: PRISMA Extension for Scoping Reviews (PRISMA-ScR) was followed to conduct this scoping review. To identify the relevant studies, both medical databases (e.g., PubMed) and technical databases (IEEE) were searched. Three reviewers carried out the study selection and extracted the data from the included studies independently. Then, the narrative approach was adopted to synthesis the extracted data. Results: Out of 1061 studies, 91 studies satisfied the eligibility criteria in this review. About half of the included studies have implemented artificial neural networks to diagnose PD. Numerous studies included focused on the freezing of gait (FoG). Biomedical voice and signal datasets were the most commonly used data types to develop and validate these models. However, MRI- and CT-scan images were also utilized in the included studies. Conclusion: Neural networks play an integral and substantial role in combating PD. Many possible applications of neural networks were identified in this review, however, most of them are limited up to research purposes.
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Affiliation(s)
- Mahmood Saleh Alzubaidi
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 53, Qatar; (U.S.); (K.D.); (A.A.A.-A.); (A.A.)
- Correspondence: (M.S.A.); (M.H.)
| | - Uzair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 53, Qatar; (U.S.); (K.D.); (A.A.A.-A.); (A.A.)
| | - Haider Dhia Zubaydi
- National Advanced IPv6 Centre, Universiti Sains Malaysia, Gelugor 11800, Malaysia;
| | - Khalid Dolaat
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 53, Qatar; (U.S.); (K.D.); (A.A.A.-A.); (A.A.)
| | - Alaa A. Abd-Alrazaq
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 53, Qatar; (U.S.); (K.D.); (A.A.A.-A.); (A.A.)
| | - Arfan Ahmed
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 53, Qatar; (U.S.); (K.D.); (A.A.A.-A.); (A.A.)
| | - Mowafa Househ
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 53, Qatar; (U.S.); (K.D.); (A.A.A.-A.); (A.A.)
- Correspondence: (M.S.A.); (M.H.)
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Termine A, Fabrizio C, Strafella C, Caputo V, Petrosini L, Caltagirone C, Giardina E, Cascella R. Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence. J Pers Med 2021; 11:280. [PMID: 33917161 PMCID: PMC8067806 DOI: 10.3390/jpm11040280] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/05/2021] [Accepted: 04/06/2021] [Indexed: 12/13/2022] Open
Abstract
In the big data era, artificial intelligence techniques have been applied to tackle traditional issues in the study of neurodegenerative diseases. Despite the progress made in understanding the complex (epi)genetics signatures underlying neurodegenerative disorders, performing early diagnosis and developing drug repurposing strategies remain serious challenges for such conditions. In this context, the integration of multi-omics, neuroimaging, and electronic health records data can be exploited using deep learning methods to provide the most accurate representation of patients possible. Deep learning allows researchers to find multi-modal biomarkers to develop more effective and personalized treatments, early diagnosis tools, as well as useful information for drug discovering and repurposing in neurodegenerative pathologies. In this review, we will describe how relevant studies have been able to demonstrate the potential of deep learning to enhance the knowledge of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases through the integration of all sources of biomedical data.
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Affiliation(s)
- Andrea Termine
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
| | - Carlo Fabrizio
- IRCCS Santa Lucia Foundation, Laboratory of Experimental and Behavioral Neurophysiology, 00143 Rome, Italy; (C.F.); (L.P.)
| | - Claudia Strafella
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
- Department of Biomedicine and Prevention, Tor Vergata University of Rome, 00133 Rome, Italy
| | - Valerio Caputo
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
- Department of Biomedicine and Prevention, Tor Vergata University of Rome, 00133 Rome, Italy
| | - Laura Petrosini
- IRCCS Santa Lucia Foundation, Laboratory of Experimental and Behavioral Neurophysiology, 00143 Rome, Italy; (C.F.); (L.P.)
| | - Carlo Caltagirone
- IRCCS Santa Lucia Foundation, Department of Clinical and Behavioral Neurology, 00179 Rome, Italy;
| | - Emiliano Giardina
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
- UILDM Lazio ONLUS Foundation, Department of Biomedicine and Prevention, Tor Vergata University, 00133 Rome, Italy
| | - Raffaella Cascella
- IRCCS Santa Lucia Foundation, Genomic Medicine Laboratory UILDM, 00179 Rome, Italy; (A.T.); (C.S.); (V.C.); (R.C.)
- Department of Biomedical Sciences, Catholic University Our Lady of Good Counsel, 1000 Tirana, Albania
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