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Jafari D, Simmatis L, Guarin D, Bouvier L, Taati B, Yunusova Y. 3D Video Tracking Technology in the Assessment of Orofacial Impairments in Neurological Disease: Clinical Validation. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2023; 66:3151-3165. [PMID: 36989177 PMCID: PMC10555456 DOI: 10.1044/2023_jslhr-22-00321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 09/09/2022] [Accepted: 01/10/2023] [Indexed: 06/19/2023]
Abstract
PURPOSE This study sought to determine whether clinically interpretable kinematic features extracted automatically from three-dimensional (3D) videos were correlated with corresponding perceptual clinical orofacial ratings in individuals with orofacial impairments due to neurological disorders. METHOD 45 participants (19 diagnosed with motor neuron diseases [MNDs] and 26 poststroke) performed two nonspeech tasks (mouth opening and lip spreading) and one speech task (repetition of a sentence "Buy Bobby a Puppy") while being video-recorded in a standardized lab setting. The color video recordings of participants were assessed by an expert clinician-a speech language pathologist-on the severity of three orofacial measures: symmetry, range of motion (ROM), and speed. Clinically interpretable 3D kinematic features, linked to symmetry, ROM, and speed, were automatically extracted from video recordings, using a deep facial landmark detection and tracking algorithm for each of the three tasks. Spearman correlations were used to identify features that were significantly correlated (p value < .05) with their corresponding clinical scores. Clinically significant kinematic features were then used in the subsequent multivariate regression models to predict the overall orofacial impairment severity score. RESULTS Several kinematic features extracted from 3D video recordings were associated with their corresponding perceptual clinical scores, indicating clinical validity of these automatically derived measures. Different patterns of significant features were observed between MND and poststroke groups; these differences were aligned with clinical expectations in both cases. CONCLUSIONS The results show that kinematic features extracted automatically from simple clinical tasks can capture characteristics used by clinicians during assessments. These findings support the clinical validity of video-based automatic extraction of kinematic features.
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Affiliation(s)
- Deniz Jafari
- Department of Speech-Language Pathology, Rehabilitation Sciences Institute, University of Toronto, Ontario, Canada
- KITE, Toronto Rehabilitation Institute, University Health Network, Ontario, Canada
| | - Leif Simmatis
- Department of Speech-Language Pathology, Rehabilitation Sciences Institute, University of Toronto, Ontario, Canada
- KITE, Toronto Rehabilitation Institute, University Health Network, Ontario, Canada
| | | | - Liziane Bouvier
- Department of Speech-Language Pathology, Rehabilitation Sciences Institute, University of Toronto, Ontario, Canada
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Babak Taati
- KITE, Toronto Rehabilitation Institute, University Health Network, Ontario, Canada
| | - Yana Yunusova
- Department of Speech-Language Pathology, Rehabilitation Sciences Institute, University of Toronto, Ontario, Canada
- KITE, Toronto Rehabilitation Institute, University Health Network, Ontario, Canada
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
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Wen P, Zhang Y, Wen G. Intelligent personalized diagnosis modeling in advanced medical system for Parkinson's disease using voice signals. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:8085-8102. [PMID: 37161187 DOI: 10.3934/mbe.2023351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Currently, machine learning methods have been utilized to realize the early detection of Parkinson's disease (PD) by using voice signals. Because the vocal system of each person is unique, and the same person's pronunciation can be different at different times, the training samples used in machine learning become very different from the speech signal of the patient to be diagnosed, frequently resulting in poor diagnostic performance. On this account, this paper presents a new intelligent personalized diagnosis method (PDM) for Parkinson's disease. The method was designed to begin with constructing new training data by assigning the best classifier to each training sample composed of features from the speech signals of patients. Subsequently, a meta-classifier was trained on the new training data. Finally, for the signal of each test patient, the method used the meta-classifier to select the most appropriate classifier, followed by adopting the selected classifier to classify the signal so that the more accurate diagnosis result of the test patient can be obtained. The novelty of the proposed method is that the proposed method uses different classifiers to perform the diagnosis of PD for diversified patients, whereas the current method uses the same classifier to diagnose all patients to be tested. Results of a large number of experiments show that PDM not only improves the performance but also exceeds the existing methods in speed.
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Affiliation(s)
- Pengcheng Wen
- College of Intelligent Systems Science and Engineering, Hubei University for Nationalities, Enshi 445000, China
| | - Yuhan Zhang
- Southern Medical University, Affiliated Dongguan Songshan Lake Central Hospital, Dongguan 523000, China
| | - Guihua Wen
- School of Computer Science & Engineering, South China University of Technology, Guangzhou 510000, China
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Predictive modelling of Parkinson's disease progression based on RNA-Sequence with densely connected deep recurrent neural networks. Sci Rep 2022; 12:21469. [PMID: 36509776 PMCID: PMC9744878 DOI: 10.1038/s41598-022-25454-1] [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: 09/01/2022] [Accepted: 11/30/2022] [Indexed: 12/14/2022] Open
Abstract
The advent of recent high throughput sequencing technologies resulted in unexplored big data of genomics and transcriptomics that might help to answer various research questions in Parkinson's disease (PD) progression. While the literature has revealed various predictive models that use longitudinal clinical data for disease progression, there is no predictive model based on RNA-Sequence data of PD patients. This study investigates how to predict the PD Progression for a patient's next medical visit by capturing longitudinal temporal patterns in the RNA-Seq data. Data provided by Parkinson Progression Marker Initiative (PPMI) includes 423 PD patients without revealing any race, sex, or age information with a variable number of visits and 34,682 predictor variables for 4 years. We propose a predictive model based on deep Recurrent Neural Network (RNN) with the addition of dense connections and batch normalization into RNN layers. The results show that the proposed architecture can predict PD progression from high dimensional RNA-seq data with a Root Mean Square Error (RMSE) of 6.0 and a rank-order correlation of (r = 0.83, p < 0.0001) between the predicted and actual disease status of PD.
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Marques G, Ferreras A, de la Torre-Diez I. An ensemble-based approach for automated medical diagnosis of malaria using EfficientNet. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:28061-28078. [PMID: 35368860 PMCID: PMC8964254 DOI: 10.1007/s11042-022-12624-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 07/03/2021] [Accepted: 02/09/2022] [Indexed: 05/05/2023]
Abstract
UNLABELLED Each year, more than 400,000 people die of malaria. Malaria is a mosquito-borne transmissible infection that affects humans and other animals. According to World Health Organization (WHO), 1.5 billion malaria cases and 7.6 million related deaths have been prevented from 2000 to 2019. Malaria is a disease that can be treated if early detected. We propose a support decision system for detecting malaria from microscopic peripheral blood cells images through convolutional neural networks (CNN). The proposed model is based on EfficientNetB0 architecture. The results are validated with 10-fold stratified cross-validation. This paper presents the classification findings using images from malaria patients and normal patients. The proposed approach is compared and outperforms the related work. Furthermore, the proposed ensemble method shows a recall value of 98.82%, a precision value of 97.74%, an F1-score of 98.28% and a ROC value of 99.76%. This work suggests that EfficientNet is a reliable architecture for automatic medical diagnostics of malaria. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s11042-022-12624-6.
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Affiliation(s)
- Gonçalo Marques
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
| | - Antonio Ferreras
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
| | - Isabel de la Torre-Diez
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
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Berke Erdaş Ç, Sümer E, Kibaroğlu S. CNN-based severity prediction of neurodegenerative diseases using gait data. Digit Health 2022; 8:20552076221075147. [PMID: 35111334 PMCID: PMC8801640 DOI: 10.1177/20552076221075147] [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: 07/05/2021] [Accepted: 01/03/2022] [Indexed: 11/17/2022] Open
Abstract
Neurodegenerative diseases occur because of degeneration in brain cells but can manifest as impairment of motor functions. One of the side effects of this impairment is an abnormality in walking. With the development of sensor technologies and artificial intelligence applications in recent years, the disease severity of patients can be estimated using their gait data. In this way, decision support applications for grading the severity of the disease that the patient suffers in the clinic can be developed. Thus, patients can have treatment methods more suitable for the severity of the disease. The presented research proposes a deep learning-based approach using gait data represented by a Quick Response code to develop an effective and reliable disease severity grading system for neurodegenerative diseases such as amyotrophic lateral sclerosis, Huntington's disease, and Parkinson's disease. The two-dimensional Quick Response data set was created by converting each one-dimensional gait data of the subjects with a novel representation approach to a Quick Response code. This data set was regressed with the convolutional neural network deep learning method, and a solution was sought for the problem of grading disease severity. Further, to demonstrate the success of the results obtained with the novel approach, native machine learning approaches such as Multilayer Perceptron, Random Forest, Extremely Randomized Trees, and K-Nearest Neighbours, and ensemble machine learning methods, such as voting and stacking, were applied on one-dimensional data. Finally, the results obtained on the prediction of disease severity by testing one-dimensional gait data with a convolutional neural network architecture that operates on one-dimensional data were included. The results showed that, in most cases, the two-dimensional convolutional neural network approach performed the best among all methods.
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Affiliation(s)
- Çağatay Berke Erdaş
- Department of Computer Engineering, Faculty of Engineering, Başkent University, Ankara, Turkey
| | - Emre Sümer
- Department of Computer Engineering, Faculty of Engineering, Başkent University, Ankara, Turkey
| | - Seda Kibaroğlu
- Department of Neurology, Faculty of Medicine, Başkent University, Ankara, Turkey
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Abstract
AbstractParkinson’s disease (PD) genes identification plays an important role in improving the diagnosis and treatment of the disease. A number of machine learning methods have been proposed to identify disease-related genes, but only few of these methods are adopted for PD. This work puts forth a novel neural network-based ensemble (n-semble) method to identify Parkinson’s disease genes. The artificial neural network is trained in a unique way to ensemble the multiple model predictions. The proposed n-semble method is composed of four parts: (1) protein sequences are used to construct feature vectors using physicochemical properties of amino acid; (2) dimensionality reduction is achieved using the t-Distributed Stochastic Neighbor Embedding (t-SNE) method, (3) the Jaccard method is applied to find likely negative samples from unknown (candidate) genes, and (4) gene prediction is performed with n-semble method. The proposed n-semble method has been compared with Smalter’s, ProDiGe, PUDI and EPU methods using various evaluation metrics. It has been concluded that the proposed n-semble method outperforms the existing gene identification methods over the other methods and achieves significantly higher precision, recall and F Score of 88.9%, 90.9% and 89.8%, respectively. The obtained results confirm the effectiveness and validity of the proposed framework.
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Ordinal classification of the affectation level of 3D-images in Parkinson diseases. Sci Rep 2021; 11:7067. [PMID: 33782476 PMCID: PMC8007580 DOI: 10.1038/s41598-021-86538-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 03/16/2021] [Indexed: 01/12/2023] Open
Abstract
Parkinson’s disease is characterised by a decrease in the density of presynaptic dopamine transporters in the striatum. Frequently, the corresponding diagnosis is performed using a qualitative analysis of the 3D-images obtained after the administration of \documentclass[12pt]{minimal}
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\begin{document}$$^{123}$$\end{document}123I-ioflupane, considering a binary classification problem (absence or existence of Parkinson’s disease). In this work, we propose a new methodology for classifying this kind of images in three classes depending on the level of severity of the disease in the image. To tackle this problem, we use an ordinal classifier given the natural order of the class labels. A novel strategy to perform feature selection is developed because of the large number of voxels in the image, and a method for generating synthetic images is proposed to improve the quality of the classifier. The methodology is tested on 434 studies conducted between September 2015 and January 2019, divided into three groups: 271 without alteration of the presynaptic nigrostriatal pathway, 73 with a slight alteration and 90 with severe alteration. Results confirm that the methodology improves the state-of-the-art algorithms, and that it is able to find informative voxels outside the standard regions of interest used for this problem. The differences are assessed by statistical tests which show that the proposed image ordinal classification could be considered as a decision support system in medicine.
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