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Mohammadi S, Rafii-Tabar H, Sasanpour P. A modeling study of the effect of an alternating magnetic field on magnetite nanoparticles in proximity of the neuronal microtubules: A proposed mechanism for detachment of tau proteins. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 222:106913. [PMID: 35738092 DOI: 10.1016/j.cmpb.2022.106913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 05/17/2022] [Accepted: 05/24/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE It is known that the disintegration of microtubules in neurons occurs in response to the phosphorylation of the tau proteins that promotes the structural instability of the microtubules, as one of the factors underlying the onset of Alzheimer's disease (AD). METHODS In this study, the mechanical variations undergone by the tau protein's and microtubule's structures due to the action of intrinsic magnetite nanoparticles inside the brain tissue have been computationally modeled using the finite element (FEM) method. RESULTS The von Mises stress induced by magnetite nanoparticles, subject to an applied alternating magnetic field, leads to local heating and mechanical forces, prompting a corresponding deformation in, and displacement of, the microtubule and the tau protein. CONCLUSIONS The induction of these deformations would increase the probability of the microtubules' depolymerization, and hence their eventual structural disintegration.
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Affiliation(s)
- Simah Mohammadi
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hashem Rafii-Tabar
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; The Physics Branch of Iran Academy of Sciences, Tehran, Iran.
| | - Pezhman Sasanpour
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Egger J, Gsaxner C, Pepe A, Pomykala KL, Jonske F, Kurz M, Li J, Kleesiek J. Medical deep learning-A systematic meta-review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106874. [PMID: 35588660 DOI: 10.1016/j.cmpb.2022.106874] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 04/22/2022] [Accepted: 05/10/2022] [Indexed: 05/22/2023]
Abstract
Deep learning has remarkably impacted several different scientific disciplines over the last few years. For example, in image processing and analysis, deep learning algorithms were able to outperform other cutting-edge methods. Additionally, deep learning has delivered state-of-the-art results in tasks like autonomous driving, outclassing previous attempts. There are even instances where deep learning outperformed humans, for example with object recognition and gaming. Deep learning is also showing vast potential in the medical domain. With the collection of large quantities of patient records and data, and a trend towards personalized treatments, there is a great need for automated and reliable processing and analysis of health information. Patient data is not only collected in clinical centers, like hospitals and private practices, but also by mobile healthcare apps or online websites. The abundance of collected patient data and the recent growth in the deep learning field has resulted in a large increase in research efforts. In Q2/2020, the search engine PubMed returned already over 11,000 results for the search term 'deep learning', and around 90% of these publications are from the last three years. However, even though PubMed represents the largest search engine in the medical field, it does not cover all medical-related publications. Hence, a complete overview of the field of 'medical deep learning' is almost impossible to obtain and acquiring a full overview of medical sub-fields is becoming increasingly more difficult. Nevertheless, several review and survey articles about medical deep learning have been published within the last few years. They focus, in general, on specific medical scenarios, like the analysis of medical images containing specific pathologies. With these surveys as a foundation, the aim of this article is to provide the first high-level, systematic meta-review of medical deep learning surveys.
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Affiliation(s)
- Jan Egger
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Styria, Austria; Department of Oral &Maxillofacial Surgery, Medical University of Graz, Auenbruggerplatz 5/1, 8036 Graz, Styria, Austria; Computer Algorithms for Medicine Laboratory, Graz, Styria, Austria; Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany; Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Hufelandstraße 55, 45147 Essen, Germany.
| | - Christina Gsaxner
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Styria, Austria; Department of Oral &Maxillofacial Surgery, Medical University of Graz, Auenbruggerplatz 5/1, 8036 Graz, Styria, Austria; Computer Algorithms for Medicine Laboratory, Graz, Styria, Austria
| | - Antonio Pepe
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Styria, Austria; Computer Algorithms for Medicine Laboratory, Graz, Styria, Austria
| | - Kelsey L Pomykala
- Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany
| | - Frederic Jonske
- Computer Algorithms for Medicine Laboratory, Graz, Styria, Austria; Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany
| | - Manuel Kurz
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Styria, Austria; Computer Algorithms for Medicine Laboratory, Graz, Styria, Austria
| | - Jianning Li
- Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Styria, Austria; Computer Algorithms for Medicine Laboratory, Graz, Styria, Austria; Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany
| | - Jens Kleesiek
- Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany; Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Hufelandstraße 55, 45147 Essen, Germany; German Cancer Consortium (DKTK), Partner Site Essen, Hufelandstraße 55, 45147 Essen, Germany
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53
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Fatima, Imran M, Ullah A, Arif M, Noor R. A unified technique for entropy enhancement based diabetic retinopathy detection using hybrid neural network. Comput Biol Med 2022; 145:105424. [DOI: 10.1016/j.compbiomed.2022.105424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/09/2022] [Accepted: 03/17/2022] [Indexed: 02/07/2023]
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Hernandez M, Ramon-Julvez U, Ferraz F. Explainable AI toward understanding the performance of the top three TADPOLE Challenge methods in the forecast of Alzheimer’s disease diagnosis. PLoS One 2022; 17:e0264695. [PMID: 35522653 PMCID: PMC9075665 DOI: 10.1371/journal.pone.0264695] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 02/16/2022] [Indexed: 11/18/2022] Open
Abstract
The Alzheimer′s Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge is the most comprehensive challenge to date with regard to the number of subjects, considered features, and challenge participants. The initial objective of TADPOLE was the identification of the most predictive data, features, and methods for the progression of subjects at risk of developing Alzheimer′s. The challenge was successful in recognizing tree-based ensemble methods such as gradient boosting and random forest as the best methods for the prognosis of the clinical status in Alzheimer’s disease (AD). However, the challenge outcome was limited to which combination of data processing and methods exhibits the best accuracy; hence, it is difficult to determine the contribution of the methods to the accuracy. The quantification of feature importance was globally approached by all the challenge participant methods. In addition, TADPOLE provided general answers that focused on improving performance while ignoring important issues such as interpretability. The purpose of this study is to intensively explore the models of the top three TADPOLE Challenge methods in a common framework for fair comparison. In addition, for these models, the most meaningful features for the prognosis of the clinical status of AD are studied and the contribution of each feature to the accuracy of the methods is quantified. We provide plausible explanations as to why the methods achieve such accuracy, and we investigate whether the methods use information coherent with clinical knowledge. Finally, we approach these issues through the analysis of SHapley Additive exPlanations (SHAP) values, a technique that has recently attracted increasing attention in the field of explainable artificial intelligence (XAI).
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Affiliation(s)
- Monica Hernandez
- Aragon Institute on Engineering Research, University of Zaragoza, Zaragoza, Spain
- * E-mail:
| | - Ubaldo Ramon-Julvez
- Aragon Institute on Engineering Research, University of Zaragoza, Zaragoza, Spain
| | - Francisco Ferraz
- Aragon Institute on Engineering Research, University of Zaragoza, Zaragoza, Spain
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55
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Chen L, Qiao H, Zhu F. Alzheimer's Disease Diagnosis With Brain Structural MRI Using Multiview-Slice Attention and 3D Convolution Neural Network. Front Aging Neurosci 2022; 14:871706. [PMID: 35557839 PMCID: PMC9088013 DOI: 10.3389/fnagi.2022.871706] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 03/17/2022] [Indexed: 01/01/2023] Open
Abstract
Numerous artificial intelligence (AI) based approaches have been proposed for automatic Alzheimer's disease (AD) prediction with brain structural magnetic resonance imaging (sMRI). Previous studies extract features from the whole brain or individual slices separately, ignoring the properties of multi-view slices and feature complementarity. For this reason, we present a novel AD diagnosis model based on the multiview-slice attention and 3D convolution neural network (3D-CNN). Specifically, we begin by extracting the local slice-level characteristic in various dimensions using multiple sub-networks. Then we proposed a slice-level attention mechanism to emphasize specific 2D-slices to exclude the redundancy features. After that, a 3D-CNN was employed to capture the global subject-level structural changes. Finally, all these 2D and 3D features were fused to obtain more discriminative representations. We conduct the experiments on 1,451 subjects from ADNI-1 and ADNI-2 datasets. Experimental results showed the superiority of our model over the state-of-the-art approaches regarding dementia classification. Specifically, our model achieves accuracy values of 91.1 and 80.1% on ADNI-1 for AD diagnosis and mild cognitive impairment (MCI) convention prediction, respectively.
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Affiliation(s)
- Lin Chen
- Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
| | - Hezhe Qiao
- Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Fan Zhu
- Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
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56
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Designing a clinical decision support system for Alzheimer’s diagnosis on OASIS-3 data set. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103527] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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57
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Zhang J, He X, Qing L, Gao F, Wang B. BPGAN: Brain PET synthesis from MRI using generative adversarial network for multi-modal Alzheimer's disease diagnosis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 217:106676. [PMID: 35167997 DOI: 10.1016/j.cmpb.2022.106676] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 01/30/2022] [Accepted: 01/30/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Multi-modal medical images, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), have been widely used for the diagnosis of brain disorder diseases like Alzheimer's disease (AD) since they can provide various information. PET scans can detect cellular changes in organs and tissues earlier than MRI. Unlike MRI, PET data is difficult to acquire due to cost, radiation, or other limitations. Moreover, PET data is missing for many subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. To solve this problem, a 3D end-to-end generative adversarial network (named BPGAN) is proposed to synthesize brain PET from MRI scans, which can be used as a potential data completion scheme for multi-modal medical image research. METHODS We propose BPGAN, which learns an end-to-end mapping function to transform the input MRI scans to their underlying PET scans. First, we design a 3D multiple convolution U-Net (MCU) generator architecture to improve the visual quality of synthetic results while preserving the diverse brain structures of different subjects. By further employing a 3D gradient profile (GP) loss and structural similarity index measure (SSIM) loss, the synthetic PET scans have higher-similarity to the ground truth. In this study, we explore alternative data partitioning ways to study their impact on the performance of the proposed method in different medical scenarios. RESULTS We conduct experiments on a publicly available ADNI database. The proposed BPGAN is evaluated by mean absolute error (MAE), peak-signal-to-noise-ratio (PSNR) and SSIM, superior to other compared models in these quantitative evaluation metrics. Qualitative evaluations also validate the effectiveness of our approach. Additionally, combined with MRI and our synthetic PET scans, the accuracies of multi-class AD diagnosis on dataset-A and dataset-B are 85.00% and 56.47%, which have been improved by about 1% and 1%, respectively, compared to the stand-alone MRI. CONCLUSIONS The experimental results of quantitative measures, qualitative displays, and classification evaluation demonstrate that the synthetic PET images by BPGAN are reasonable and high-quality, which provide complementary information to improve the performance of AD diagnosis. This work provides a valuable reference for multi-modal medical image analysis.
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Affiliation(s)
- Jin Zhang
- College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610064, China
| | - Xiaohai He
- College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610064, China.
| | - Linbo Qing
- College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610064, China
| | - Feng Gao
- National Interdisciplinary Institute on Aging (NIIA), Southwest Jiaotong University, Chengdu, Sichuan, 611756, China; External cooperation and liaison office, Southwest Jiaotong University, Chengdu, Sichuan, 611756, China
| | - Bin Wang
- College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610064, China
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Zhu L, Zhang L, Hu W, Chen H, Li H, Wei S, Chen X, Ma X. A multi-task two-path deep learning system for predicting the invasiveness of craniopharyngioma. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 216:106651. [PMID: 35104686 DOI: 10.1016/j.cmpb.2022.106651] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 01/19/2022] [Accepted: 01/19/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Craniopharyngioma is a kind of benign brain tumor in histography. However, it might be clinically aggressive and have severe manifestations, such as increased intracranial pressure, hypothalamic-pituitary dysfunction, and visual impairment. It is considered challenging for radiologists to predict the invasiveness of craniopharyngioma through MRI images. Therefore, developing a non-invasive method that can predict the invasiveness and boundary of CP as a reference before surgery is of clinical value for making more appropriate and individualized treatment decisions and reducing the occurrence of inappropriate surgical plan choices. METHODS The MT-Brain system has consisted of two pathways, a sub-path based on 2D CNN for capturing the features from each slice of MRI images, and a 3D sub-network for capturing additional context information between slices. By introducing the two-path architecture, our system can make full use of the fusion of the above 2D and 3D features for classification. Furthermore, position encoding and mask-guided attention also have been introduced to improve the segmentation and diagnosis performance. To verify the performance of the MT-Brain system, we have enrolled 1032 patients with craniopharyngioma (302 invasion and 730 non-invasion patients), segmented the tumors on postcontrast coronal T1WI and randomized them into a training dataset and a testing dataset at a ratio of 8:2. RESULTS The MT-Brain system achieved a remarkable performance in diagnosing the invasiveness of craniopharyngioma with the AUC of 83.84%, the accuracy of 77.94%, the sensitivity of 70.97%, and the specificity of 80.99%. In the lesion segmentation task, the predicted boundaries of lesions were similar to those labeled by radiologists with the dice of 66.36%. In addition, some explorations also have been made on the interpretability of deep learning models, illustrating the reliability of the model. CONCLUSIONS To the best of our knowledge, this study is the first to develop an integrated deep learning model to predict the invasiveness of craniopharyngioma preoperatively and locate the lesion boundary synchronously on MRI. The excellent performances indicate that the MT-Brain system has great potential in real-world clinical applications.
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Affiliation(s)
- Lin Zhu
- School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China; CBSR&NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Lingling Zhang
- Department of radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Wenxing Hu
- University of New South Wales, Sydney, Australia
| | - Haixu Chen
- Institute of Geriatrics&National Clinical Research Center of Geriatrics Disease, The Second Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
| | - Han Li
- CBSR&NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Department of Orthopedics, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Shoushui Wei
- School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China.
| | - Xuzhu Chen
- Department of radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Xibo Ma
- CBSR&NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of the Chinese Academy of Sciences, Beijing, 100049, China.
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A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders. BIOLOGY 2022; 11:biology11030469. [PMID: 35336842 PMCID: PMC8945195 DOI: 10.3390/biology11030469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/12/2022] [Accepted: 03/14/2022] [Indexed: 01/19/2023]
Abstract
Simple Summary This study represents a resourceful review article that can deliver resources on neurological diseases and their implemented classification algorithms to reveal the future direction of researchers. Researchers interested in studying neurological diseases and previously implemented techniques in this field can follow this article. Various challenges occur in detecting different stages of the disorders. A limited amount of labeled and unlabeled datasets and other limitations is represented in this article to assist them in finding out the directions. The authors’ purpose for composing this article is to make a straightforward and concrete path for researchers to quickly find the way and the scope in this field for implementing future research on neurological disease detection. Abstract Neurological disorders (NDs) are becoming more common, posing a concern to pregnant women, parents, healthy infants, and children. Neurological disorders arise in a wide variety of forms, each with its own set of origins, complications, and results. In recent years, the intricacy of brain functionalities has received a better understanding due to neuroimaging modalities, such as magnetic resonance imaging (MRI), magnetoencephalography (MEG), and positron emission tomography (PET), etc. With high-performance computational tools and various machine learning (ML) and deep learning (DL) methods, these modalities have discovered exciting possibilities for identifying and diagnosing neurological disorders. This study follows a computer-aided diagnosis methodology, leading to an overview of pre-processing and feature extraction techniques. The performance of existing ML and DL approaches for detecting NDs is critically reviewed and compared in this article. A comprehensive portion of this study also shows various modalities and disease-specified datasets that detect and records images, signals, and speeches, etc. Limited related works are also summarized on NDs, as this domain has significantly fewer works focused on disease and detection criteria. Some of the standard evaluation metrics are also presented in this study for better result analysis and comparison. This research has also been outlined in a consistent workflow. At the conclusion, a mandatory discussion section has been included to elaborate on open research challenges and directions for future work in this emerging field.
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Ashtari-Majlan M, Seifi A, Dehshibi MM. A multi-stream convolutional neural network for classification of progressive MCI in Alzheimer's disease using structural MRI images. IEEE J Biomed Health Inform 2022; 26:3918-3926. [PMID: 35239494 DOI: 10.1109/jbhi.2022.3155705] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Early diagnosis of Alzheimers disease and its prodromal stage, also known as mild cognitive impairment (MCI), is critical since some patients with progressive MCI will develop the disease. We propose a multi-stream deep convolutional neural network fed with patch-based imaging data to classify stable MCI and progressive MCI. First, we compare MRI images of Alzheimers disease with cognitively normal subjects to identify distinct anatomical landmarks using a multivariate statistical test. These landmarks are then used to extract patches that are fed into the proposed multi-stream convolutional neural network to classify MRI images. Next, we train the architecture in a separate scenario using samples from Alzheimers disease images, which are anatomically similar to the progressive MCI ones and cognitively normal images to compensate for the lack of progressive MCI training data. Finally, we transfer the trained model weights to the proposed architecture in order to fine-tune the model using progressive MCI and stable MCI data. Experimental results on the ADNI-1 dataset indicate that our method outperforms existing methods for MCI classification, with an F1-score of 85.96%.
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Merkin A, Krishnamurthi R, Medvedev ON. Machine learning, artificial intelligence and the prediction of dementia. Curr Opin Psychiatry 2022; 35:123-129. [PMID: 34861656 DOI: 10.1097/yco.0000000000000768] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE OF REVIEW Artificial intelligence and its division machine learning are emerging technologies that are increasingly applied in medicine. Artificial intelligence facilitates automatization of analytical modelling and contributes to prediction, diagnostics and treatment of diseases. This article presents an overview of the application of artificial intelligence in dementia research. RECENT FINDINGS Machine learning and its branch Deep Learning are widely used in research to support in diagnosis and prediction of dementia. Deep Learning models in certain tasks often result in better accuracy of detection and prediction of dementia than traditional machine learning methods, but they are more costly in terms of run times and hardware requirements. Both machine learning and Deep Learning models have their own strengths and limitations. Currently, there are few datasets with limited data available to train machine learning models. There are very few commercial applications of machine learning in medical practice to date, mostly represented by mobile applications, which include questionnaires and psychometric assessments with limited machine learning data processing. SUMMARY Application of machine learning technologies in detection and prediction of dementia may provide an advantage to psychiatry and neurology by promoting a better understanding of the nature of the disease and more accurate evidence-based processes that are reproducible and standardized.
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Affiliation(s)
| | | | - Oleg N Medvedev
- University of Waikato, School of Psychology, Hamilton, New Zealand
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Ardalan Z, Subbian V. Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review. Front Artif Intell 2022; 5:780405. [PMID: 35265830 PMCID: PMC8899512 DOI: 10.3389/frai.2022.780405] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 01/17/2022] [Indexed: 12/18/2022] Open
Abstract
Deep learning algorithms have been moderately successful in diagnoses of diseases by analyzing medical images especially through neuroimaging that is rich in annotated data. Transfer learning methods have demonstrated strong performance in tackling annotated data. It utilizes and transfers knowledge learned from a source domain to target domain even when the dataset is small. There are multiple approaches to transfer learning that result in a range of performance estimates in diagnosis, detection, and classification of clinical problems. Therefore, in this paper, we reviewed transfer learning approaches, their design attributes, and their applications to neuroimaging problems. We reviewed two main literature databases and included the most relevant studies using predefined inclusion criteria. Among 50 reviewed studies, more than half of them are on transfer learning for Alzheimer's disease. Brain mapping and brain tumor detection were second and third most discussed research problems, respectively. The most common source dataset for transfer learning was ImageNet, which is not a neuroimaging dataset. This suggests that the majority of studies preferred pre-trained models instead of training their own model on a neuroimaging dataset. Although, about one third of studies designed their own architecture, most studies used existing Convolutional Neural Network architectures. Magnetic Resonance Imaging was the most common imaging modality. In almost all studies, transfer learning contributed to better performance in diagnosis, classification, segmentation of different neuroimaging diseases and problems, than methods without transfer learning. Among different transfer learning approaches, fine-tuning all convolutional and fully-connected layers approach and freezing convolutional layers and fine-tuning fully-connected layers approach demonstrated superior performance in terms of accuracy. These recent transfer learning approaches not only show great performance but also require less computational resources and time.
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Affiliation(s)
- Zaniar Ardalan
- Department of Systems and Industrial Engineering, College of Engineering, University of Arizona, Tucson, AZ, United States
- *Correspondence: Zaniar Ardalan
| | - Vignesh Subbian
- Department of Systems and Industrial Engineering, College of Engineering, University of Arizona, Tucson, AZ, United States
- Department of Biomedical Engineering, College of Engineering, University of Arizona, Tucson, AZ, United States
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Developing an Artificial Intelligence Model for Tumor Grading and Classification, Based on MRI Sequences of Human Brain Gliomas. INTERNATIONAL JOURNAL OF CANCER MANAGEMENT 2022. [DOI: 10.5812/ijcm.120638] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Background: Artificial intelligence (AI) models have provided advanced applications to many scientific areas, including the prediction of the pathologic grade of tumors, utilizing radiology techniques. Gliomas are among the malignant brain tumors in human adults, and their efficient diagnosis is of high clinical significance. Objectives: Given the contribution of AI to medical diagnoses, we investigated the role of deep learning in the differential diagnosis and grading of human brain gliomas. Methods: This study developed a new AI diagnostic model, i.e., EfficientNetB0, to grade and classify human brain gliomas, using sequences from magnetic resonance imaging (MRI). Results: We validated the new AI model, using a standard dataset (BraTS-2019) and demonstrated that the AI components, i.e., convolutional neural networks and transfer learning, provided excellent performance for classifying and grading glioma images at 98.8% accuracy. Conclusions: The proposed model, EfficientNetB0, is capable to classify and grade glioma from MRI sequences at high accuracy, validity, and specificity. It can provide better performance and diagnostic results for human glioma images than models developed by previous studies.
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Li R, Wang X, Lawler K, Garg S, Bai Q, Alty J. Applications of Artificial Intelligence to aid detection of dementia: a scoping review on current capabilities and future directions. J Biomed Inform 2022; 127:104030. [DOI: 10.1016/j.jbi.2022.104030] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/21/2022] [Accepted: 02/12/2022] [Indexed: 12/17/2022]
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Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103293] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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66
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Poloni KM, Ferrari RJ. Automated detection, selection and classification of hippocampal landmark points for the diagnosis of Alzheimer's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 214:106581. [PMID: 34923325 DOI: 10.1016/j.cmpb.2021.106581] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 11/12/2021] [Accepted: 12/04/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Alzheimer's disease (AD) is a neurodegenerative, progressive, and irreversible disease that accounts for up to 80% of all dementia cases. AD predominantly affects older adults, and its clinical diagnosis is a challenging evaluation process, with imprecision rates between 12 and 23%. Structural magnetic resonance (MR) imaging has been widely used in studies related to AD because this technique provides images with excellent anatomical details and information about structural changes induced by the disease in the brain. Current studies are focused on detecting AD in its initial stage, i.e., mild cognitive impairment (MCI), since treatments for preventing or delaying the onset of symptoms is more effective when administered at the early stages of the disease. This study proposes a new technique to perform MR image classification in AD diagnosis using discriminative hippocampal point landmarks among the cognitively normal (CN), MCI, and AD populations. METHODS Our approach, based on a two-level classification, first detects and selects discriminative landmark points from two diagnosis populations based on their matching distance compared to a probabilistic atlas of 3-D labeled landmark points. The points are classified using attributes computed in a spherical support region around each point using information from brain probability image tissues of gray matter, white matter, and cerebrospinal fluid as sources of information. Next, at the second level, the images are classified based on a quantitative evaluation obtained from the first-level classifier outputs. RESULTS For the CN×MCI experiment, we achieved an AUC of 0.83, an accuracy of 75.58%, with 72.9% of sensitivity and 77.81% of specificity. For the MCI×AD experiment, we achieved an AUC value of 0.73, an accuracy of 69.8%, a sensitivity of 74.09% and specificity of 64.57%. Finally, for the CN×AD, we achieved an AUC of 0.95, an accuracy of 89.24%, with 85.58% of sensitivity and 92.71% of specificity. CONCLUSIONS The obtained classification results are similar to (or even higher than) other studies that classify AD compared to CN individuals and comparable to those classified patients with MCI.
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Affiliation(s)
- Katia M Poloni
- Department of Computing, Federal University of São Carlos, Rod. Washington Luis, Km 235, São Carlos, 13565-905, SP, Brazil
| | - Ricardo J Ferrari
- Department of Computing, Federal University of São Carlos, Rod. Washington Luis, Km 235, São Carlos, 13565-905, SP, Brazil.
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Sethi M, Ahuja S, Rani S, Koundal D, Zaguia A, Enbeyle W. An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network. BIOMED RESEARCH INTERNATIONAL 2022; 2022:8739960. [PMID: 35103240 PMCID: PMC8800619 DOI: 10.1155/2022/8739960] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/08/2021] [Indexed: 12/15/2022]
Abstract
Alzheimer's disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning models have shown outstanding performance in the diagnosis of AD, and these models do not need any handcrafted feature extraction over conventional machine learning algorithms. Since the 2012 AlexNet accomplishment, the convolutional neural network (CNN) has been progressively utilized by the medical community to assist practitioners to early diagnose AD. This paper explores the current cutting edge applications of CNN on single and multimodality (combination of two or more modalities) neuroimaging data for the classification of AD. An exhaustive systematic search is conducted on four notable databases: Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed in June 2021. The objective of this study is to examine the effectiveness of classification approaches on AD to analyze different kinds of datasets, neuroimaging modalities, preprocessing techniques, and data handling methods. However, CNN has achieved great success in the classification of AD; still, there are a lot of challenges particularly due to scarcity of medical imaging data and its possible scope in this field.
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Affiliation(s)
- Monika Sethi
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Sachin Ahuja
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Shalli Rani
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Deepika Koundal
- Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
| | - Atef Zaguia
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. BOX 11099, Taif 21944, Saudi Arabia
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Deep Learning for Diagnosis of Alzheimer’s Disease with FDG-PET Neuroimaging. PATTERN RECOGNITION AND IMAGE ANALYSIS 2022. [DOI: 10.1007/978-3-031-04881-4_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Decuyper M, Maebe J, Van Holen R, Vandenberghe S. Artificial intelligence with deep learning in nuclear medicine and radiology. EJNMMI Phys 2021; 8:81. [PMID: 34897550 PMCID: PMC8665861 DOI: 10.1186/s40658-021-00426-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 11/19/2021] [Indexed: 12/19/2022] Open
Abstract
The use of deep learning in medical imaging has increased rapidly over the past few years, finding applications throughout the entire radiology pipeline, from improved scanner performance to automatic disease detection and diagnosis. These advancements have resulted in a wide variety of deep learning approaches being developed, solving unique challenges for various imaging modalities. This paper provides a review on these developments from a technical point of view, categorizing the different methodologies and summarizing their implementation. We provide an introduction to the design of neural networks and their training procedure, after which we take an extended look at their uses in medical imaging. We cover the different sections of the radiology pipeline, highlighting some influential works and discussing the merits and limitations of deep learning approaches compared to other traditional methods. As such, this review is intended to provide a broad yet concise overview for the interested reader, facilitating adoption and interdisciplinary research of deep learning in the field of medical imaging.
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Affiliation(s)
- Milan Decuyper
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Jens Maebe
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Roel Van Holen
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Stefaan Vandenberghe
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
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71
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Li L, Jiang H, Wen G, Cao P, Xu M, Liu X, Yang J, Zaiane O. TE-HI-GCN: An Ensemble of Transfer Hierarchical Graph Convolutional Networks for Disorder Diagnosis. Neuroinformatics 2021; 20:353-375. [PMID: 34761367 DOI: 10.1007/s12021-021-09548-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/14/2021] [Indexed: 11/25/2022]
Abstract
Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life for patients and potentially supports the development of new treatments. Graph convolutional networks (GCNs) are shown to be successful in modeling applications with graph structures. However, training an accurate GCNs model for brain networks faces several challenges, including high dimensional and noisy correlation in the brain networks, limited labeled training data, and depth limitation of GCN learning. Generalization and interpretability are important in developing predictive models for clinical diagnosis. To address these challenges, we proposed an ensemble framework involving hierarchical GCN and transfer learning for sparse brain networks, which allows GCN to capture the intrinsic correlation among the subjects and domains, to improve the network embedding learning for disease diagnosis. Extensive experiments on two real medical clinical applications: diagnosis of Autism spectrum disorder (ASD) and diagnosis of Alzheimer's disease (AD) on both the ADNI and ABIDE databases, showing the effectiveness of the proposed framework. We achieved state-of-the-art accuracy and AUC for AD/MCI and ASD/NC (Normal control) classification in comparison with studies that used functional connectivity as features or GCN models. The proposed TE-HI-GCN model achieves the best classification performance, leading to about 27.93% (31.38%) improvement for ASD and 16.86% (44.50%) for AD in terms of accuracy and AUC compared with the traditional GCN model. Moreover, the obtained clustering results show high correspondence with the previous neuroimaging derived evidence of within and between-networks biomarkers for ASD. The discovered subnetworks are used as evidence for the proposed TE-HI-GCN model. Furthermore, this work is the first attempt of transfer learning on the two related disorder domains to uncover the correlation among the two diseases with a transfer learning scheme.
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Affiliation(s)
- Lanting Li
- Computer Science and Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China
| | - Hao Jiang
- Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Guangqi Wen
- Computer Science and Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China
| | - Peng Cao
- Computer Science and Engineering, Northeastern University, Shenyang, China.
- Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China.
| | - Mingyi Xu
- Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Xiaoli Liu
- Department of Chemical and Biomolecular Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore
| | - Jinzhu Yang
- Computer Science and Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China
| | - Osmar Zaiane
- Amii, University of Alberta, Edmonton, Alberta, Canada
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Qiao H, Chen L, Zhu F. A Fusion of Multi-view 2D and 3D Convolution Neural Network based MRI for Alzheimer's Disease Diagnosis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3317-3321. [PMID: 34891950 DOI: 10.1109/embc46164.2021.9629923] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease leading to irreversible and progressive brain damage. Close monitoring is essential for slowing down the progression of AD. Magnetic Resonance Imaging (MRI) has been widely used for AD diagnosis and disease monitoring. Previous studies usually focused on extracting features from whole image or specific slices separately, but ignore the characteristics of each slice from multiple perspectives and the complementarity between features at different scales. In this study, we proposed a novel classification method based on the fusion of multi-view 2D and 3D convolutions for MRI-based AD diagnosis. Specifically, we first use multiple sub-networks to extract the local slice-level feature of each slice in different dimensions. Then a 3D convolution network was used to extract the global subject-level information of MRI. Finally, local and global information were fused to acquire more discriminative features. Experiments conducted on the ADNI-1 and ADNI-2 dataset demonstrated the superiority of this proposed model over other state-of-the-art methods for their ability to discriminate AD and Normal Controls (NC). Our model achieves 90.2% and 85.2% of accuracy on ADNI-2 and ADNI-1 respectively, thus it can be effective in AD diagnosis. The source code of our model is freely available at https://github.com/fengduqianhe/ADMultiView.
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Alinsaif S, Lang J. 3D shearlet-based descriptors combined with deep features for the classification of Alzheimer's disease based on MRI data. Comput Biol Med 2021; 138:104879. [PMID: 34598071 DOI: 10.1016/j.compbiomed.2021.104879] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 09/15/2021] [Accepted: 09/15/2021] [Indexed: 01/14/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease that afflicts millions of people worldwide. Early detection of AD is critical, as drug trials show a promising advantage to those patients with early diagnoses. In this study, magnetic resonance imaging (MRI) datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and The Open Access Series of Imaging Studies are used. Our method for performing the classification of AD is to combine a set of shearlet-based descriptors with deep features. A major challenge in classifying such MRI datasets is the high dimensionality of feature vectors because of the large number of slices of each MRI sample. Given the volumetric nature of the MRI data, we propose using the 3D shearlet transform (3D-ST), but we obtain the average of all directionalities, which reduces the dimensionality. On the other hand, we propose to leverage the capabilities of convolutional neural networks (CNN) to learn feature maps from stacked MRI slices, which generate a very compact feature vector for each MRI sample. The 3D-ST and CNN feature vectors are combined for the classification of AD. After the concatenation of the feature vectors, they are used to train a classifier. Alternatively, a custom CNN model is utilized, in which the descriptors are further processed end to end to obtain the classification model. Our experimental results show that the fusion of shearlet-based descriptors and deep features improves classification performance, especially on the ADNI dataset.
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Affiliation(s)
- Sadiq Alinsaif
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, K1N 6N5, Canada; College of Computer Science and Engineering, University of Hafr Al Batin, Al Jamiah, Hafar Al Batin, 39524, Saudi Arabia.
| | - Jochen Lang
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, K1N 6N5, Canada.
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Grueso S, Viejo-Sobera R. Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review. Alzheimers Res Ther 2021; 13:162. [PMID: 34583745 PMCID: PMC8480074 DOI: 10.1186/s13195-021-00900-w] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 09/12/2021] [Indexed: 01/18/2023]
Abstract
BACKGROUND An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer's disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer's disease dementia. METHODS We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer's disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. RESULTS Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer's disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. CONCLUSIONS Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.
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Affiliation(s)
- Sergio Grueso
- Cognitive NeuroLab, Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Rambla del Poblenou 156, 08018, Barcelona, Spain.
| | - Raquel Viejo-Sobera
- Cognitive NeuroLab, Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Rambla del Poblenou 156, 08018, Barcelona, Spain
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Qiao H, Chen L, Ye Z, Zhu F. Early Alzheimer's disease diagnosis with the contrastive loss using paired structural MRIs. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106282. [PMID: 34343744 DOI: 10.1016/j.cmpb.2021.106282] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 07/08/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Alzheimer's Disease (AD) is a chronic and fatal neurodegenerative disease with progressive impairment of memory. Brain structural magnetic resonance imaging (sMRI) has been widely applied as important biomarkers of AD. Various machine learning approaches, especially deep learning-based models, have been proposed for the early diagnosis of AD and monitoring the disease progression on sMRI data. However, the requirement for a large number of training images still hinders the extensive usage of AD diagnosis. In addition, due to the similarities in human whole-brain structure, finding the subtle brain changes is essential to extract discriminative features from limited sMRI data effectively. METHODS In this work, we proposed two types of contrastive losses with paired sMRIs to promote the diagnostic performance using group categories (G-CAT) and varying subject mini-mental state examination (S-MMSE) information, respectively. Specifically, G-CAT contrastive loss layer was used to learn the closer feature representation from sMRIs with the same categories, while ranking information from S-MMSE assists the model to explore subtle changes between individuals. RESULTS The model was trained on ADNI-1. Comparison with baseline methods was performed on MIRIAD and ADNI-2. For the classification task on MIRIAD, S-MMSE achieves 93.5% of accuracy, 96.6% of sensitivity, and 94.9% of specificity, respectively. G-CAT and S-MMSE both reach remarkable performance in terms of classification sensitivity and specificity respectively. Comparing with state-of-the-art methods, we found this proposed method could achieve comparable results with other approaches. CONCLUSION The proposed model could extract discriminative features under whole-brain similarity. Extensive experiments also support the accuracy of this model, i.e., it provides better ability to identify uncertain samples, especially for the classification task of subjects with MMSE in 22-27. Source code is freely available at https://github.com/fengduqianhe/ADComparative.
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Affiliation(s)
- Hezhe Qiao
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; University of Chinese Academy of Sciences, BeiJing 100049, China.
| | - Lin Chen
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
| | - Zi Ye
- Johns Hopkins University, Baltimore, MD 21218, United States of America.
| | - Fan Zhu
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
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Xie Y, Jiang H, Du H, Xu J, Qiu B. Fasu-Net: Fast Alzheimer’s Disease Screening with Undersampled MRI Using Convolutional Neural Networks. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Alzheimer’s Disease (AD) is a progressive and irreversible neurodegenerative condition, which results in dementia. Mild Cognitive Impairment (MCI) is an intermediate state between normal aging and AD. Instead of traditional questionnaire method, magnetic resonance imaging (MRI)
can be used by radiologists to diagnose and screening AD recently, but long acquisition time is not conducive to screening AD and MCI. To solve this problem, we develop a Fasu-Net (Fast Alzheimer’s disease Screening neural network with Undersampled MRI) for AD and MCI clinical classification.
The network uses undersampled structural MRI with a shorter acquisition time to improve the screening and diagnosis efficiency of AD. For achieving the best classification result, three axial planes of brain MR images were feed into the Fasu-Net with transfer learning method. The experiment
results on undersampled 3D T1-weighted images database (ADNI) show that in the AD versus MCI versus HC (Healthy Controls) classification, the Fasu-Net achieved the accuracy of 91.41%, thus can be a potential method for fast clinical screening of AD.
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Affiliation(s)
- Yuanbo Xie
- Hefei National Lab for Physical Sciences at the Microscale and the Centers for Biomedical Engineering, University of Science and Technology of China, Hefei 230027, China
| | - Haitao Jiang
- Hefei National Lab for Physical Sciences at the Microscale and the Centers for Biomedical Engineering, University of Science and Technology of China, Hefei 230027, China
| | - Hongwei Du
- Hefei National Lab for Physical Sciences at the Microscale and the Centers for Biomedical Engineering, University of Science and Technology of China, Hefei 230027, China
| | - Jinzhang Xu
- School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui 230009, China
| | - Bensheng Qiu
- Hefei National Lab for Physical Sciences at the Microscale and the Centers for Biomedical Engineering, University of Science and Technology of China, Hefei 230027, China
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Shoeibi A, Khodatars M, Jafari M, Moridian P, Rezaei M, Alizadehsani R, Khozeimeh F, Gorriz JM, Heras J, Panahiazar M, Nahavandi S, Acharya UR. Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review. Comput Biol Med 2021; 136:104697. [PMID: 34358994 DOI: 10.1016/j.compbiomed.2021.104697] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 07/22/2021] [Accepted: 07/25/2021] [Indexed: 11/18/2022]
Abstract
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far; among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error; on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided.
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Affiliation(s)
- Afshin Shoeibi
- Faculty of Electrical Engineering, Biomedical Data Acquisition Lab (BDAL), K. N. Toosi University of Technology, Tehran, Iran.
| | - Marjane Khodatars
- Faculty of Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Mahboobeh Jafari
- Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran
| | - Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Mitra Rezaei
- Electrical and Computer Engineering Dept., Tarbiat Modares University, Tehran, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - Fahime Khozeimeh
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - Juan Manuel Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Spain; Department of Psychiatry. University of Cambridge, UK
| | - Jónathan Heras
- Department of Mathematics and Computer Science, University of La Rioja, La Rioja, Spain
| | | | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - U Rajendra Acharya
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Dept. of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan
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Veluppal A, Sadhukhan D, Gopinath V, Swaminathan R. Detection of Mild Cognitive Impairment using Kernel Density Estimation based texture analysis of the Corpus Callosum in brain MR images. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2021.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Ebrahimi A, Luo S, Chiong R. Deep sequence modelling for Alzheimer's disease detection using MRI. Comput Biol Med 2021; 134:104537. [PMID: 34118752 DOI: 10.1016/j.compbiomed.2021.104537] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 05/27/2021] [Accepted: 05/27/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is one of the deadliest diseases in developed countries. Treatments following early AD detection can significantly delay institutionalisation and extend patients' independence. There has been a growing focus on early AD detection using artificial intelligence. Convolutional neural networks (CNNs) have proven revolutionary for image-based applications and have been applied to brain scans. In recent years, studies have utilised two-dimensional (2D) CNNs on magnetic resonance imaging (MRI) scans for AD detection. To apply a 2D CNN on three-dimensional (3D) MRI volumes, each MRI scan is split into 2D image slices. A CNN is trained over the image slices by calculating a loss function between each subject's label and each image slice's predicted output. Although 2D CNNs can discover spatial dependencies in an image slice, they cannot understand the temporal dependencies among 2D image slices in a 3D MRI volume. This study aims to resolve this issue by modelling the sequence of MRI features produced by a CNN with deep sequence-based networks for AD detection. METHOD The CNN utilised in this paper was ResNet-18 pre-trained on an ImageNet dataset. The employed sequence-based models were the temporal convolutional network (TCN) and different types of recurrent neural networks. Several deep sequence-based models and configurations were implemented and compared for AD detection. RESULTS Our proposed TCN model achieved the best classification performance with 91.78% accuracy, 91.56% sensitivity and 92% specificity. CONCLUSION Our results show that applying sequence-based models can improve the classification accuracy of 2D and 3D CNNs for AD detection by up to 10%.
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Affiliation(s)
- Amir Ebrahimi
- School of Electrical Engineering and Computing, The University of Newcastle, NSW 2308, Australia
| | - Suhuai Luo
- School of Electrical Engineering and Computing, The University of Newcastle, NSW 2308, Australia
| | - Raymond Chiong
- School of Electrical Engineering and Computing, The University of Newcastle, NSW 2308, Australia.
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Li Q, Zhang Y, Liang H, Gong H, Jiang L, Liu Q, Shen L. Deep learning based neuronal soma detection and counting for Alzheimer's disease analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 203:106023. [PMID: 33744751 DOI: 10.1016/j.cmpb.2021.106023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 02/21/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Alzheimer's Disease (AD) is associated with neuronal damage and decrease. Micro-Optical Sectioning Tomography (MOST) provides an approach to acquire high-resolution images for neuron analysis in the whole-brain. Application of this technique to AD mouse brain enables us to investigate neuron changes during the progression of AD pathology. However, how to deal with the huge amount of data becomes the bottleneck. METHODS Using MOST technology, we acquired 3D whole-brain images of six AD mice, and sampled the imaging data of four regions in each mouse brain for AD progression analysis. To count the number of neurons, we proposed a deep learning based method by detecting neuronal soma in the neuronal images. In our method, the neuronal images were first cut into small cubes, then a Convolutional Neural Network (CNN) classifier was designed to detect the neuronal soma by classifying the cubes into three categories, "soma", "fiber", and "background". RESULTS Compared with the manual method and currently available NeuroGPS software, our method demonstrates faster speed and higher accuracy in identifying neurons from the MOST images. By applying our method to various brain regions of 6-month-old and 12-month-old AD mice, we found that the amount of neurons in three brain regions (lateral entorhinal cortex, medial entorhinal cortex, and presubiculum) decreased slightly with the increase of age, which is consistent with the experimental results previously reported. CONCLUSION This paper provides a new method to automatically handle the huge amounts of data and accurately identify neuronal soma from the MOST images. It also provides the potential possibility to construct a whole-brain neuron projection to reveal the impact of AD pathology on mouse brain.
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Affiliation(s)
- Qiufu Li
- Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China; AI Research Center for Medical Image Analysis and Diagnosis, Shenzhen University, Shenzhen 518060, China; Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China
| | - Yu Zhang
- College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - Hanbang Liang
- Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China; AI Research Center for Medical Image Analysis and Diagnosis, Shenzhen University, Shenzhen 518060, China; Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China
| | - Hui Gong
- National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
| | - Liang Jiang
- College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong, 518055, China.
| | - Qiong Liu
- College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong, 518055, China; Shenzhen Bay Laboratory, Shenzhen, 518055, China; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China.
| | - Linlin Shen
- Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China; AI Research Center for Medical Image Analysis and Diagnosis, Shenzhen University, Shenzhen 518060, China; Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China.
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82
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Ebrahimi A, Luo S. Convolutional neural networks for Alzheimer's disease detection on MRI images. J Med Imaging (Bellingham) 2021; 8:024503. [PMID: 33937437 DOI: 10.1117/1.jmi.8.2.024503] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Accepted: 04/13/2021] [Indexed: 01/17/2023] Open
Abstract
Purpose: Detection of Alzheimer's disease (AD) on magnetic resonance imaging (MRI) using convolutional neural networks (CNNs), which is useful for detecting AD in its preliminary states. Approach: Our study implements and compares several deep models and configurations, including two-dimensional (2D) and three-dimensional (3D) CNNs and recurrent neural networks (RNNs). To use a 2D CNN on 3D MRI volumes, each MRI scan is split into 2D slices, neglecting the connection among 2D image slices in an MRI volume. Instead, a CNN model could be followed by an RNN in a way that the model of 2D CNN + RNN can understand the connection among sequences of 2D image slices for an MRI. The issue is that the feature extraction step in the 2D CNN is independent of classification in the RNN. To tackle this, 3D CNNs can be employed instead of 2D CNNs to make voxel-based decisions. Our study's main contribution is to introduce transfer learning from a dataset of 2D images to 3D CNNs. Results: The results on our MRI dataset indicate that sequence-based decisions improve the accuracy of slice-based decisions by 2% in classifying AD patients from healthy subjects. Also the 3D voxel-based method with transfer learning outperforms the other methods with 96.88% accuracy, 100% sensitivity, and 94.12% specificity. Conclusions: Several implementations and experiments using CNNs on MRI scans for AD detection demonstrated that the voxel-based method with transfer learning from ImageNet to MRI datasets using 3D CNNs considerably improved the results compared with the others.
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Affiliation(s)
- Amir Ebrahimi
- The University of Newcastle, School of Electrical Engineering and Computing, Callaghan, New South Wales, Australia
| | - Suhuai Luo
- The University of Newcastle, School of Electrical Engineering and Computing, Callaghan, New South Wales, Australia
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83
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Borges AF, Laurindo FJ, Spínola MM, Gonçalves RF, Mattos CA. The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2021. [DOI: 10.1016/j.ijinfomgt.2020.102225] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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84
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Abd-alrazaq A, Schneider J, Alhuwail D, Toro CT, Ahmed A, Alajlani M, Househ M. The performance of artificial intelligence-driven technologies in diagnosing mental disorders: An umbrella review (Preprint).. [DOI: 10.2196/preprints.29235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
BACKGROUND
Diagnosing mental disorders is usually not an easy task and requires a large amount of time and effort given the complex nature of mental disorders. Artificial intelligence (AI) has been successfully exploited in diagnosing many mental disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI models in diagnosing different mental disorders.
OBJECTIVE
This umbrella review aims to synthesize results of previous systematic reviews on the performance of AI models in diagnosing mental disorders.
METHODS
To identify relevant systematic reviews, we searched 11 electronic databases, checked the reference list of the included reviews, and checked the reviews that cited the included reviews. Two reviewers independently selected the relevant reviews, extracted the data from them, and appraised their quality. We synthesized the extracted data using the narrative approach. Specifically, results of the included reviews were grouped based on the target mental disorders that the AI classifiers distinguish.
RESULTS
We included 15 systematic reviews of 852 citations identified by searching all databases. The included reviews assessed the performance of AI models in diagnosing Alzheimer’s disease (n=7), mild cognitive impairment (n=6), schizophrenia (n=3), bipolar disease (n=2), autism spectrum disorder (n=1), obsessive-compulsive disorder (n=1), post-traumatic stress disorder (n=1), and psychotic disorders (n=1). The performance of the AI models in diagnosing these mental disorders ranged between 21% and 100%.
CONCLUSIONS
AI technologies offer great promise in diagnosing mental health disorders. The reported performance metrics paint a vivid picture of a bright future for AI in this field. To expedite progress towards these technologies being incorporated into routine practice, we recommend that healthcare professionals in the field cautiously and consciously begin to explore the opportunities of AI-based tools for their daily routine. It would also be encouraging to see a greater number of meta-analyses and further systematic reviews on performance of AI models in diagnosing other common mental disorders such as depression and anxiety.
CLINICALTRIAL
CRD42021231558
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85
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Ursin F, Timmermann C, Steger F. Ethical Implications of Alzheimer's Disease Prediction in Asymptomatic Individuals through Artificial Intelligence. Diagnostics (Basel) 2021; 11:diagnostics11030440. [PMID: 33806501 PMCID: PMC7998766 DOI: 10.3390/diagnostics11030440] [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: 01/05/2021] [Revised: 02/09/2021] [Accepted: 02/25/2021] [Indexed: 11/25/2022] Open
Abstract
Biomarker-based predictive tests for subjectively asymptomatic Alzheimer’s disease (AD) are utilized in research today. Novel applications of artificial intelligence (AI) promise to predict the onset of AD several years in advance without determining biomarker thresholds. Until now, little attention has been paid to the new ethical challenges that AI brings to the early diagnosis in asymptomatic individuals, beyond contributing to research purposes, when we still lack adequate treatment. The aim of this paper is to explore the ethical arguments put forward for AI aided AD prediction in subjectively asymptomatic individuals and their ethical implications. The ethical assessment is based on a systematic literature search. Thematic analysis was conducted inductively of 18 included publications. The ethical framework includes the principles of autonomy, beneficence, non-maleficence, and justice. Reasons for offering predictive tests to asymptomatic individuals are the right to know, a positive balance of the risk-benefit assessment, and the opportunity for future planning. Reasons against are the lack of disease modifying treatment, the accuracy and explicability of AI aided prediction, the right not to know, and threats to social rights. We conclude that there are serious ethical concerns in offering early diagnosis to asymptomatic individuals and the issues raised by the application of AI add to the already known issues. Nevertheless, pre-symptomatic testing should only be offered on request to avoid inflicted harm. We recommend developing training for physicians in communicating AI aided prediction.
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86
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Cheng CT, Wang Y, Chen HW, Hsiao PM, Yeh CN, Hsieh CH, Miao S, Xiao J, Liao CH, Lu L. A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs. Nat Commun 2021; 12:1066. [PMID: 33594071 PMCID: PMC7887334 DOI: 10.1038/s41467-021-21311-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 01/20/2021] [Indexed: 12/13/2022] Open
Abstract
Pelvic radiograph (PXR) is essential for detecting proximal femur and pelvis injuries in trauma patients, which is also the key component for trauma survey. None of the currently available algorithms can accurately detect all kinds of trauma-related radiographic findings on PXRs. Here, we show a universal algorithm can detect most types of trauma-related radiographic findings on PXRs. We develop a multiscale deep learning algorithm called PelviXNet trained with 5204 PXRs with weakly supervised point annotation. PelviXNet yields an area under the receiver operating characteristic curve (AUROC) of 0.973 (95% CI, 0.960-0.983) and an area under the precision-recall curve (AUPRC) of 0.963 (95% CI, 0.948-0.974) in the clinical population test set of 1888 PXRs. The accuracy, sensitivity, and specificity at the cutoff value are 0.924 (95% CI, 0.912-0.936), 0.908 (95% CI, 0.885-0.908), and 0.932 (95% CI, 0.919-0.946), respectively. PelviXNet demonstrates comparable performance with radiologists and orthopedics in detecting pelvic and hip fractures.
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Affiliation(s)
- Chi-Tung Cheng
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan
| | | | - Huan-Wu Chen
- Division of Emergency and Critical Care Radiology, Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Po-Meng Hsiao
- New Taipei Municipal TuCheng Hospital, New Taipei city, Taiwan
| | - Chun-Nan Yeh
- Department of Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan
| | - Chi-Hsun Hsieh
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan
| | | | | | - Chien-Hung Liao
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial hospital, Linkou, Taoyuan, Taiwan.
| | - Le Lu
- PAII Inc, Bethesda, MD, USA
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87
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Quaak M, van de Mortel L, Thomas RM, van Wingen G. Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis. Neuroimage Clin 2021; 30:102584. [PMID: 33677240 PMCID: PMC8209481 DOI: 10.1016/j.nicl.2021.102584] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 01/18/2021] [Accepted: 01/29/2021] [Indexed: 12/20/2022]
Abstract
Deep learning (DL) methods have been increasingly applied to neuroimaging data to identify patients with psychiatric and neurological disorders. This review provides an overview of the different DL applications within psychiatry and compares DL model accuracy to standard machine learning (SML). Fifty-three articles were included for qualitative analysis, primarily investigating autism spectrum disorder (ASD; n = 22), schizophrenia (SZ; n = 22) and attention-deficit/hyperactivity disorder (ADHD; n = 9). Thirty-two of the thirty-five studies that directly compared DL to SML reported a higher accuracy for DL. Only sixteen studies could be included in a meta-regression to quantitatively compare DL and SML performance. This showed a higher odds ratio for DL models, though the comparison attained significance only for ASD. Our results suggest that deep learning of neuroimaging data is a promising tool for the classification of individual psychiatric patients. However, it is not yet used to its full potential: most studies use pre-engineered features, whereas one of the main advantages of DL is its ability to learn representations of minimally processed data. Our current evaluation is limited by minimal reporting of performance measures to enable quantitative comparisons, and the restriction to ADHD, SZ and ASD as current research focusses on large publicly available datasets. To truly uncover the added value of DL, we need carefully designed comparisons of SML and DL models which are yet rarely performed.
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Affiliation(s)
- Mirjam Quaak
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands
| | - Laurens van de Mortel
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands
| | - Rajat Mani Thomas
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands
| | - Guido van Wingen
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands.
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88
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Zhang X, Yang Y, Li T, Zhang Y, Wang H, Fujita H. CMC: A consensus multi-view clustering model for predicting Alzheimer's disease progression. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 199:105895. [PMID: 33341477 DOI: 10.1016/j.cmpb.2020.105895] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 11/29/2020] [Indexed: 06/12/2023]
Abstract
Machine learning has been used in the past for the auxiliary diagnosis of Alzheimer's Disease (AD). However, most existing technologies only explore single-view data, require manual parameter setting and focus on two-class (i.e., dementia or not) classification problems. Unlike single-view data, multi-view data provide more powerful feature representation capability. Learning with multi-view data is referred to as multi-view learning, which has received certain attention in recent years. In this paper, we propose a new multi-view clustering model called Consensus Multi-view Clustering (CMC) based on nonnegative matrix factorization for predicting the multiple stages of AD progression. The proposed CMC performs multi-view learning idea to fully capture data features with limited medical images, approaches similarity relations between different entities, addresses the shortcoming from multi-view fusion that requires manual setting parameters, and further acquires a consensus representation containing shared features and complementary knowledge of multiple view data. It not only can improve the predication performance of AD, but also can screen and classify the symptoms of different AD's phases. Experimental results using data with twelve views constructed by brain Magnetic Resonance Imaging (MRI) database from Alzheimer's Disease Neuroimaging Initiative expound and prove the effectiveness of the proposed model.
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Affiliation(s)
- Xiaobo Zhang
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China; Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China
| | - Yan Yang
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China; Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China.
| | - Tianrui Li
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China; Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China
| | - Yiling Zhang
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China; Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China
| | - Hao Wang
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China; Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China
| | - Hamido Fujita
- Faculty of Software and Information Science, Iwate Prefectural University, Iwate, Japan
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89
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Predicting the Potency of Anti-Alzheimer’s Drug Combinations Using Machine Learning. Processes (Basel) 2021. [DOI: 10.3390/pr9020264] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Clinical trials of single drugs intended to slow the progression of Alzheimer’s Disease (AD) have been notoriously unsuccessful. Combinations of repurposed drugs could provide effective treatments for AD. The challenge is to identify potentially effective combinations. To meet this challenge, machine learning (ML) was used to extract the knowledge from two leading AD databases, and then “the machine” predicted which combinations of the drugs in common between the two databases would be the most effective as treatments for AD. Specifically, three-layered artificial neural networks (ANNs) with compound, gated units in their internal layer were trained using ML to predict the cognitive scores of participants, separately in either database, given other data fields including age, demographic variables, comorbidities, and drugs taken. The predictions from the separately trained ANNs were statistically highly significantly correlated. The best drug combinations, jointly determined from both sets of predictions, were high in nonsteroidal anti-inflammatory drugs; anticoagulant, lipid-lowering, and antihypertensive drugs; and female hormones. The results suggest that the neurodegenerative processes that underlie AD and other dementias could be effectively treated using a combination of repurposed drugs. Predicted drug combinations could be evaluated in clinical trials.
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90
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Ahmed S, Kim BC, Lee KH, Jung HY. Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging. PLoS One 2020; 15:e0242712. [PMID: 33290403 PMCID: PMC7723284 DOI: 10.1371/journal.pone.0242712] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 11/07/2020] [Indexed: 11/26/2022] Open
Abstract
Patches from three orthogonal views of selected cerebral regions can be utilized to learn convolutional neural network (CNN) models for staging the Alzheimer disease (AD) spectrum including preclinical AD, mild cognitive impairment due to AD, and dementia due to AD and normal controls. Hippocampi, amygdalae and insulae were selected from the volumetric analysis of structured magnetic resonance images (MRIs). Three-view patches (TVPs) from these regions were fed to the CNN for training. MRIs were classified with the SoftMax-normalized scores of individual model predictions on TVPs. The significance of each region of interest (ROI) for staging the AD spectrum was evaluated and reported. The results of the ensemble classifier are compared with state-of-the-art methods using the same evaluation metrics. Patch-based ROI ensembles provide comparable diagnostic performance for AD staging. In this work, TVP-based ROI analysis using a CNN provides informative landmarks in cerebral MRIs and may have significance in clinical studies and computer-aided diagnosis system design.
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Affiliation(s)
- Samsuddin Ahmed
- Department of Computer Engineering, Chosun University, Gwangju, South Korea
| | - Byeong C. Kim
- Gwangju Alzheimer’s disease and Related Dementias Cohort Research Center, Chosun University, Gwangju, Korea
- Department of Neurology, Chonnam National University Medical School, Gwangju, South Korea
| | - Kun Ho Lee
- Gwangju Alzheimer’s disease and Related Dementias Cohort Research Center, Chosun University, Gwangju, Korea
- Department of Biomedical Science, Chosun University, Gwangju, South Korea
- Korea Brain Research Institute, Daegu, Korea
| | - Ho Yub Jung
- Department of Computer Engineering, Chosun University, Gwangju, South Korea
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91
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Almubark I, Chang LC, Shattuck KF, Nguyen T, Turner RS, Jiang X. A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease. Front Aging Neurosci 2020; 12:603179. [PMID: 33343337 PMCID: PMC7744695 DOI: 10.3389/fnagi.2020.603179] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Accepted: 11/13/2020] [Indexed: 12/15/2022] Open
Abstract
Introduction: The goal of this study was to investigate and compare the classification performance of machine learning with behavioral data from standard neuropsychological tests, a cognitive task, or both. Methods: A neuropsychological battery and a simple 5-min cognitive task were administered to eight individuals with mild cognitive impairment (MCI), eight individuals with mild Alzheimer's disease (AD), and 41 demographically match controls (CN). A fully connected multilayer perceptron (MLP) network and four supervised traditional machine learning algorithms were used. Results: Traditional machine learning algorithms achieved similar classification performances with neuropsychological or cognitive data. MLP outperformed traditional algorithms with the cognitive data (either alone or together with neuropsychological data), but not neuropsychological data. In particularly, MLP with a combination of summarized scores from neuropsychological tests and the cognitive task achieved ~90% sensitivity and ~90% specificity. Applying the models to an independent dataset, in which the participants were demographically different from the ones in the main dataset, a high specificity was maintained (100%), but the sensitivity was dropped to 66.67%. Discussion: Deep learning with data from specific cognitive task(s) holds promise for assisting in the early diagnosis of Alzheimer's disease, but future work with a large and diverse sample is necessary to validate and to improve this approach.
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Affiliation(s)
- Ibrahim Almubark
- Department of Electrical Engineering and Computer Science, Catholic University of America, Washington, DC, United States
| | - Lin-Ching Chang
- Department of Electrical Engineering and Computer Science, Catholic University of America, Washington, DC, United States
| | - Kyle F Shattuck
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC, United States
| | - Thanh Nguyen
- Department of Electrical Engineering and Computer Science, Catholic University of America, Washington, DC, United States
| | - Raymond Scott Turner
- Department of Neurology, Georgetown University Medical Center, Washington, DC, United States
| | - Xiong Jiang
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC, United States
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92
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Jiang H, Cao P, Xu M, Yang J, Zaiane O. Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction. Comput Biol Med 2020; 127:104096. [DOI: 10.1016/j.compbiomed.2020.104096] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/24/2020] [Accepted: 10/24/2020] [Indexed: 02/06/2023]
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93
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Multi-class diagnosis of Alzheimer's disease using cascaded three dimensional-convolutional neural network. Phys Eng Sci Med 2020; 43:1219-1228. [PMID: 32926392 DOI: 10.1007/s13246-020-00924-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 09/03/2020] [Indexed: 12/13/2022]
Abstract
Dementia is a social problem in the aging society of advanced countries. Presently, 46.8 million people affected with dementia worldwide, and it may increase to 130 million by 2050. Alzheimer's disease (AD) is the most common form of dementia. The cost of care for AD patients in 2015 was 818 billion US dollars and is expected to increase intensely due to the increasing number of patients due to the aging society. It isn't easy to cure AD, but early detection is crucial. This paper proposes a multi-class classification of AD, mild cognitive impairment (MCI), and normal control (NC) subjects using three dimensional-convolutional neural network with Support Vector Machine classifier. A cross-sectional study on structural MRI data of 465 subjects, including 132 AD patients, 181 MCI, and 152 NC, is performed in this paper. The highly complex and spatial atrophy patterns of the brain related to Alzheimer's Disease and MCI are extracted from structural MRI images using cascaded layers of the three dimensional convolutional neural network. The hectic process of segmentation and further extraction of handcrafted features is eliminated. The complete image is considered for the processing, thus incorporating every region of the brain for the classification. The features extracted using four cascaded layers of three dimensional-convolutional neural network are fed into the Support Vector Machine classifier. The proposed method achieved 97.77% accuracy which outperforms state of the art, and this algorithm is a promising indicator for the diagnosis of AD.
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94
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Bringas S, Salomón S, Duque R, Lage C, Montaña JL. Alzheimer's Disease stage identification using deep learning models. J Biomed Inform 2020; 109:103514. [PMID: 32711124 DOI: 10.1016/j.jbi.2020.103514] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 07/15/2020] [Accepted: 07/16/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVE The aim of this research is to identify the stage of Alzheimer's Disease (AD) patients through the use of mobility data and deep learning models. This process facilitates the monitoring of the disease and allows actions to be taken in order to provide the optimal treatment and the prevention of complications. MATERIALS AND METHODS We employed data from 35 patients with AD collected by smartphones for a week in a daycare center. The data sequences of each patient recorded the accelerometer changes while daily activities were performed and they were labeled with the stage of the disease (early, middle or late). Our methodology processes these time series and uses a Convolutional Neural Network (CNN) model to recognize the patterns that identify each stage. RESULTS The CNN-based method achieved a 90.91% accuracy and an F1-score of 0.897, greatly improving the results obtained by the traditional feature-based classifiers. DISCUSSION AND CONCLUSION In our research, we show that mobility data can be a valuable resource for the treatment of patients with AD as well as to study the progress of the disease. The use of our CNN-based method improves the accuracy of the identification of AD stages in comparison to common supervised learning models.
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Affiliation(s)
- Santos Bringas
- Fundación Centro Tecnológico de Componentes CTC, 39011 Santander, Spain.
| | | | - Rafael Duque
- Department of Mathematics, Statistics and Computer Science, Universidad de Cantabria, 39005 Santander, Spain.
| | - Carmen Lage
- Cognitive Disorders Unit, Department of Neurology, Marqués de Valdecilla University Hospital (HUMV), Valdecilla Biomedical Research Institute (IDIVAL), 39008 Santander, Spain.
| | - José Luis Montaña
- Department of Mathematics, Statistics and Computer Science, Universidad de Cantabria, 39005 Santander, Spain.
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95
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Automated detection of Alzheimer's disease using bi-directional empirical model decomposition. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2020.03.014] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Moreira R, Teles A, Fialho R, Dos Santos TCP, Vasconcelos SS, de Sá IC, Bastos VH, Silva F, Teixeira S. Can human posture and range of motion be measured automatically by smart mobile applications? Med Hypotheses 2020; 142:109741. [PMID: 32344284 DOI: 10.1016/j.mehy.2020.109741] [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] [Received: 02/22/2020] [Revised: 03/23/2020] [Accepted: 04/11/2020] [Indexed: 12/25/2022]
Abstract
Human posture and Range of Motion (ROM) are important components of a physical assessment and, from the collected data, it is possible to identify postural deviations such as scoliosis or joint and muscle limitations, hence identifying risks of more serious injuries. Posture assessment and ROM measures are also necessary metrics to monitor the effect of treatments used in the motor rehabilitation of patients, as well as to monitor their clinical progress. These evaluation processes are more frequently performed through visual inspection and manual palpation, which are simple and low cost methods. These methods, however, can be optimized with the use of tools such as photogrammetry and goniometry. Mobile solutions have also been developed to help health professionals to capture more objective data and with less risk of bias. Although there are already several systems proposed for assessing human posture and ROM in the literature, they have not been able to automatically identify and mark Anatomical and Segment Points (ASPs). The hypothesis presented here considers the development of a mobile application for automatic identification of ASPs by using machine learning algorithms and computer vision models associated with technologies embedded in smartphones. From ASPs identification, it will be possible to identify changes in postural alignment and ROM. In this context, our view is that an application derived from the hypothesis will serve as an additional tool to assist in the physical assessment process and, consequently, in the diagnosis of disorders related to postural and movement changes.
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Affiliation(s)
- Rayele Moreira
- Federal University of Piauí, Parnaíba, PI, Brazil; University Center Inta - UNINTA, Sobral, CE, Brazil.
| | - Ariel Teles
- Federal University of Piauí, Parnaíba, PI, Brazil; Federal Institute of Maranhão, Araioses, MA, Brazil; Federal University of Maranhão, São Luís, MA, Brazil.
| | - Renan Fialho
- Federal University of Piauí, Parnaíba, PI, Brazil
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