1
|
Laffafchi S, Ebrahimi A, Kafan S. Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data. Health Inf Sci Syst 2024; 12:17. [PMID: 38464464 PMCID: PMC10917730 DOI: 10.1007/s13755-024-00276-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 01/17/2024] [Indexed: 03/12/2024] Open
Abstract
Pulmonary Embolism (PE) is a life-threatening clinical disease with no specific clinical symptoms and Computed Tomography Angiography (CTA) is used for diagnosis. Clinical decision support scoring systems like Wells and rGeneva based on PE risk factors have been developed to estimate the pre-test probability but are underused, leading to continuous overuse of CTA imaging. This diagnostic study aimed to propose a novel approach for efficient management of PE diagnosis using a two-step interconnected machine learning framework directly by analyzing patients' Electronic Health Records data. First, we performed feature importance analysis according to the result of LightGBM superiority for PE prediction, then four state-of-the-art machine learning methods were applied for PE prediction based on the feature importance results, enabling swift and accurate pre-test diagnosis. Throughout the study patients' data from different departments were collected from Sina educational hospital, affiliated with the Tehran University of medical sciences in Iran. Generally, the Ridge classification method obtained the best performance with an F1 score of 0.96. Extensive experimental findings showed the effectiveness and simplicity of this diagnostic process of PE in comparison with the existing scoring systems. The main strength of this approach centered on PE disease management procedures, which would reduce avoidable invasive CTA imaging and be applied as a primary prognosis of PE, hence assisting the healthcare system, clinicians, and patients by reducing costs and promoting treatment quality and patient satisfaction.
Collapse
Affiliation(s)
- Soroor Laffafchi
- Department of Business Administration and Entrepreneurship, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran
| | - Ahmad Ebrahimi
- Department of Industrial and Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran
| | - Samira Kafan
- Department of Pulmonary Medicine, Sina Hospital, International Relations Office, Medical School, Tehran University of Medical Sciences, PourSina St., Tehran, 1417613151 Iran
| |
Collapse
|
2
|
Shaffi N, Subramanian K, Vimbi V, Hajamohideen F, Abdesselam A, Mahmud M. Performance Evaluation of Deep, Shallow and Ensemble Machine Learning Methods for the Automated Classification of Alzheimer's Disease. Int J Neural Syst 2024; 34:2450029. [PMID: 38576308 DOI: 10.1142/s0129065724500291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
Artificial intelligence (AI)-based approaches are crucial in computer-aided diagnosis (CAD) for various medical applications. Their ability to quickly and accurately learn from complex data is remarkable. Deep learning (DL) models have shown promising results in accurately classifying Alzheimer's disease (AD) and its related cognitive states, Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), along with the healthy conditions known as Cognitively Normal (CN). This offers valuable insights into disease progression and diagnosis. However, certain traditional machine learning (ML) classifiers perform equally well or even better than DL models, requiring less training data. This is particularly valuable in CAD in situations with limited labeled datasets. In this paper, we propose an ensemble classifier based on ML models for magnetic resonance imaging (MRI) data, which achieved an impressive accuracy of 96.52%. This represents a 3-5% improvement over the best individual classifier. We evaluated popular ML classifiers for AD classification under both data-scarce and data-rich conditions using the Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies datasets. By comparing the results to state-of-the-art CNN-centric DL algorithms, we gain insights into the strengths and weaknesses of each approach. This work will help users to select the most suitable algorithm for AD classification based on data availability.
Collapse
Affiliation(s)
- Noushath Shaffi
- College of Computing and Information Sciences, University of Technology and Applied Sciences, P.O. Box: 135, Suhar 311, Sultanate of Oman, Oman
| | - Karthikeyan Subramanian
- College of Computing and Information Sciences, University of Technology and Applied Sciences, P.O. Box: 135, Suhar 311, Sultanate of Oman, Oman
| | - Viswan Vimbi
- College of Computing and Information Sciences, University of Technology and Applied Sciences, P.O. Box: 135, Suhar 311, Sultanate of Oman, Oman
| | - Faizal Hajamohideen
- College of Computing and Information Sciences, University of Technology and Applied Sciences, P.O. Box: 135, Suhar 311, Sultanate of Oman, Oman
| | - Abdelhamid Abdesselam
- Department of Computer Science, College of Science, Sultan Qaboos University, P.O. Box: 36, Al-Khod 123, Sultanate of Oman, Oman
| | - Mufti Mahmud
- Department of Computer Science, Medical Technologies Innovation Facility and Centre for Computer Science and Informatics (CIRC), Nottingham Trent University, Nottingham NG11 8NS, UK
| |
Collapse
|
3
|
Zhang Z, Gao L, Li P, Jin G, Wang J. DAUF: A disease-related attentional UNet framework for progressive and stable mild cognitive impairment identification. Comput Biol Med 2023; 165:107401. [PMID: 37678136 DOI: 10.1016/j.compbiomed.2023.107401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 08/02/2023] [Accepted: 08/26/2023] [Indexed: 09/09/2023]
Abstract
Identifying progressive mild cognitive impairment (pMCI) and stable mild cognitive impairment (sMCI) plays a significant role in early Alzheimer's disease (AD) diagnosis, which can effectively boost the life quality of patients. Recently, convolutional neural network (CNN)- based methods using structural magnetic resonance imaging (sMRI) images have shown effective for AD identification. However, these CNN-based methods fail to effectively explore the feature extraction of disease-related multi-scale tissues, such as ventricles, hippocampi and cerebral cortex. To address this issue, we propose an end-to-end disease-related attentional UNet framework (DAUF) for identifying pMCI and sMCI, by embedding a devised dual disease-related attention module (D2AM) and a novel tree-structured feature fusion classifier (TFFC). Specifically, D2AM leverages the complementarity between feature maps and attention maps and the complementary features from the encoder and decoder, so as to highlight discriminative semantic and detailed features. Additionally, TFFC is a powerfully joint multi-scale feature fusion and classification head, by employing the homogeneity among multi-scale features, so that the discriminative features of the multi-scale tissues are adequately fused for enhancing classification performance. Finally, extensive experiments demonstrate the superior performance of DAUF, with the effectiveness of D2AM and TFFC on identifying pMCI and sMCI subjects.
Collapse
Affiliation(s)
- Zhehao Zhang
- First Affiliated Hospital of Ningbo University, Ningbo, 315020, China; Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315210, China
| | - Linlin Gao
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315210, China; Zhejiang Key Laboratory of Mobile Network Application Technology, Ningbo University, Ningbo 315210, China; Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo 315210, China.
| | - Pengyuan Li
- IBM Research-Almaden, San Jose, CA 95120, USA
| | - Guang Jin
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315210, China
| | - Jianhua Wang
- First Affiliated Hospital of Ningbo University, Ningbo, 315020, China.
| |
Collapse
|
4
|
Tang C, Wei M, Sun J, Wang S, Zhang Y. CsAGP: Detecting Alzheimer's disease from multimodal images via dual-transformer with cross-attention and graph pooling. JOURNAL OF KING SAUD UNIVERSITY. COMPUTER AND INFORMATION SCIENCES 2023; 35:101618. [PMID: 38559705 PMCID: PMC7615783 DOI: 10.1016/j.jksuci.2023.101618] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Alzheimer's disease (AD) is a terrible and degenerative disease commonly occurring in the elderly. Early detection can prevent patients from further damage, which is crucial in treating AD. Over the past few decades, it has been demonstrated that neuroimaging can be a critical diagnostic tool for AD, and the feature fusion of different neuroimaging modalities can enhance diagnostic performance. Most previous studies in multimodal feature fusion have only concatenated the high-level features extracted by neural networks from various neuroimaging images simply. However, a major problem of these studies is over-looking the low-level feature interactions between modalities in the feature extraction stage, resulting in suboptimal performance in AD diagnosis. In this paper, we develop a dual-branch vision transformer with cross-attention and graph pooling, namely CsAGP, which enables multi-level feature interactions between the inputs to learn a shared feature representation. Specifically, we first construct a brand-new cross-attention fusion module (CAFM), which processes MRI and PET images by two independent branches of differing computational complexity. These features are fused merely by the cross-attention mechanism to enhance each other. After that, a concise graph pooling algorithm-based Reshape-Pooling-Reshape (RPR) framework is developed for token selection to reduce token redundancy in the proposed model. Extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrated that the suggested method obtains 99.04%, 97.43%, 98.57%, and 98.72% accuracy for the classification of AD vs. CN, AD vs. MCI, CN vs. MCI, and AD vs. CN vs. MCI, respectively.
Collapse
Affiliation(s)
- Chaosheng Tang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, PR China
| | - Mingyang Wei
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, PR China
| | - Junding Sun
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, PR China
| | - Shuihua Wang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, PR China
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Yudong Zhang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, PR China
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | | |
Collapse
|
5
|
Agarwal D, Berbís MÁ, Luna A, Lipari V, Ballester JB, de la Torre-Díez I. Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network. J Med Syst 2023; 47:57. [PMID: 37129723 PMCID: PMC10154284 DOI: 10.1007/s10916-023-01941-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 03/20/2023] [Indexed: 05/03/2023]
Abstract
Alzheimer's disease (AD) poses an enormous challenge to modern healthcare. Since 2017, researchers have been using deep learning (DL) models for the early detection of AD using neuroimaging biomarkers. In this paper, we implement the EfficietNet-b0 convolutional neural network (CNN) with a novel approach-"fusion of end-to-end and transfer learning"-to classify different stages of AD. 245 T1W MRI scans of cognitively normal (CN) subjects, 229 scans of AD subjects, and 229 scans of subjects with stable mild cognitive impairment (sMCI) were employed. Each scan was preprocessed using a standard pipeline. The proposed models were trained and evaluated using preprocessed scans. For the sMCI vs. AD classification task we obtained 95.29% accuracy and 95.35% area under the curve (AUC) for model training and 93.10% accuracy and 93.00% AUC for model testing. For the multiclass AD vs. CN vs. sMCI classification task we obtained 85.66% accuracy and 86% AUC for model training and 87.38% accuracy and 88.00% AUC for model testing. Based on our experimental results, we conclude that CNN-based DL models can be used to analyze complicated MRI scan features in clinical settings.
Collapse
Affiliation(s)
- Deevyankar Agarwal
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011, Valladolid, Spain.
| | | | - Antonio Luna
- MRI Unit, Radiology Department, HT Médica, Carmelo Torres No. 2, 23007, Jaén, Spain
| | - Vivian Lipari
- European Atlantic University, Isabel Torres 21, 39011, Santander, Spain
| | | | - Isabel de la Torre-Díez
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011, Valladolid, Spain
| |
Collapse
|
6
|
Abbas Q, Hussain A, Baig AR. CAD-ALZ: A Blockwise Fine-Tuning Strategy on Convolutional Model and Random Forest Classifier for Recognition of Multistage Alzheimer's Disease. Diagnostics (Basel) 2023; 13:167. [PMID: 36611459 PMCID: PMC9818479 DOI: 10.3390/diagnostics13010167] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/24/2022] [Accepted: 12/31/2022] [Indexed: 01/05/2023] Open
Abstract
Mental deterioration or Alzheimer's (ALZ) disease is progressive and causes both physical and mental dependency. There is a need for a computer-aided diagnosis (CAD) system that can help doctors make an immediate decision. (1) Background: Currently, CAD systems are developed based on hand-crafted features, machine learning (ML), and deep learning (DL) techniques. Those CAD systems frequently require domain-expert knowledge and massive datasets to extract deep features or model training, which causes problems with class imbalance and overfitting. Additionally, there are still manual approaches used by radiologists due to the lack of dataset availability and to train the model with cost-effective computation. Existing works rely on performance improvement by neglecting the problems of the limited dataset, high computational complexity, and unavailability of lightweight and efficient feature descriptors. (2) Methods: To address these issues, a new approach, CAD-ALZ, is developed by extracting deep features through a ConvMixer layer with a blockwise fine-tuning strategy on a very small original dataset. At first, we apply the data augmentation method to images to increase the size of datasets. In this study, a blockwise fine-tuning strategy is employed on the ConvMixer model to detect robust features. Afterwards, a random forest (RF) is used to classify ALZ disease stages. (3) Results: The proposed CAD-ALZ model obtained significant results by using six evaluation metrics such as the F1-score, Kappa, accuracy, precision, sensitivity, and specificity. The CAD-ALZ model performed with a sensitivity of 99.69% and an F1-score of 99.61%. (4) Conclusions: The suggested CAD-ALZ approach is a potential technique for clinical use and computational efficiency compared to state-of-the-art approaches. The CAD-ALZ model code is freely available on GitHub for the scientific community.
Collapse
Affiliation(s)
- Qaisar Abbas
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
| | - Ayyaz Hussain
- Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan
| | - Abdul Rauf Baig
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
| |
Collapse
|
7
|
Li M, Jiang Y, Li X, Yin S, Luo H. Ensemble of convolutional neural networks and multilayer perceptron for the diagnosis of mild cognitive impairment and Alzheimer's disease. Med Phys 2023; 50:209-225. [PMID: 36121183 DOI: 10.1002/mp.15985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 08/09/2022] [Accepted: 09/07/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Structural magnetic resonance imaging (sMRI) can provide morphological information about the structure and function of the brain in the same scanning process. It has been widely used in the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). PURPOSE To capture the anatomical changes in the brain caused by AD/MCI, deep learning-based MRI image analysis methods have been proposed in recent years. However, it is observed that the performance of most existing methods is limited as they only construct a single type of deep network and ignore the significance of other clinical information. METHODS To make up for these defects, an ensemble framework that incorporates three types of dedicatedly-designed convolutional neural networks (CNNs) and a multilayer perceptron (MLP) network is proposed, where three CNNs with entropy-based multi-instance learning pooling layers have more reliable feature selection abilities. The dedicatedly-designed base classifiers can make use of the heterogeneous data, and empower the framework with enhanced diversity and robustness. In particular, to consider the interactions among the base classifiers, a novel multi-head self-attention voting scheme is designed. Moreover, considering the chance that MCI can be transformed to AD, the proposed framework is designed to diagnose AD and predict MCI conversion simultaneously, with the aid of the transfer learning technique. RESULTS For performance evaluation and comparison, extensive experiments are conducted on the public dataset of the Alzheimer's Disease Neuroimaging Initiative (ADNI). The results show that the proposed ensemble framework provides superior performance under most of the evaluation metrics. Especially, the proposed framework achieves state-of-the-art diagnostic accuracy (98.61% for the AD diagnosis task, and 84.49% for the MCI conversion prediction task). CONCLUSIONS These promising results demonstrate the proposed ensemble framework can accurately diagnose AD patients and predict the conversion of MCI patients, which has the potential of clinical practice for diagnosing AD and MCI.
Collapse
Affiliation(s)
- Minglei Li
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Yuchen Jiang
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Xiang Li
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Shen Yin
- Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway
| | - Hao Luo
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China
| |
Collapse
|
8
|
Ban Y, Zhang X, Lao H. Diagnosis of Alzheimer's Disease using Structure Highlighting Key Slice Stacking and Transfer Learning. Med Phys 2022; 49:5855-5869. [PMID: 35894542 DOI: 10.1002/mp.15888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 06/16/2022] [Accepted: 07/23/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND In recent years, two-dimensional convolutional neural network (2D CNN) have been widely used in the diagnosis of Alzheimer's disease (AD) based on structural magnetic resonance imaging (sMRI). However, due to the lack of targeted processing of the key slices of sMRI images, the classification performance of the CNN model needs to be improved. PURPOSE Therefore, in this paper, we propose a key slice processing technique called the structural highlighting key slice stacking (SHKSS) technique, and we apply it to a 2D transfer learning model for AD classification. METHODS Specifically, first, 3D MR images were preprocessed. Second, the 2D axial middle-layer image was extracted from the MR image as a key slice. Then, the image was normalized by intensity and mapped to the RGB space, and histogram specification was performed on the obtained RGB image to generate the final three-channel image. The final three-channel image was input into a pre-trained CNN model for AD classification. Finally, classification and generalization experiments were conducted to verify the validity of the proposed method. RESULTS The experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that our SHKSS method can effectively highlight the structural information in MRI slices. Compared with existing key slice processing techniques, our SHKSS method has an average accuracy improvement of at least 26% on the same test dataset, and it has better performance and generalization ability. CONCLUSIONS Our SHKSS method not only converts single-channel images into three-channel images to match the input requirements of the 2D transfer learning model but also highlights the structural information of MRI slices to improve the accuracy of AD diagnosis. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Yanjiao Ban
- School of Computer, Electronics and Information, Guangxi University, Nanning, Guangxi, 530004, PR China
| | - Xuejun Zhang
- School of Computer, Electronics and Information, Guangxi University, Nanning, Guangxi, 530004, PR China.,School of Artificial Intelligence, Guangxi Minzu University, Guangxi, 530006, PR China.,Guangxi Key Laboratory of Multimedia Communications and Network Technology, Nanning, Guangxi, 530004, PR China
| | - Huan Lao
- School of Artificial Intelligence, Guangxi Minzu University, Guangxi, 530006, PR China
| |
Collapse
|
9
|
End-to-End Deep Learning Architectures Using 3D Neuroimaging Biomarkers for Early Alzheimer’s Diagnosis. MATHEMATICS 2022. [DOI: 10.3390/math10152575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
This study uses magnetic resonance imaging (MRI) data to propose end-to-end learning implementing volumetric convolutional neural network (CNN) models for two binary classification tasks: Alzheimer’s disease (AD) vs. cognitively normal (CN) and stable mild cognitive impairment (sMCI) vs. AD. The baseline MP-RAGE T1 MR images of 245 AD patients and 229 with sMCI were obtained from the ADNI dataset, whereas 245 T1 MR images of CN people were obtained from the IXI dataset. All of the images were preprocessed in four steps: N4 bias field correction, denoising, brain extraction, and registration. End-to-end-learning-based deep CNNs were used to discern between different phases of AD. Eight CNN-based architectures were implemented and assessed. The DenseNet264 excelled in both types of classification, with 82.5% accuracy and 87.63% AUC for training and 81.03% accuracy for testing relating to the sMCI vs. AD and 100% accuracy and 100% AUC for training and 99.56% accuracy for testing relating to the AD vs. CN. Deep learning approaches based on CNN and end-to-end learning offer a strong tool for examining minute but complex properties in MR images which could aid in the early detection and prediction of Alzheimer’s disease in clinical settings.
Collapse
|
10
|
Pereira P, Silveira M. Cross-Modal Transfer Learning Methods 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 2022; 2022:3789-3792. [PMID: 36083922 DOI: 10.1109/embc48229.2022.9871163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In this paper we propose cross-modal transfer learning for Alzheimer's disease detection. We use positron emission tomography (PET) and magnetic resonance imaging (MRI) brain scans from ADNI to train convolutional neural networks (CNNs) on one modality and fine-tune it on the other modality. We start by showing that cross-modal transfer learning approaches outperform CNNs trained from scratch on a single modality. We then show that cross-modal transfer-learning also outperforms multimodal approaches using the same data.
Collapse
|
11
|
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.
Collapse
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
| | | |
Collapse
|
12
|
Diagnosis of Alzheimer's disease based on regional attention with sMRI gray matter slices. J Neurosci Methods 2022; 365:109376. [PMID: 34627926 DOI: 10.1016/j.jneumeth.2021.109376] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 09/30/2021] [Accepted: 10/01/2021] [Indexed: 01/11/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common symptom of aggressive and irreversible dementia that affects people's ability of daily life. At present, neuroimaging technology plays an important role in the evaluation and early diagnosis of AD. With the widespread application of artificial intelligence in the medical field, deep learning has shown great potential in computer-aided AD diagnosis based on MRI. NEW METHOD In this study, we proposed a deep learning framework based on sMRI gray matter slice for AD diagnosis. Compared with the previous methods based on deep learning, our method enhanced gray matter feature information more effectively by combination of slice region and attention mechanism, which can improve the accuracy on the AD diagnosis. RESULTS To ensure the performance of our proposed method, the experiment was evaluated on T1 weighted structural MRI (sMRI) images with non-leakage splitting from the ADNI database. Our method can achieve 0.90 accuracy in classification of AD/NC and 0.825 accuracy in classification of AD/MCI, which has better diagnostic performance and advantages than other competitive single-modality methods based on sMRI. Furthermore, we indicated the most discriminative brain MRI slice area determined for AD diagnosis. COMPARISON WITH EXISTING METHODS Our proposed method based on the regional attention with GM slice has a 1%-8% improvement in accuracy compared with several state-of-the-art methods for AD diagnosis. CONCLUSIONS The results of experiment indicate that our method can focus more effective features in the gray matter of coronal slices and to achieve a more accurate diagnosis of Alzheimer's disease. This study can provide a more remarkably effective approach and more objective evaluation for the diagnosis of AD based on sMRI slice images.
Collapse
|
13
|
Yagis E, Atnafu SW, García Seco de Herrera A, Marzi C, Scheda R, Giannelli M, Tessa C, Citi L, Diciotti S. Effect of data leakage in brain MRI classification using 2D convolutional neural networks. Sci Rep 2021; 11:22544. [PMID: 34799630 PMCID: PMC8604922 DOI: 10.1038/s41598-021-01681-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 10/28/2021] [Indexed: 11/10/2022] Open
Abstract
In recent years, 2D convolutional neural networks (CNNs) have been extensively used to diagnose neurological diseases from magnetic resonance imaging (MRI) data due to their potential to discern subtle and intricate patterns. Despite the high performances reported in numerous studies, developing CNN models with good generalization abilities is still a challenging task due to possible data leakage introduced during cross-validation (CV). In this study, we quantitatively assessed the effect of a data leakage caused by 3D MRI data splitting based on a 2D slice-level using three 2D CNN models to classify patients with Alzheimer's disease (AD) and Parkinson's disease (PD). Our experiments showed that slice-level CV erroneously boosted the average slice level accuracy on the test set by 30% on Open Access Series of Imaging Studies (OASIS), 29% on Alzheimer's Disease Neuroimaging Initiative (ADNI), 48% on Parkinson's Progression Markers Initiative (PPMI) and 55% on a local de-novo PD Versilia dataset. Further tests on a randomly labeled OASIS-derived dataset produced about 96% of (erroneous) accuracy (slice-level split) and 50% accuracy (subject-level split), as expected from a randomized experiment. Overall, the extent of the effect of an erroneous slice-based CV is severe, especially for small datasets.
Collapse
Affiliation(s)
- Ekin Yagis
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Selamawet Workalemahu Atnafu
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Via dell'Università 50, 47521, Cesena, Italy
| | | | - Chiara Marzi
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Via dell'Università 50, 47521, Cesena, Italy
| | - Riccardo Scheda
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Via dell'Università 50, 47521, Cesena, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Pisa, Italy
| | - Carlo Tessa
- Division of Radiology, Versilia Hospital, Azienda USL Toscana Nord Ovest, Lido di Camaiore, LU, Italy
| | - Luca Citi
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Via dell'Università 50, 47521, Cesena, Italy.
| |
Collapse
|
14
|
Zhou Y, Song Z, Han X, Li H, Tang X. Prediction of Alzheimer's Disease Progression Based on Magnetic Resonance Imaging. ACS Chem Neurosci 2021; 12:4209-4223. [PMID: 34723463 DOI: 10.1021/acschemneuro.1c00472] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The neuroimaging method of multimodal magnetic resonance imaging (MRI) can identify the changes in brain structure and function caused by Alzheimer's disease (AD) at different stages, and it is a practical method to study the mechanism of AD progression. This paper reviews the studies of methods and biomarkers for predicting AD progression based on multimodal MRI. First, different approaches for predicting AD progression are analyzed and summarized, including machine learning, deep learning, regression, and other MRI analysis methods. Then, the effective biomarkers of AD progression under structural magnetic resonance imaging, diffusion tensor imaging, functional magnetic resonance imaging, and arterial spin labeling modes of MRI are summarized. It is believed that the brain changes shown on MRI may be related to the cognitive decline in different prodrome stages of AD, which is conducive to the further realization of early intervention and prevention of AD. Finally, the deficiencies of the existing studies are analyzed in terms of data set size, data heterogeneity, processing methods, and research depth. More importantly, future research directions are proposed, including enriching data sets, simplifying biomarkers, utilizing multimodal magnetic resonance, etc. In the future, the study of AD progression by multimodal MRI will still be a challenge but also a significant research hotspot.
Collapse
Affiliation(s)
- Ying Zhou
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, P.R. China
| | - Zeyu Song
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, P.R. China
| | - Xiao Han
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, P.R. China
| | - Hanjun Li
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, P.R. China
| | - Xiaoying Tang
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, P.R. China
| |
Collapse
|
15
|
Agarwal D, Marques G, de la Torre-Díez I, Franco Martin MA, García Zapiraín B, Martín Rodríguez F. Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:7259. [PMID: 34770565 PMCID: PMC8587338 DOI: 10.3390/s21217259] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 10/27/2021] [Accepted: 10/27/2021] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.
Collapse
Affiliation(s)
- Deevyankar Agarwal
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; (G.M.); (I.d.l.T.-D.)
| | - Gonçalo Marques
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; (G.M.); (I.d.l.T.-D.)
- Polytechnic of Coimbra, ESTGOH, Rua General Santos Costa, 3400-124 Oliveira do Hospital, Portugal
| | - Isabel de la Torre-Díez
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; (G.M.); (I.d.l.T.-D.)
| | - Manuel A. Franco Martin
- Psychiatric Department, University Rio Hortega Hospital–Valladolid, 47011 Valladolid, Spain;
| | - Begoña García Zapiraín
- eVIDA Laboratory, University of Deusto, Avenida de las Universidades 24, 48007 Bilbao, Spain;
| | - Francisco Martín Rodríguez
- Advanced Clinical Simulation Center, School of Medicine, University of Valladolid, 47011 Valladolid, Spain;
| |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
Smart aging monitoring and early dementia recognition (SAMEDR): uncovering the hidden wellness parameter for preventive well-being monitoring to categorize cognitive impairment and dementia in community-dwelling elderly subjects through AI. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06139-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
AbstractReasoning weakening because of dementia degrades the performance in activities of daily living (ADL). Present research work distinguishes care needs, dangers and monitors the effect of dementia on an individual. This research contrasts in ADL design execution between dementia-affected people and other healthy elderly with heterogeneous sensors. More than 300,000 sensors associated activation data were collected from the dementia patients and healthy controls with wellness sensors networks. Generated ADLs were envisioned and understood through the activity maps, diversity and other wellness parameters to categorize wellness healthy, and dementia affected the elderly. Diversity was significant between diseased and healthy subjects. Heterogeneous unobtrusive sensor data evaluate behavioral patterns associated with ADL, helpful to reveal the impact of cognitive degradation, to measure ADL variation throughout dementia. The primary focus of activity recognition in the current research is to transfer dementia subject occupied homes models to generalized age-matched healthy subject data models to utilize new services, label classified datasets and produce limited datasets due to less training. Current research proposes a novel Smart Aging Monitoring and Early Dementia Recognition system that provides the exchange of data models between dementia subject occupied homes (DSOH) to healthy subject occupied homes (HSOH) in a move to resolve the deficiency of training data. At that point, the key attributes are mapped onto each other utilizing a sensor data fusion that assures to retain the diversities between various HSOH & DSOH by diminishing the divergence between them. Moreover, additional tests have been conducted to quantify the excellence of the offered framework: primary, in contradiction of the precision of feature mapping techniques; next, computing the merit of categorizing data at DSOH; and, the last, the aptitude of the projected structure to function thriving due to noise data. The outcomes show encouraging pointers and highlight the boundaries of the projected approach.
Collapse
|
18
|
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%.
Collapse
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.
| |
Collapse
|
19
|
Feng Q, Ding Z. MRI Radiomics Classification and Prediction in Alzheimer's Disease and Mild Cognitive Impairment: A Review. Curr Alzheimer Res 2021; 17:297-309. [PMID: 32124697 DOI: 10.2174/1567205017666200303105016] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 02/03/2020] [Accepted: 03/01/2020] [Indexed: 01/18/2023]
Abstract
BACKGROUND Alzheimer's Disease (AD) is a progressive neurodegenerative disease that threatens the health of the elderly. Mild Cognitive Impairment (MCI) is considered to be the prodromal stage of AD. To date, AD or MCI diagnosis is established after irreversible brain structure alterations. Therefore, the development of new biomarkers is crucial to the early detection and treatment of this disease. At present, there exist some research studies showing that radiomics analysis can be a good diagnosis and classification method in AD and MCI. OBJECTIVE An extensive review of the literature was carried out to explore the application of radiomics analysis in the diagnosis and classification among AD patients, MCI patients, and Normal Controls (NCs). RESULTS Thirty completed MRI radiomics studies were finally selected for inclusion. The process of radiomics analysis usually includes the acquisition of image data, Region of Interest (ROI) segmentation, feature extracting, feature selection, and classification or prediction. From those radiomics methods, texture analysis occupied a large part. In addition, the extracted features include histogram, shapebased features, texture-based features, wavelet features, Gray Level Co-Occurrence Matrix (GLCM), and Run-Length Matrix (RLM). CONCLUSION Although radiomics analysis is already applied to AD and MCI diagnosis and classification, there still is a long way to go from these computer-aided diagnostic methods to the clinical application.
Collapse
Affiliation(s)
- Qi Feng
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Translational Medicine Research Center, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| |
Collapse
|
20
|
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.
Collapse
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
| | | |
Collapse
|
21
|
续 宝, 丁 冲, 徐 桂. [Research on the application of convolution neural network in the diagnosis of Alzheimer's disease]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2021; 38:169-177. [PMID: 33899442 PMCID: PMC10307567 DOI: 10.7507/1001-5515.202007019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 11/24/2020] [Indexed: 11/03/2022]
Abstract
With the wide application of deep learning technology in disease diagnosis, especially the outstanding performance of convolutional neural network (CNN) in computer vision and image processing, more and more studies have proposed to use this algorithm to achieve the classification of Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal cognition (CN). This article systematically reviews the application progress of several classic convolutional neural network models in brain image analysis and diagnosis at different stages of Alzheimer's disease, and discusses the existing problems and gives the possible development directions in order to provide some references.
Collapse
Affiliation(s)
- 宝红 续
- 河北工业大学 天津市生物电工与智能健康重点实验室(天津 300130)Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, P.R.China
- 河北工业大学 省部共建电工装备可靠性与智能化国家重点实验室(天津 300130)State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China
| | - 冲 丁
- 河北工业大学 天津市生物电工与智能健康重点实验室(天津 300130)Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, P.R.China
- 河北工业大学 省部共建电工装备可靠性与智能化国家重点实验室(天津 300130)State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China
| | - 桂芝 徐
- 河北工业大学 天津市生物电工与智能健康重点实验室(天津 300130)Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, P.R.China
- 河北工业大学 省部共建电工装备可靠性与智能化国家重点实验室(天津 300130)State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China
| |
Collapse
|
22
|
A tongue features fusion approach to predicting prediabetes and diabetes with machine learning. J Biomed Inform 2021; 115:103693. [PMID: 33540076 DOI: 10.1016/j.jbi.2021.103693] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 01/27/2021] [Accepted: 01/28/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Diabetics has become a serious public health burden in China. Multiple complications appear with the progression of diabetics pose a serious threat to the quality of human life and health. We can prevent the progression of prediabetics to diabetics and delay the progression to diabetics by early identification of diabetics and prediabetics and timely intervention, which have positive significance for improving public health. OBJECTIVE Using machine learning techniques, we establish the noninvasive diabetics risk prediction model based on tongue features fusion and predict the risk of prediabetics and diabetics. METHODS Applying the type TFDA-1 Tongue Diagnosis Instrument, we collect tongue images, extract tongue features including color and texture features using TDAS, and extract the advanced tongue features with ResNet-50, achieve the fusion of the two features with GA_XGBT, finally establish the noninvasive diabetics risk prediction model and evaluate the performance of testing effectiveness. RESULTS Cross-validation suggests the best performance of GA_XGBT model with fusion features, whose average CA is 0.821, the average AUROC is 0.924, the average AUPRC is 0.856, the average Precision is 0.834, the average Recall is 0.822, the average F1-score is 0.813. Test set suggests the best testing performance of GA_XGBT model, whose average CA is 0.81, the average AUROC is 0.918, the average AUPRC is 0.839, the average Precision is 0.821, the average Recall is 0.81, the average F1-score is 0.796. When we test prediabetics with GA_XGBT model, we find that the AUROC is 0.914, the Precision is 0.69, the Recall is 0.952, the F1-score is 0.8. When we test diabetics with GA_XGBT model, we find that the AUROC is 0.984, the Precision is 0.929, the Recall is 0.951, the F1-score is 0.94. CONCLUSIONS Based on tongue features, the study uses classical machine learning algorithm and deep learning algorithm to maximum the respective advantages. We combine the prior knowledge and potential features together, establish the noninvasive diabetics risk prediction model with features fusion algorithm, and detect prediabetics and diabetics noninvasively. Our study presents a feasible method for establishing the association between diabetics and the tongue image information and prove that tongue image information is a potential marker which facilitates effective early diagnosis of prediabetics and diabetics.
Collapse
|
23
|
Mehmood A, Yang S, Feng Z, Wang M, Ahmad AS, Khan R, Maqsood M, Yaqub M. A Transfer Learning Approach for Early Diagnosis of Alzheimer's Disease on MRI Images. Neuroscience 2021; 460:43-52. [PMID: 33465405 DOI: 10.1016/j.neuroscience.2021.01.002] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 12/23/2020] [Accepted: 01/03/2021] [Indexed: 01/07/2023]
Abstract
Mild cognitive impairment (MCI) detection using magnetic resonance image (MRI), plays a crucial role in the treatment of dementia disease at an early stage. Deep learning architecture produces impressive results in such research. Algorithms require a large number of annotated datasets for training the model. In this study, we overcome this issue by using layer-wise transfer learning as well as tissue segmentation of brain images to diagnose the early stage of Alzheimer's disease (AD). In layer-wise transfer learning, we used the VGG architecture family with pre-trained weights. The proposed model segregates between normal control (NC), the early mild cognitive impairment (EMCI), the late mild cognitive impairment (LMCI), and the AD. In this paper, 85 NC patients, 70 EMCI, 70 LMCI, and 75 AD patients access form the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Tissue segmentation was applied on each subject to extract the gray matter (GM) tissue. In order to check the validity, the proposed method is tested on preprocessing data and achieved the highest rates of the classification accuracy on AD vs NC is 98.73%, also distinguish between EMCI vs LMCI patients testing accuracy 83.72%, whereas remaining classes accuracy is more than 80%. Finally, we provide a comparative analysis with other studies which shows that the proposed model outperformed the state-of-the-art models in terms of testing accuracy.
Collapse
Affiliation(s)
- Atif Mehmood
- School of Artificial Intelligence, Xidian University, Xi'an 710071, China
| | - Shuyuan Yang
- School of Artificial Intelligence, Xidian University, Xi'an 710071, China.
| | - Zhixi Feng
- School of Artificial Intelligence, Xidian University, Xi'an 710071, China
| | - Min Wang
- Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China
| | - Al Smadi Ahmad
- School of Artificial Intelligence, Xidian University, Xi'an 710071, China
| | - Rizwan Khan
- School of Electronic Information and Communications, HUST University, Wuhan 4370074, China
| | - Muazzam Maqsood
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan
| | - Muhammad Yaqub
- Faculty of Information Technology, Beijing University of Technology, Beijing 10000, China
| |
Collapse
|
24
|
Wang F, Liu X, Yuan N, Qian B, Ruan L, Yin C, Jin C. Study on automatic detection and classification of breast nodule using deep convolutional neural network system. J Thorac Dis 2020; 12:4690-4701. [PMID: 33145042 PMCID: PMC7578508 DOI: 10.21037/jtd-19-3013] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Backgrounds Conventional ultrasound manual scanning and artificial diagnosis approaches in breast are considered to be operator-dependence, slight slow and error-prone. In this study, we used Automated Breast Ultrasound (ABUS) machine for the scanning, and deep convolutional neural network (CNN) technology, a kind of Deep Learning (DL) algorithm, for the detection and classification of breast nodules, aiming to achieve the automatic and accurate diagnosis of breast nodules. Methods Two hundred and ninety-three lesions from 194 patients with definite pathological diagnosis results (117 benign and 176 malignancy) were recruited as case group. Another 70 patients without breast diseases were enrolled as control group. All the breast scans were carried out by an ABUS machine and then randomly divided into training set, verification set and test set, with a proportion of 7:1:2. In the training set, we constructed a detection model by a three-dimensionally U-shaped convolutional neural network (3D U-Net) architecture for the purpose of segment the nodules from background breast images. Processes such as residual block, attention connections, and hard mining were used to optimize the model while strategies of random cropping, flipping and rotation for data augmentation. In the test phase, the current model was compared with those in previously reported studies. In the verification set, the detection effectiveness of detection model was evaluated. In the classification phase, multiple convolutional layers and fully-connected layers were applied to set up a classification model, aiming to identify whether the nodule was malignancy. Results Our detection model yielded a sensitivity of 91% and 1.92 false positive subjects per automatically scanned imaging. The classification model achieved a sensitivity of 87.0%, a specificity of 88.0% and an accuracy of 87.5%. Conclusions Deep CNN combined with ABUS maybe a promising tool for easy detection and accurate diagnosis of breast nodule.
Collapse
Affiliation(s)
- Feiqian Wang
- Department of Ultrasound, The First Affiliated Hospital of Xi'an Jiaotong University, China
| | - Xiaotong Liu
- National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, China.,School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Na Yuan
- Department of Ultrasound, The First Affiliated Hospital of Xi'an Jiaotong University, China
| | - Buyue Qian
- National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, China.,School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Litao Ruan
- Department of Ultrasound, The First Affiliated Hospital of Xi'an Jiaotong University, China
| | - Changchang Yin
- National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, China.,School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Ciping Jin
- National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, China.,School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| |
Collapse
|
25
|
Guo S, Xiao B, Wu C. Identifying subtypes of mild cognitive impairment from healthy aging based on multiple cortical features combined with volumetric measurements of the hippocampal subfields. Quant Imaging Med Surg 2020; 10:1477-1489. [PMID: 32676366 DOI: 10.21037/qims-19-872] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Background Mild cognitive impairment (MCI) is subtle cognitive decline with an estimated 10-15% yearly conversion rate toward Alzheimer's disease (AD). It remains unexplored in brain cortical association areas in different lobes and its changes with progression and conversion of MCI. Methods Brain structural magnetic resonance (MR) images were collected from 102 stable MCI (sMCI) patients. One hundred eleven were converted MCI (cMCI) patients, and 109 were normal control (NC). The cortical surface features and volumes of subcortical hippocampal subfields were calculated using the FreeSurfer software, followed by an analysis of variance (ANOVA) model, to reveal the differences between the NC-sMCI, NC-cMCI, and sMCI-cMCI groups. Afterward, the support vector machine-recursive feature elimination (SVM-RFE) method was applied to determine the differences between the groups. Results The experimental results showed that there were progressive degradations in either range or degree of the brain structure from NC to sMCI, and then to cMCI. The SVM classifier obtained accuracies with 64.62%, 78.96%, and 70.33% in the sMCI-NC, cMCI-NC, and cMCI-sMCI groups, respectively, using the volumes of hippocampal subfields independently. The combination of the volumes from the hippocampal subfields and cortical measurements could significantly increase the performance to 71.86%, 84.64%, and 76.86% for the sMCI-NC, cMCI-NC, and cMCI-sMCI classifications, respectively. Also, the brain regions corresponding to the dominant features with strong discriminative power were widely located in the temporal, frontal, parietal, olfactory cortexes, and most of the hippocampal subfields, which were associated with cognitive decline, memory impairment, spatial navigation, and attention control. Conclusions The combination of cortical features with the volumes of hippocampal subfields could supply critical information for MCI detection and its conversion.
Collapse
Affiliation(s)
- Shengwen Guo
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Benheng Xiao
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Congling Wu
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | | |
Collapse
|
26
|
Wen J, Thibeau-Sutre E, Diaz-Melo M, Samper-González J, Routier A, Bottani S, Dormont D, Durrleman S, Burgos N, Colliot O. Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation. Med Image Anal 2020; 63:101694. [PMID: 32417716 DOI: 10.1016/j.media.2020.101694] [Citation(s) in RCA: 195] [Impact Index Per Article: 48.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 03/23/2020] [Accepted: 03/27/2020] [Indexed: 10/24/2022]
Abstract
Numerous machine learning (ML) approaches have been proposed for automatic classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 papers have proposed to use convolutional neural networks (CNN) for AD classification from anatomical MRI. However, the classification performance is difficult to compare across studies due to variations in components such as participant selection, image preprocessing or validation procedure. Moreover, these studies are hardly reproducible because their frameworks are not publicly accessible and because implementation details are lacking. Lastly, some of these papers may report a biased performance due to inadequate or unclear validation or model selection procedures. In the present work, we aim to address these limitations through three main contributions. First, we performed a systematic literature review. We identified four main types of approaches: i) 2D slice-level, ii) 3D patch-level, iii) ROI-based and iv) 3D subject-level CNN. Moreover, we found that more than half of the surveyed papers may have suffered from data leakage and thus reported biased performance. Our second contribution is the extension of our open-source framework for classification of AD using CNN and T1-weighted MRI. The framework comprises previously developed tools to automatically convert ADNI, AIBL and OASIS data into the BIDS standard, and a modular set of image preprocessing procedures, classification architectures and evaluation procedures dedicated to deep learning. Finally, we used this framework to rigorously compare different CNN architectures. The data was split into training/validation/test sets at the very beginning and only the training/validation sets were used for model selection. To avoid any overfitting, the test sets were left untouched until the end of the peer-review process. Overall, the different 3D approaches (3D-subject, 3D-ROI, 3D-patch) achieved similar performances while that of the 2D slice approach was lower. Of note, the different CNN approaches did not perform better than a SVM with voxel-based features. The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://github.com/aramis-lab/AD-DL.
Collapse
Affiliation(s)
- Junhao Wen
- Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Inria, Aramis project-team, Paris F-75013, France
| | - Elina Thibeau-Sutre
- Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Inria, Aramis project-team, Paris F-75013, France
| | - Mauricio Diaz-Melo
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Jorge Samper-González
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Alexandre Routier
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Simona Bottani
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Didier Dormont
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Department of Neuroradiology, AP-HP, Hôpital de la PitiéSalpêtrière, Paris F-75013, France
| | - Stanley Durrleman
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Ninon Burgos
- Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Inria, Aramis project-team, Paris F-75013, France
| | - Olivier Colliot
- Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Inria, Aramis project-team, Paris F-75013, France; Department of Neuroradiology, AP-HP, Hôpital de la PitiéSalpêtrière, Paris F-75013, France; Department of Neurology, AP-HP, Hôpital de la PitiéSalpêtrière, Paris F-75013, France.
| | | | | |
Collapse
|
27
|
Ebrahimighahnavieh MA, Luo S, Chiong R. Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 187:105242. [PMID: 31837630 DOI: 10.1016/j.cmpb.2019.105242] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 11/13/2019] [Accepted: 11/25/2019] [Indexed: 06/10/2023]
Abstract
Alzheimer's Disease (AD) is one of the leading causes of death in developed countries. From a research point of view, impressive results have been reported using computer-aided algorithms, but clinically no practical diagnostic method is available. In recent years, deep models have become popular, especially in dealing with images. Since 2013, deep learning has begun to gain considerable attention in AD detection research, with the number of published papers in this area increasing drastically since 2017. Deep models have been reported to be more accurate for AD detection compared to general machine learning techniques. Nevertheless, AD detection is still challenging, and for classification, it requires a highly discriminative feature representation to separate similar brain patterns. This paper reviews the current state of AD detection using deep learning. Through a systematic literature review of over 100 articles, we set out the most recent findings and trends. Specifically, we review useful biomarkers and features (personal information, genetic data, and brain scans), the necessary pre-processing steps, and different ways of dealing with neuroimaging data originating from single-modality and multi-modality studies. Deep models and their performance are described in detail. Although deep learning has achieved notable performance in detecting AD, there are several limitations, especially regarding the availability of datasets and training procedures.
Collapse
Affiliation(s)
| | - Suhuai Luo
- The University of Newcastle, University Drive, Callaghan 2308, Australia
| | - Raymond Chiong
- The University of Newcastle, University Drive, Callaghan 2308, Australia.
| |
Collapse
|
28
|
Pan Z, Huang K, Huang W, Kim KH, Wu H, Yu Y, Kim KN, Yi S, Shin DA, Vora D, Gragnaniello C, Phan K, Tasiou A, Winder MJ, Koga H, Azimi P, Kang SY, Ha Y. The risk factors associated with delirium after lumbar spine surgery in elderly patients. Quant Imaging Med Surg 2019; 9:700-710. [PMID: 31143661 DOI: 10.21037/qims.2019.04.09] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Background To prospectively explore the incidence and risk factors for postoperative delirium in elderly patients following lumbar spine surgery. Methods This prospective study enrolled 148 consecutive patients over the age of 65 who were scheduled to undergo spine surgery. Patients were screened for delirium using the short Confusion Assessment Method (CAM) postoperatively. Patient demographics and relevant medical information were collected. Logistic regression analysis was used to identify the risk factors associated with postoperative delirium. Results Eighty-three patients (56.1%) who underwent lumbar spine surgery (not coexisting with cervical or thoracic spine surgery) were enrolled in our study. Post-operative delirium was noted in 14.5% of patients over 65 years old. The presence of preoperative Parkinsonism was significantly higher in the delirium group (41.7% vs. 8.5%, P=0.002), as was a higher preoperative C-reactive protein (CRP) (7.0±15.2 vs. 1.3±2.3 mg/L, P=0.017) when compared with the non-delirium group. Of the risk factors, male sex [odds ratio (OR) =0.10, 95% confidence interval (CI): 0.01-0.66, P=0.017], Parkinsonism (OR =5.83, 95% CI: 1.03-32.89, P=0.046), and lower baseline MMSE score (OR =0.71, 95% CI: 0.52-0.97, P=0.032) were independently associated with postoperative delirium in elderly patients undergoing lumbar spine surgery. Conclusions Post-operative delirium occurred in 14.5% of elderly patients who underwent lumbar spine surgery. Male sex, Parkinsonism, and lower baseline MMSE score were identified as independent risk factors for postoperative delirium in elderly patients following lumbar surgery.
Collapse
Affiliation(s)
- Zhimin Pan
- Department of Neurosurgery, Spine and Spinal Cord Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.,Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Kai Huang
- Department of Orthopedics, Zhabei Central Hospital of Jing'an District, Shanghai 200070, China
| | - Wei Huang
- Department of Clinical Laboratory, Jiangxi Province Children's Hospital, Nanchang 330006, China
| | - Ki Hoon Kim
- Department of Neurology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Hao Wu
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Yanghong Yu
- Department of Radiology, Jiangxi Province Children's Hospital, Nanchang 330006, China
| | - Keung Nyun Kim
- Department of Neurosurgery, Spine and Spinal Cord Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Seong Yi
- Department of Neurosurgery, Spine and Spinal Cord Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Dong Ah Shin
- Department of Neurosurgery, Spine and Spinal Cord Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Darshan Vora
- Department of Neurosurgery, George Washington University, Washington, DC 20037, USA
| | | | - Kevin Phan
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia; Prince of Wales Clinical School, University of New South Wales, Randwick, NSW 2031, Australia
| | - Anastasia Tasiou
- Department of Neurosurgery, University Hospital of Larissa, Larissa 41110, Greece
| | - Mark J Winder
- Department of Neurosurgery, St Vincent's Public and Private Hospitals, Darlinghurst 2010, NSW, Australia
| | - Hisashi Koga
- PELD Center, Iwai Orthopaedic Medical Hospital, Tokyo 133-0056, Japan
| | - Parisa Azimi
- Functional Neurosurgery Research Center of Shohada Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Suk Yun Kang
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong 18450, Republic of Korea
| | - Yoon Ha
- Department of Neurosurgery, Spine and Spinal Cord Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | | |
Collapse
|