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Xu X, Li J, Zhu Z, Zhao L, Wang H, Song C, Chen Y, Zhao Q, Yang J, Pei Y. A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis. Bioengineering (Basel) 2024; 11:219. [PMID: 38534493 DOI: 10.3390/bioengineering11030219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 02/15/2024] [Accepted: 02/21/2024] [Indexed: 03/28/2024] Open
Abstract
Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer's disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.
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Affiliation(s)
- Xi Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Zhichao Zhu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Linna Zhao
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Huina Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Changwei Song
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Yining Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Qing Zhao
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Jijiang Yang
- Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China
| | - Yan Pei
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan
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Qu R, Ji X, Wang S, Wang Z, Wang L, Yang X, Yin S, Gu J, Wang A, Xu G. An Integrated Multi-Channel Deep Neural Network for Mesial Temporal Lobe Epilepsy Identification Using Multi-Modal Medical Data. Bioengineering (Basel) 2023; 10:1234. [PMID: 37892964 PMCID: PMC10604199 DOI: 10.3390/bioengineering10101234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/22/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023] Open
Abstract
Epilepsy is a chronic brain disease with recurrent seizures. Mesial temporal lobe epilepsy (MTLE) is the most common pathological cause of epilepsy. With the development of computer-aided diagnosis technology, there are many auxiliary diagnostic approaches based on deep learning algorithms. However, the causes of epilepsy are complex, and distinguishing different types of epilepsy accurately is challenging with a single mode of examination. In this study, our aim is to assess the combination of multi-modal epilepsy medical information from structural MRI, PET image, typical clinical symptoms and personal demographic and cognitive data (PDC) by adopting a multi-channel 3D deep convolutional neural network and pre-training PET images. The results show better diagnosis accuracy than using one single type of medical data alone. These findings reveal the potential of a deep neural network in multi-modal medical data fusion.
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Affiliation(s)
- Ruowei Qu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China; (R.Q.); (X.J.); (Z.W.); (X.Y.)
| | - Xuan Ji
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China; (R.Q.); (X.J.); (Z.W.); (X.Y.)
| | - Shifeng Wang
- Tianjin Universal Medical Imaging Diagnostic Center, Tianjin 300110, China;
| | - Zhaonan Wang
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China; (R.Q.); (X.J.); (Z.W.); (X.Y.)
| | - Le Wang
- Department of Functional Neurosurgery, Huanhu Hospital, Tianjin 300350, China; (L.W.); (S.Y.)
| | - Xinsheng Yang
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China; (R.Q.); (X.J.); (Z.W.); (X.Y.)
| | - Shaoya Yin
- Department of Functional Neurosurgery, Huanhu Hospital, Tianjin 300350, China; (L.W.); (S.Y.)
| | - Junhua Gu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China; (R.Q.); (X.J.); (Z.W.); (X.Y.)
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Grafton, Auckland 1010, New Zealand;
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China; (R.Q.); (X.J.); (Z.W.); (X.Y.)
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Zeibich R, Kwan P, J. O’Brien T, Perucca P, Ge Z, Anderson A. Applications for Deep Learning in Epilepsy Genetic Research. Int J Mol Sci 2023; 24:14645. [PMID: 37834093 PMCID: PMC10572791 DOI: 10.3390/ijms241914645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 09/11/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023] Open
Abstract
Epilepsy is a group of brain disorders characterised by an enduring predisposition to generate unprovoked seizures. Fuelled by advances in sequencing technologies and computational approaches, more than 900 genes have now been implicated in epilepsy. The development and optimisation of tools and methods for analysing the vast quantity of genomic data is a rapidly evolving area of research. Deep learning (DL) is a subset of machine learning (ML) that brings opportunity for novel investigative strategies that can be harnessed to gain new insights into the genomic risk of people with epilepsy. DL is being harnessed to address limitations in accuracy of long-read sequencing technologies, which improve on short-read methods. Tools that predict the functional consequence of genetic variation can represent breaking ground in addressing critical knowledge gaps, while methods that integrate independent but complimentary data enhance the predictive power of genetic data. We provide an overview of these DL tools and discuss how they may be applied to the analysis of genetic data for epilepsy research.
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Affiliation(s)
- Robert Zeibich
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3800, Australia; (R.Z.); (P.K.); (T.J.O.); (P.P.)
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3800, Australia; (R.Z.); (P.K.); (T.J.O.); (P.P.)
- Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia
- Department of Neurology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Terence J. O’Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3800, Australia; (R.Z.); (P.K.); (T.J.O.); (P.P.)
- Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia
- Department of Neurology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Piero Perucca
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3800, Australia; (R.Z.); (P.K.); (T.J.O.); (P.P.)
- Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia
- Department of Neurology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
- Epilepsy Research Centre, Department of Medicine, Austin Health, The University of Melbourne, Melbourne, VIC 3084, Australia
- Bladin-Berkovic Comprehensive Epilepsy Program, Department of Neurology, Austin Health, The University of Melbourne, Melbourne, VIC 3084, Australia
| | - Zongyuan Ge
- Faculty of Engineering, Monash University, Melbourne, VIC 3800, Australia;
- Monash-Airdoc Research, Monash University, Melbourne, VIC 3800, Australia
| | - Alison Anderson
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3800, Australia; (R.Z.); (P.K.); (T.J.O.); (P.P.)
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC 3052, Australia
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Si X, Zhang X, Zhou Y, Chao Y, Lim SN, Sun Y, Yin S, Jin W, Zhao X, Li Q, Ming D. White matter structural connectivity as a biomarker for detecting juvenile myoclonic epilepsy by transferred deep convolutional neural networks with varying transfer rates. J Neural Eng 2021; 18. [PMID: 34507303 DOI: 10.1088/1741-2552/ac25d8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 09/10/2021] [Indexed: 11/12/2022]
Abstract
Objective. By detecting abnormal white matter changes, diffusion magnetic resonance imaging (MRI) contributes to the detection of juvenile myoclonic epilepsy (JME). In addition, deep learning has greatly improved the detection performance of various brain disorders. However, there is almost no previous study effectively detecting JME by a deep learning approach with diffusion MRI.Approach. In this study, the white matter structural connectivity was generated by tracking the white matter fibers in detail based on Q-ball imaging and neurite orientation dispersion and density imaging. Four advanced deep convolutional neural networks (CNNs) were deployed by using the transfer learning approach, in which the transfer rate searching strategy was proposed to achieve the best detection performance.Main results. Our results showed: (a) Compared to normal control, the white matter' neurite density of JME was significantly decreased. The most significantly abnormal fiber tracts between the two groups were found to be cortico-cortical connection tracts. (b) The proposed transfer rate searching approach contributed to find each CNN's best performance, in which the best JME detection accuracy of 92.2% was achieved by using the Inception_resnet_v2 network with a 16% transfer rate.Significance. The results revealed: (a) Through detection of the abnormal white matter changes, the white matter structural connectivity can be used as a useful biomarker for detecting JME, which helps to characterize the pathophysiology of epilepsy. (b) The proposed transfer rate, as a new hyperparameter, promotes the CNNs transfer learning performance in detecting JME.
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Affiliation(s)
- Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China.,Institute of Applied Psychology, Tianjin University, Tianjin 300350, People's Republic of China
| | - Xingjian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Yu Zhou
- School of Microelectronics, Tianjin University, Tianjin 300072, People's Republic of China
| | - Yiping Chao
- Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan 33302, Taiwan
| | - Siew-Na Lim
- Department of Neurology, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
| | - Yulin Sun
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Shaoya Yin
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin 300072, People's Republic of China
| | - Weipeng Jin
- Department of Neurosurgery, Huanhu Hospital, Tianjin University, Tianjin 300072, People's Republic of China
| | - Xin Zhao
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Qiang Li
- School of Microelectronics, Tianjin University, Tianjin 300072, People's Republic of China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, People's Republic of China
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Sone D, Beheshti I. Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review. Front Neurosci 2021; 15:684825. [PMID: 34239413 PMCID: PMC8258163 DOI: 10.3389/fnins.2021.684825] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/31/2021] [Indexed: 12/13/2022] Open
Abstract
Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions.
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Affiliation(s)
- Daichi Sone
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan.,Department of Clinical and Experimental Epilepsy, University College London Institute of Neurology, London, United Kingdom
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
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Valverde JM, Imani V, Abdollahzadeh A, De Feo R, Prakash M, Ciszek R, Tohka J. Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review. J Imaging 2021; 7:66. [PMID: 34460516 PMCID: PMC8321322 DOI: 10.3390/jimaging7040066] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 03/26/2021] [Accepted: 03/29/2021] [Indexed: 11/25/2022] Open
Abstract
(1) Background: Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. Additionally, transfer learning is beneficial for reutilizing machine learning models that were trained to solve different (but related) tasks to the task of interest. The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging; (2) Methods: We performed a systematic literature search for articles that applied transfer learning to MR brain imaging tasks. We screened 433 studies for their relevance, and we categorized and extracted relevant information, including task type, application, availability of labels, and machine learning methods. Furthermore, we closely examined brain MRI-specific transfer learning approaches and other methods that tackled issues relevant to medical imaging, including privacy, unseen target domains, and unlabeled data; (3) Results: We found 129 articles that applied transfer learning to MR brain imaging tasks. The most frequent applications were dementia-related classification tasks and brain tumor segmentation. The majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). Only a few approaches utilized clearly brain MRI-specific methodology, and considered privacy issues, unseen target domains, or unlabeled data. We proposed a new categorization to group specific, widely-used approaches such as pretraining and fine-tuning CNNs; (4) Discussion: There is increasing interest in transfer learning for brain MRI. Well-known public datasets have clearly contributed to the popularity of Alzheimer's diagnostics/prognostics and tumor segmentation as applications. Likewise, the availability of pretrained CNNs has promoted their utilization. Finally, the majority of the surveyed studies did not examine in detail the interpretation of their strategies after applying transfer learning, and did not compare their approach with other transfer learning approaches.
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Affiliation(s)
| | | | | | | | | | | | - Jussi Tohka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70150 Kuopio, Finland; (J.M.V.); (V.I.); (A.A.); (R.D.F.); (M.P.); (R.C.)
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