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Schielen SJC, Pilmeyer J, Aldenkamp AP, Zinger S. The diagnosis of ASD with MRI: a systematic review and meta-analysis. Transl Psychiatry 2024; 14:318. [PMID: 39095368 PMCID: PMC11297045 DOI: 10.1038/s41398-024-03024-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 06/25/2024] [Accepted: 07/15/2024] [Indexed: 08/04/2024] Open
Abstract
While diagnosing autism spectrum disorder (ASD) based on an objective test is desired, the current diagnostic practice involves observation-based criteria. This study is a systematic review and meta-analysis of studies that aim to diagnose ASD using magnetic resonance imaging (MRI). The main objective is to describe the state of the art of diagnosing ASD using MRI in terms of performance metrics and interpretation. Furthermore, subgroups, including different MRI modalities and statistical heterogeneity, are analyzed. Studies that dichotomously diagnose individuals with ASD and healthy controls by analyses progressing from magnetic resonance imaging obtained in a resting state were systematically selected by two independent reviewers. Studies were sought on Web of Science and PubMed, which were last accessed on February 24, 2023. The included studies were assessed on quality and risk of bias using the revised Quality Assessment of Diagnostic Accuracy Studies tool. A bivariate random-effects model was used for syntheses. One hundred and thirty-four studies were included comprising 159 eligible experiments. Despite the overlap in the studied samples, an estimated 4982 unique participants consisting of 2439 individuals with ASD and 2543 healthy controls were included. The pooled summary estimates of diagnostic performance are 76.0% sensitivity (95% CI 74.1-77.8), 75.7% specificity (95% CI 74.0-77.4), and an area under curve of 0.823, but uncertainty in the study assessments limits confidence. The main limitations are heterogeneity and uncertainty about the generalization of diagnostic performance. Therefore, comparisons between subgroups were considered inappropriate. Despite the current limitations, methods progressing from MRI approach the diagnostic performance needed for clinical practice. The state of the art has obstacles but shows potential for future clinical application.
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Affiliation(s)
- Sjir J C Schielen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
| | - Jesper Pilmeyer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Albert P Aldenkamp
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Department of Behavioral Sciences, Epilepsy Center Kempenhaeghe, Heeze, the Netherlands
| | - Svitlana Zinger
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
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2
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Maurya R, Mahapatra S, Dutta MK, Singh VP, Karnati M, Sahu G, Pandey NN. Skin cancer detection through attention guided dual autoencoder approach with extreme learning machine. Sci Rep 2024; 14:17785. [PMID: 39090261 PMCID: PMC11294626 DOI: 10.1038/s41598-024-68749-1] [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: 04/16/2024] [Accepted: 07/26/2024] [Indexed: 08/04/2024] Open
Abstract
Skin cancer is a lethal disease, and its early detection plays a pivotal role in preventing its spread to other body organs and tissues. Artificial Intelligence (AI)-based automated methods can play a significant role in its early detection. This study presents an AI-based novel approach, termed 'DualAutoELM' for the effective identification of various types of skin cancers. The proposed method leverages a network of autoencoders, comprising two distinct autoencoders: the spatial autoencoder and the FFT (Fast Fourier Transform)-autoencoder. The spatial-autoencoder specializes in learning spatial features within input lesion images whereas the FFT-autoencoder learns to capture textural and distinguishing frequency patterns within transformed input skin lesion images through the reconstruction process. The use of attention modules at various levels within the encoder part of these autoencoders significantly improves their discriminative feature learning capabilities. An Extreme Learning Machine (ELM) with a single layer of feedforward is trained to classify skin malignancies using the characteristics that were recovered from the bottleneck layers of these autoencoders. The 'HAM10000' and 'ISIC-2017' are two publicly available datasets used to thoroughly assess the suggested approach. The experimental findings demonstrate the accuracy and robustness of the proposed technique, with AUC, precision, and accuracy values for the 'HAM10000' dataset being 0.98, 97.68% and 97.66%, and for the 'ISIC-2017' dataset being 0.95, 86.75% and 86.68%, respectively. This study highlights the possibility of the suggested approach for accurate detection of skin cancer.
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Affiliation(s)
- Ritesh Maurya
- Amity Centre for Artificial Intelligence, Amity University, Noida, India
| | - Satyajit Mahapatra
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
| | | | - Vibhav Prakash Singh
- Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Allahabad, India
| | - Mohan Karnati
- Computer Science and Engineering Department, National Institute of Technology Raipur, Chhattisgarh, 492010, India
| | - Geet Sahu
- Amity Centre for Artificial Intelligence, Amity University, Noida, India
| | - Nageshwar Nath Pandey
- Department of Computer Science and Engineering, ITER, Siksha 'O' Anusandhan, Bhubaneswar, Odisha, India
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3
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Jang HD, Kwon S, Nam H, Chang DE. Semi-Supervised Autoencoder for Chemical Gas Classification with FTIR Spectrum. SENSORS (BASEL, SWITZERLAND) 2024; 24:3601. [PMID: 38894390 PMCID: PMC11175179 DOI: 10.3390/s24113601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/13/2024] [Accepted: 05/31/2024] [Indexed: 06/21/2024]
Abstract
Chemical warfare agents pose a serious threat due to their extreme toxicity, necessitating swift the identification of chemical gases and individual responses to the identified threats. Fourier transform infrared (FTIR) spectroscopy offers a method for remote material analysis, particularly in detecting colorless and odorless chemical agents. In this paper, we propose a deep neural network utilizing a semi-supervised autoencoder (SSAE) for the classification of chemical gases based on FTIR spectra. In contrast to traditional methods, the SSAE concurrently trains an autoencoder and a classifier attached to a latent vector of the autoencoder, enhancing feature extraction for classification. The SSAE was evaluated on laboratory-collected FTIR spectra, demonstrating a superior classification performance compared to existing methods. The efficacy of the SSAE lies in its ability to generate denser cluster distributions in latent vectors, thereby enhancing gas classification. This study established a consistent experimental environment for hyperparameter optimization, offering valuable insights into the influence of latent vectors on classification performance.
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Affiliation(s)
- Hee-Deok Jang
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea; (H.-D.J.); (S.K.)
| | - Seokjoon Kwon
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea; (H.-D.J.); (S.K.)
| | - Hyunwoo Nam
- Chem-Bio Technology Center, Advanced Defense Science and Technology Research Institute, Agency for Defense Development, Daejeon 34186, Republic of Korea;
| | - Dong Eui Chang
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea; (H.-D.J.); (S.K.)
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4
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Zhang X, Gao Y, Zhang Y, Li F, Li H, Lei F. Identification of Autism Spectrum Disorder Using Topological Data Analysis. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1023-1037. [PMID: 38351222 PMCID: PMC11169318 DOI: 10.1007/s10278-024-01002-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/30/2023] [Accepted: 11/21/2023] [Indexed: 06/13/2024]
Abstract
Autism spectrum disorder (ASD) is a pervasive brain development disease. Recently, the incidence rate of ASD has increased year by year and posed a great threat to the lives and families of individuals with ASD. Therefore, the study of ASD has become very important. A suitable feature representation that preserves the data intrinsic information and also reduces data complexity is very vital to the performance of established models. Topological data analysis (TDA) is an emerging and powerful mathematical tool for characterizing shapes and describing intrinsic information in complex data. In TDA, persistence barcodes or diagrams are usually regarded as visual representations of topological features of data. In this paper, the Regional Homogeneity (ReHo) data of subjects obtained from Autism Brain Imaging Data Exchange (ABIDE) database were used to extract features by using TDA. The average accuracy of cross validation on ABIDE I database was 95.6% that was higher than any other existing methods (the highest accuracy among existing methods was 93.59%). The average accuracy for sampling with the same resolutions with the ABIDE I on the ABIDE II database was 96.5% that was also higher than any other existing methods (the highest accuracy among existing methods was 75.17%).
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Affiliation(s)
- Xudong Zhang
- School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China
| | - Yaru Gao
- School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China
| | - Yunge Zhang
- School of Biomedical Engineering, Dalian University of Technology, Dalian, 116024, China
| | - Fengling Li
- School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China.
| | - Huanjie Li
- School of Biomedical Engineering, Dalian University of Technology, Dalian, 116024, China
| | - Fengchun Lei
- School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China
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5
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Dai P, Shi Y, Lu D, Zhou Y, Luo J, He Z, Chen Z, Zou B, Tang H, Huang Z, Liao S. Classification of recurrent major depressive disorder using a residual denoising autoencoder framework: Insights from large-scale multisite fMRI data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 247:108114. [PMID: 38447315 DOI: 10.1016/j.cmpb.2024.108114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 02/14/2024] [Accepted: 03/01/2024] [Indexed: 03/08/2024]
Abstract
BACKGROUND AND OBJECTIVE Recurrent major depressive disorder (rMDD) has a high recurrence rate, and symptoms often worsen with each episode. Classifying rMDD using functional magnetic resonance imaging (fMRI) can enhance understanding of brain activity and aid diagnosis and treatment of this disorder. METHODS We developed a Residual Denoising Autoencoder (Res-DAE) framework for the classification of rMDD. The functional connectivity (FC) was extracted from fMRI data as features. The framework addresses site heterogeneity by employing the Combat method to harmonize feature distribution differences. A feature selection method based on Fisher scores was used to reduce redundant information in the features. A data augmentation strategy using a Synthetic Minority Over-sampling Technique algorithm based on Extended Frobenius Norm measure was incorporated to increase the sample size. Furthermore, a residual module was integrated into the autoencoder network to preserve important features and improve the classification accuracy. RESULTS We tested our framework on a large-scale, multisite fMRI dataset, which includes 189 rMDD patients and 427 healthy controls. The Res-DAE achieved an average accuracy of 75.1 % (sensitivity = 69 %, specificity = 77.8 %) in cross-validation, thereby outperforming comparison methods. In a larger dataset that also includes first-episode depression (comprising 832 MDD patients and 779 healthy controls), the accuracy reached 70 %. CONCLUSIONS We proposed a deep learning framework that can effectively classify rMDD and 33 identify the altered FC associated with rMDD. Our study may reveal changes in brain function 34 associated with rMDD and provide assistance for the diagnosis and treatment of rMDD.
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Affiliation(s)
- Peishan Dai
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China.
| | - Yun Shi
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Da Lu
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Ying Zhou
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Jialin Luo
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Zhuang He
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Zailiang Chen
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Beiji Zou
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Hui Tang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410083, China
| | - Zhongchao Huang
- Department of Biomedical Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan 410083, China
| | - Shenghui Liao
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
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6
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Creux C, Zehraoui F, Hanczar B, Tahi F. A3SOM, abstained explainable semi-supervised neural network based on self-organizing map. PLoS One 2023; 18:e0286137. [PMID: 37228138 DOI: 10.1371/journal.pone.0286137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 05/09/2023] [Indexed: 05/27/2023] Open
Abstract
In the sea of data generated daily, unlabeled samples greatly outnumber labeled ones. This is due to the fact that, in many application areas, labels are scarce or hard to obtain. In addition, unlabeled samples might belong to new classes that are not available in the label set associated with data. In this context, we propose A3SOM, an abstained explainable semi-supervised neural network that associates a self-organizing map to dense layers in order to classify samples. Abstained classification enables the detection of new classes and class overlaps. The use of a self-organizing map in A3SOM allows integrated visualization and makes the model explainable. Along with describing our approach, this paper shows that the method is competitive with other classifiers and demonstrates the benefits of including abstention rules. A use case is presented on breast cancer subtype classification and discovery to show the relevance of our method in real-world medical problems.
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Affiliation(s)
- Constance Creux
- Univ Evry, IBISC, Université Paris-Saclay, Evry-Courcouronnes, France
| | - Farida Zehraoui
- Univ Evry, IBISC, Université Paris-Saclay, Evry-Courcouronnes, France
| | - Blaise Hanczar
- Univ Evry, IBISC, Université Paris-Saclay, Evry-Courcouronnes, France
| | - Fariza Tahi
- Univ Evry, IBISC, Université Paris-Saclay, Evry-Courcouronnes, France
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7
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Fu H, Yu H, Wang X, Lu X, Zhu C. A Semi-Supervised Speech Deception Detection Algorithm Combining Acoustic Statistical Features and Time-Frequency Two-Dimensional Features. Brain Sci 2023; 13:brainsci13050725. [PMID: 37239197 DOI: 10.3390/brainsci13050725] [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: 03/09/2023] [Revised: 04/23/2023] [Accepted: 04/24/2023] [Indexed: 05/28/2023] Open
Abstract
Human lying is influenced by cognitive neural mechanisms in the brain, and conducting research on lie detection in speech can help to reveal the cognitive mechanisms of the human brain. Inappropriate deception detection features can easily lead to dimension disaster and make the generalization ability of the widely used semi-supervised speech deception detection model worse. Because of this, this paper proposes a semi-supervised speech deception detection algorithm combining acoustic statistical features and time-frequency two-dimensional features. Firstly, a hybrid semi-supervised neural network based on a semi-supervised autoencoder network (AE) and a mean-teacher network is established. Secondly, the static artificial statistical features are input into the semi-supervised AE to extract more robust advanced features, and the three-dimensional (3D) mel-spectrum features are input into the mean-teacher network to obtain features rich in time-frequency two-dimensional information. Finally, a consistency regularization method is introduced after feature fusion, effectively reducing the occurrence of over-fitting and improving the generalization ability of the model. This paper carries out experiments on the self-built corpus for deception detection. The experimental results show that the highest recognition accuracy of the algorithm proposed in this paper is 68.62% which is 1.2% higher than the baseline system and effectively improves the detection accuracy.
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Affiliation(s)
- Hongliang Fu
- Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China
- Henan Engineering Laboratory of Grain IOT Technology, Henan University of Technology, Zhengzhou 450001, China
| | - Hang Yu
- Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China
| | - Xuemei Wang
- Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China
- Henan Engineering Laboratory of Grain IOT Technology, Henan University of Technology, Zhengzhou 450001, China
| | - Xiangying Lu
- Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China
| | - Chunhua Zhu
- Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China
- Henan Engineering Laboratory of Grain IOT Technology, Henan University of Technology, Zhengzhou 450001, China
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8
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Astrid M, Zaheer MZ, Lee SI. PseudoBound: Limiting the Anomaly Reconstruction Capability of One-Class Classifiers Using Pseudo Anomalies. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
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9
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Jo H, Jun CH. A personalized classification model using similarity learning via supervised autoencoder. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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10
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Hao X, An Q, Li J, Min H, Guo Y, Yu M, Qin J. Exploring high-order correlations with deep-broad learning for autism spectrum disorder diagnosis. Front Neurosci 2022; 16:1046268. [PMID: 36483179 PMCID: PMC9723136 DOI: 10.3389/fnins.2022.1046268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 11/02/2022] [Indexed: 01/25/2023] Open
Abstract
Recently, a lot of research has been conducted on diagnosing neurological disorders, such as autism spectrum disorder (ASD). Functional magnetic resonance imaging (fMRI) is the commonly used technique to assist in the diagnosis of ASD. In the past years, some conventional methods have been proposed to extract the low-order functional connectivity network features for ASD diagnosis, which ignore the complexity and global features of the brain network. Most deep learning-based methods generally have a large number of parameters that need to be adjusted during the learning process. To overcome the limitations mentioned above, we propose a novel deep-broad learning method for learning the higher-order brain functional connectivity network features to assist in ASD diagnosis. Specifically, we first construct the high-order functional connectivity network that describes global correlations of the brain regions based on hypergraph, and then we use the deep-broad learning method to extract the high-dimensional feature representations for brain networks sequentially. The evaluation of the proposed method is conducted on Autism Brain Imaging Data Exchange (ABIDE) dataset. The results show that our proposed method can achieve 71.8% accuracy on the multi-center dataset and 70.6% average accuracy on 17 single-center datasets, which are the best results compared with the state-of-the-art methods. Experimental results demonstrate that our method can describe the global features of the brain regions and get rich discriminative information for the classification task.
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Affiliation(s)
- Xiaoke Hao
- School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Qijin An
- School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Jiayang Li
- School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Hongjie Min
- School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Yingchun Guo
- School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Ming Yu
- School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Jing Qin
- School of Nursing, Centre for Smart Health, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
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11
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Chen ZS, Kulkarni P(P, Galatzer-Levy IR, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. PATTERNS (NEW YORK, N.Y.) 2022; 3:100602. [PMID: 36419447 PMCID: PMC9676543 DOI: 10.1016/j.patter.2022.100602] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA
| | | | - Isaac R. Galatzer-Levy
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Meta Reality Lab, New York, NY, USA
| | - Benedetta Bigio
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Carla Nasca
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA
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12
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Moridian P, Ghassemi N, Jafari M, Salloum-Asfar S, Sadeghi D, Khodatars M, Shoeibi A, Khosravi A, Ling SH, Subasi A, Alizadehsani R, Gorriz JM, Abdulla SA, Acharya UR. Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review. Front Mol Neurosci 2022; 15:999605. [PMID: 36267703 PMCID: PMC9577321 DOI: 10.3389/fnmol.2022.999605] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 08/09/2022] [Indexed: 12/04/2022] Open
Abstract
Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.
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Affiliation(s)
- Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Navid Ghassemi
- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mahboobeh Jafari
- Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran
| | - Salam Salloum-Asfar
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Delaram Sadeghi
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Marjane Khodatars
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Afshin Shoeibi
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Sai Ho Ling
- Faculty of Engineering and IT, University of Technology Sydney (UTS), Ultimo, NSW, Australia
| | - Abdulhamit Subasi
- Faculty of Medicine, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Computer Science, College of Engineering, Effat University, Jeddah, Saudi Arabia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Juan M. Gorriz
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Sara A. Abdulla
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Singapore, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
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13
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Supervised Approach to Identify Autism Spectrum Neurological Disorder via Label Distribution Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4464603. [PMID: 36065371 PMCID: PMC9440771 DOI: 10.1155/2022/4464603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/09/2022] [Accepted: 08/02/2022] [Indexed: 11/17/2022]
Abstract
Autism Spectrum Disorder (ASD) is a complicated collection of neurodevelopmental illnesses characterized by a variety of developmental defects. It is a binary classification system that cannot cope with reality. Furthermore, ASD, data label noise, high dimension, and data distribution imbalance have all hampered the existing classification algorithms. As a result, a new ASD was proposed. This strategy employs label distribution learning (LDL) to deal with label noise and uses support vector regression (SVR) to deal with sample imbalance. The experimental results show that the proposed method balances the effects of majority and minority classes on outcomes. It can effectively deal with imbalanced data in ASD diagnosis, and it can help with ASD diagnosis. This study presents a cost-sensitive approach to correct sample imbalance and uses a support vector regression (SVR)-based method to remove label noise. The label distribution learning approach overcomes high-dimensional feature classification issues by mapping samples to the feature space and then diagnosing multiclass ASD. This technique outperforms previous methods in terms of classification performance and accuracy, as well as resolving the issue of unbalanced data in ASD diagnosis.
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