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Rubio-Martín S, García-Ordás MT, Bayón-Gutiérrez M, Prieto-Fernández N, Benítez-Andrades JA. Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing. Health Inf Sci Syst 2024; 12:20. [PMID: 38455725 PMCID: PMC10917721 DOI: 10.1007/s13755-024-00281-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 02/04/2024] [Indexed: 03/09/2024] Open
Abstract
Purpose The main aim of our study was to explore the utility of artificial intelligence (AI) in diagnosing autism spectrum disorder (ASD). The study primarily focused on using machine learning (ML) and deep learning (DL) models to detect ASD potential cases by analyzing text inputs, especially from social media platforms like Twitter. This is to overcome the ongoing challenges in ASD diagnosis, such as the requirement for specialized professionals and extensive resources. Timely identification, particularly in children, is essential to provide immediate intervention and support, thereby improving the quality of life for affected individuals. Methods We employed natural language processing (NLP) techniques along with ML models like decision trees, extreme gradient boosting (XGB), k-nearest neighbors algorithm (KNN), and DL models such as recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), bidirectional encoder representations from transformers (BERT and BERTweet). We extracted a dataset of 404,627 tweets from Twitter users using the platform's API and classified them based on whether they were written by individuals claiming to have ASD (ASD users) or by those without ASD (non-ASD users). From this dataset, we used a subset of 90,000 tweets (45,000 from each classification group) for the training and testing of these models. Results The application of our AI models yielded promising results, with the predictive model reaching an accuracy of almost 88% when classifying texts that potentially originated from individuals with ASD. Conclusion Our research demonstrated the potential of using AI, particularly DL models, in enhancing the accuracy of ASD detection and diagnosis. This innovative approach signifies the critical role AI can play in advancing early diagnostic techniques, enabling better patient outcomes and underlining the importance of early identification of ASD, especially in children.
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Affiliation(s)
- Sergio Rubio-Martín
- SALBIS Research Group, Dept. of Electric, Systems and Automatics Engineering, Universidad de León, Campus of Vegazana s/n, 24071 León, León Spain
| | - María Teresa García-Ordás
- SECOMUCI Research Group, Dept. of Electric, Systems and Automatics Engineering, Universidad de León, Campus of Vegazana s/n, 24071 León, León Spain
| | - Martín Bayón-Gutiérrez
- SECOMUCI Research Group, Dept. of Electric, Systems and Automatics Engineering, Universidad de León, Campus of Vegazana s/n, 24071 León, León Spain
| | - Natalia Prieto-Fernández
- SECOMUCI Research Group, Dept. of Electric, Systems and Automatics Engineering, Universidad de León, Campus of Vegazana s/n, 24071 León, León Spain
| | - José Alberto Benítez-Andrades
- SALBIS Research Group, Dept. of Electric, Systems and Automatics Engineering, Universidad de León, Campus of Vegazana s/n, 24071 León, León Spain
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2
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Ma C, Neri F, Gu L, Wang Z, Wang J, Qing A, Wang Y. Crowd Counting Using Meta-Test-Time Adaptation. Int J Neural Syst 2024; 34:2450061. [PMID: 39252679 DOI: 10.1142/s0129065724500618] [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: 09/11/2024]
Abstract
Machine learning algorithms are commonly used for quickly and efficiently counting people from a crowd. Test-time adaptation methods for crowd counting adjust model parameters and employ additional data augmentation to better adapt the model to the specific conditions encountered during testing. The majority of current studies concentrate on unsupervised domain adaptation. These approaches commonly perform hundreds of epochs of training iterations, requiring a sizable number of unannotated data of every new target domain apart from annotated data of the source domain. Unlike these methods, we propose a meta-test-time adaptive crowd counting approach called CrowdTTA, which integrates the concept of test-time adaptation into the meta-learning framework and makes it easier for the counting model to adapt to the unknown test distributions. To facilitate the reliable supervision signal at the pixel level, we introduce uncertainty by inserting the dropout layer into the counting model. The uncertainty is then used to generate valuable pseudo labels, serving as effective supervisory signals for adapting the model. In the context of meta-learning, one image can be regarded as one task for crowd counting. In each iteration, our approach is a dual-level optimization process. In the inner update, we employ a self-supervised consistency loss function to optimize the model so as to simulate the parameters update process that occurs during the test phase. In the outer update, we authentically update the parameters based on the image with ground truth, improving the model's performance and making the pseudo labels more accurate in the next iteration. At test time, the input image is used for adapting the model before testing the image. In comparison to various supervised learning and domain adaptation methods, our results via extensive experiments on diverse datasets showcase the general adaptive capability of our approach across datasets with varying crowd densities and scales.
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Affiliation(s)
- Chaoqun Ma
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, P. R. China
| | - Ferrante Neri
- NICE Group, School of Computer Science and Electronic Engineering, University of Surrey, Guildford, Surrey GU2 7XH, UK
| | - Li Gu
- Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3H 2L9, Canada
| | - Ziqiang Wang
- Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3H 2L9, Canada
| | - Jian Wang
- Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, P. R. China
| | - Anyong Qing
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, P. R. China
| | - Yang Wang
- Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3H 2L9, Canada
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3
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Liu Y, Jiang Y, Liu J, Li J, Liu M, Nie W, Yuan Q. Efficient EEG Feature Learning Model Combining Random Convolutional Kernel with Wavelet Scattering for Seizure Detection. Int J Neural Syst 2024; 34:2450060. [PMID: 39252680 DOI: 10.1142/s0129065724500606] [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: 09/11/2024]
Abstract
Automatic seizure detection has significant value in epilepsy diagnosis and treatment. Although a variety of deep learning models have been proposed to automatically learn electroencephalography (EEG) features for seizure detection, the generalization performance and computational burden of such deep models remain the bottleneck of practical application. In this study, a novel lightweight model based on random convolutional kernel transform (ROCKET) is developed for EEG feature learning for seizure detection. Specifically, random convolutional kernels are embedded into the structure of a wavelet scattering network instead of original wavelet transform convolutions. Then the significant EEG features are selected from the scattering coefficients and convolutional outputs by analysis of variance (ANOVA) and minimum redundancy-maximum relevance (MRMR) methods. This model not only preserves the merits of the fast-training process from ROCKET, but also provides insight into seizure detection by retaining only the helpful channels. The extreme gradient boosting (XGboost) classifier was combined with this EEG feature learning model to build a comprehensive seizure detection system that achieved promising epoch-based results, with over 90% of both sensitivity and specificity on the scalp and intracranial EEG databases. The experimental comparisons showed that the proposed method outperformed other state-of-the-art methods for cross-patient and patient-specific seizure detection.
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Affiliation(s)
- Yasheng Liu
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250358, P. R. China
| | - Yonghui Jiang
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250358, P. R. China
| | - Jie Liu
- Department of Pediatric Intensive Care Unit, Shandong Provincial Maternal and Child Health Care Hospital, Affiliated to Qingdao University, Jinan 250014, P. R. China
| | - Jie Li
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250358, P. R. China
| | - Mingze Liu
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250358, P. R. China
| | - Weiwei Nie
- The First Affiliated Hospital of Shandong First Medical University, Shandong First Medical University, Jinan 250014, P. R. China
| | - Qi Yuan
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250358, P. R. China
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Díaz-Francés JÁ, Fernández-Rodríguez JD, Thurnhofer-Hemsi K, López-Rubio E. Semi-Supervised Semantic Image Segmentation by Deep Diffusion Models and Generative Adversarial Networks. Int J Neural Syst 2024; 34:2450057. [PMID: 39155691 DOI: 10.1142/s0129065724500576] [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: 08/20/2024]
Abstract
Typically, deep learning models for image segmentation tasks are trained using large datasets of images annotated at the pixel level, which can be expensive and highly time-consuming. A way to reduce the amount of annotated images required for training is to adopt a semi-supervised approach. In this regard, generative deep learning models, concretely Generative Adversarial Networks (GANs), have been adapted to semi-supervised training of segmentation tasks. This work proposes MaskGDM, a deep learning architecture combining some ideas from EditGAN, a GAN that jointly models images and their segmentations, together with a generative diffusion model. With careful integration, we find that using a generative diffusion model can improve EditGAN performance results in multiple segmentation datasets, both multi-class and with binary labels. According to the quantitative results obtained, the proposed model improves multi-class image segmentation when compared to the EditGAN and DatasetGAN models, respectively, by [Formula: see text] and [Formula: see text]. Moreover, using the ISIC dataset, our proposal improves the results from other models by up to [Formula: see text] for the binary image segmentation approach.
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Affiliation(s)
- José Ángel Díaz-Francés
- ITIS Software, University of Málaga, Calle Arquitecto Francisco Peñalosa 18, Málaga 29010, Spain
| | | | - Karl Thurnhofer-Hemsi
- ITIS Software, University of Málaga, Calle Arquitecto Francisco Peñalosa 18, Málaga 29010, Spain
| | - Ezequiel López-Rubio
- ITIS Software, University of Málaga, Calle Arquitecto Francisco Peñalosa 18, Málaga 29010, Spain
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5
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Chen W, Yu Z, Yang C, Lu Y. Abnormal Behavior Recognition Based on 3D Dense Connections. Int J Neural Syst 2024; 34:2450049. [PMID: 39010725 DOI: 10.1142/s0129065724500497] [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: 07/17/2024]
Abstract
Abnormal behavior recognition is an important technology used to detect and identify activities or events that deviate from normal behavior patterns. It has wide applications in various fields such as network security, financial fraud detection, and video surveillance. In recent years, Deep Convolution Networks (ConvNets) have been widely applied in abnormal behavior recognition algorithms and have achieved significant results. However, existing abnormal behavior detection algorithms mainly focus on improving the accuracy of the algorithms and have not explored the real-time nature of abnormal behavior recognition. This is crucial to quickly identify abnormal behavior in public places and improve urban public safety. Therefore, this paper proposes an abnormal behavior recognition algorithm based on three-dimensional (3D) dense connections. The proposed algorithm uses a multi-instance learning strategy to classify various types of abnormal behaviors, and employs dense connection modules and soft-threshold attention mechanisms to reduce the model's parameter count and enhance network computational efficiency. Finally, redundant information in the sequence is reduced by attention allocation to mitigate its negative impact on recognition results. Experimental verification shows that our method achieves a recognition accuracy of 95.61% on the UCF-crime dataset. Comparative experiments demonstrate that our model has strong performance in terms of recognition accuracy and speed.
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Affiliation(s)
- Wei Chen
- School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, P. R. China
| | - Zhanhe Yu
- School of Information Science and Technology, North China University of Technology, Beijing 100144, P. R. China
| | - Chaochao Yang
- School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, P. R. China
| | - Yuanyao Lu
- School of Information Science and Technology, North China University of Technology, Beijing 100144, P. R. China
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6
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Jiang P, Neri F, Xue Y, Maulik U. A Generalized Attention Mechanism to Enhance the Accuracy Performance of Neural Networks. Int J Neural Syst 2024:2450063. [PMID: 39212940 DOI: 10.1142/s0129065724500631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
In many modern machine learning (ML) models, attention mechanisms (AMs) play a crucial role in processing data and identifying significant parts of the inputs, whether these are text or images. This selective focus enables subsequent stages of the model to achieve improved classification performance. Traditionally, AMs are applied as a preprocessing substructure before a neural network, such as in encoder/decoder architectures. In this paper, we extend the application of AMs to intermediate stages of data propagation within ML models. Specifically, we propose a generalized attention mechanism (GAM), which can be integrated before each layer of a neural network for classification tasks. The proposed GAM allows for at each layer/step of the ML architecture identification of the most relevant sections of the intermediate results. Our experimental results demonstrate that incorporating the proposed GAM into various ML models consistently enhances the accuracy of these models. This improvement is achieved with only a marginal increase in the number of parameters, which does not significantly affect the training time.
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Affiliation(s)
- Pengcheng Jiang
- School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China
| | - Ferrante Neri
- NICE Research Group, School of Computer Science and Electronic Engineering, University of Surrey, Guildford GU2 7XS, UK
| | - Yu Xue
- School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China
| | - Ujjwal Maulik
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
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7
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Pleşa MI, Gheorghe M, Ipate F, Zhang G. A Federated Learning Protocol for Spiking Neural Membrane Systems. Int J Neural Syst 2024:2450062. [PMID: 39212939 DOI: 10.1142/s012906572450062x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Although deep learning models have shown promising results in solving problems related to image recognition or natural language processing, they do not match how the biological brain works. Some of the differences include the amount of energy consumed, the way neurons communicate, or the way they learn. To close the gap between artificial neural networks and biological ones, researchers proposed the spiking neural network. Layered Spiking Neural P systems (LSN P systems) are networks of spiking neurons used to solve various classification problems. In this paper, we study the LSN P systems in the context of a federated learning client-server architecture over horizontally partitioned data. We analyze the privacy implications of pre-trained LSN P systems through membership inference attacks. We also perform experiments to assess the performance of an LSN P system trained in the federated learning setup. Our findings suggest that LSN P systems demonstrate higher accuracy and faster convergence compared to federated algorithms based on either perceptron or spiking neural networks.
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Affiliation(s)
| | - Marian Gheorghe
- School of Electrical Engineering and Computer Science, University of Bradford, Bradford, UK
| | - Florentin Ipate
- Department of Computer Science, University of Bucharest, Bucharest, Romania
| | - Gexiang Zhang
- School of Automation, Chengdu University of Information Technology, Chengdu 610225, P. R. China
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8
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Martinez-Murcia FJ, Arco JE, Jimenez-Mesa C, Segovia F, Illan IA, Ramirez J, Gorriz JM. Bridging Imaging and Clinical Scores in Parkinson's Progression via Multimodal Self-Supervised Deep Learning. Int J Neural Syst 2024; 34:2450043. [PMID: 38770651 DOI: 10.1142/s0129065724500436] [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: 05/22/2024]
Abstract
Neurodegenerative diseases pose a formidable challenge to medical research, demanding a nuanced understanding of their progressive nature. In this regard, latent generative models can effectively be used in a data-driven modeling of different dimensions of neurodegeneration, framed within the context of the manifold hypothesis. This paper proposes a joint framework for a multi-modal, common latent generative model to address the need for a more comprehensive understanding of the neurodegenerative landscape in the context of Parkinson's disease (PD). The proposed architecture uses coupled variational autoencoders (VAEs) to joint model a common latent space to both neuroimaging and clinical data from the Parkinson's Progression Markers Initiative (PPMI). Alternative loss functions, different normalization procedures, and the interpretability and explainability of latent generative models are addressed, leading to a model that was able to predict clinical symptomatology in the test set, as measured by the unified Parkinson's disease rating scale (UPDRS), with R2 up to 0.86 for same-modality and 0.441 cross-modality (using solely neuroimaging). The findings provide a foundation for further advancements in the field of clinical research and practice, with potential applications in decision-making processes for PD. The study also highlights the limitations and capabilities of the proposed model, emphasizing its direct interpretability and potential impact on understanding and interpreting neuroimaging patterns associated with PD symptomatology.
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Affiliation(s)
- Francisco J Martinez-Murcia
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Center for Advanced Studies, Ludwig-Maximilien Universität München, München, Germany
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Juan Eloy Arco
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Carmen Jimenez-Mesa
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Fermin Segovia
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Ignacio A Illan
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Javier Ramirez
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Juan Manuel Gorriz
- Department of Signal Processing, Networking and Communications, University of Granada, Granada, Spain
- Center for Advanced Studies, Ludwig-Maximilien Universität München, München, Germany
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
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9
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Zheng X, Yang Y, Li D, Deng Y, Xie Y, Yi Z, Ma L, Xu L. Precise Localization for Anatomo-Physiological Hallmarks of the Cervical Spine by Using Neural Memory Ordinary Differential Equation. Int J Neural Syst 2024:2450056. [PMID: 39049777 DOI: 10.1142/s0129065724500564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
In the evaluation of cervical spine disorders, precise positioning of anatomo-physiological hallmarks is fundamental for calculating diverse measurement metrics. Despite the fact that deep learning has achieved impressive results in the field of keypoint localization, there are still many limitations when facing medical image. First, these methods often encounter limitations when faced with the inherent variability in cervical spine datasets, arising from imaging factors. Second, predicting keypoints for only 4% of the entire X-ray image surface area poses a significant challenge. To tackle these issues, we propose a deep neural network architecture, NF-DEKR, specifically tailored for predicting keypoints in cervical spine physiological anatomy. Leveraging neural memory ordinary differential equation with its distinctive memory learning separation and convergence to a singular global attractor characteristic, our design effectively mitigates inherent data variability. Simultaneously, we introduce a Multi-Resolution Focus module to preprocess feature maps before entering the disentangled regression branch and the heatmap branch. Employing a differentiated strategy for feature maps of varying scales, this approach yields more accurate predictions of densely localized keypoints. We construct a medical dataset, SCUSpineXray, comprising X-ray images annotated by orthopedic specialists and conduct similar experiments on the publicly available UWSpineCT dataset. Experimental results demonstrate that compared to the baseline DEKR network, our proposed method enhances average precision by 2% to 3%, accompanied by a marginal increase in model parameters and the floating-point operations (FLOPs). The code (https://github.com/Zhxyi/NF-DEKR) is available.
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Affiliation(s)
- Xi Zheng
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
| | - Yi Yang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Road, Chengdu 610041, P. R. China
| | - Dehan Li
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
| | - Yi Deng
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Road, Chengdu 610041, P. R. China
| | - Yuexiong Xie
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
| | - Zhang Yi
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
| | - Litai Ma
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Road, Chengdu 610041, P. R. China
| | - Lei Xu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
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10
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Luosang G, Wang Z, Liu J, Zeng F, Yi Z, Wang J. Automated Quality Assessment of Medical Images in Echocardiography Using Neural Networks with Adaptive Ranking and Structure-Aware Learning. Int J Neural Syst 2024:2450054. [PMID: 38984421 DOI: 10.1142/s0129065724500540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
The quality of medical images is crucial for accurately diagnosing and treating various diseases. However, current automated methods for assessing image quality are based on neural networks, which often focus solely on pixel distortion and overlook the significance of complex structures within the images. This study introduces a novel neural network model designed explicitly for automated image quality assessment that addresses pixel and semantic distortion. The model introduces an adaptive ranking mechanism enhanced with contrast sensitivity weighting to refine the detection of minor variances in similar images for pixel distortion assessment. More significantly, the model integrates a structure-aware learning module employing graph neural networks. This module is adept at deciphering the intricate relationships between an image's semantic structure and quality. When evaluated on two ultrasound imaging datasets, the proposed method outshines existing leading models in performance. Additionally, it boasts seamless integration into clinical workflows, enabling real-time image quality assessment, crucial for precise disease diagnosis and treatment.
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Affiliation(s)
- Gadeng Luosang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
- College of Information Science and Technology, Tibet University, Lhasa 850000, P. R. China
| | - Zhihua Wang
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, P. R. China
- Anhui Kunlong Kangxin Medical, Technology Company Limited, Anhui 230000, P. R. China
| | - Jian Liu
- Department of Ultrasound, Clinical Medical College, The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, P. R. China
| | - Fanxin Zeng
- Department of Clinical Research Center, Dazhou Central Hospital, Sichuan 635099, P. R. China
| | - Zhang Yi
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
| | - Jianyong Wang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
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11
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Xu Y, Yu Z, Li Y, Liu Y, Li Y, Wang Y. Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN-LSTM model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108196. [PMID: 38678958 DOI: 10.1016/j.cmpb.2024.108196] [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: 08/12/2023] [Revised: 01/30/2024] [Accepted: 04/21/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND AND OBJECTIVE People with autism spectrum disorder (ASD) often have cognitive impairments. Effective connectivity between different areas of the brain is essential for normal cognition. Electroencephalography (EEG) has been widely used in the detection of neurological diseases. Previous studies on detecting ASD with EEG data have focused on frequency-related features. Most of these studies have augmented data by splitting the dataset into time slices or sliding windows. However, such approaches to data augmentation may cause the testing data to be contaminated by the training data. To solve this problem, this study developed a novel method for detecting ASD with EEG data. METHODS This study quantified the functional connectivity of the subject's brain from EEG signals and defined the individual to be the unit of analysis. Publicly available EEG data were gathered from 97 and 92 subjects with ASD and typical development (TD), respectively, while they were at rest or performing a task. Time-series maps of brain functional connectivity were constructed, and the data were augmented using a deep convolutional generative adversarial network. In addition, a combined network for ASD detection, based on convolutional neural network (CNN) and long short-term memory (LSTM), was designed and implemented. RESULTS Based on functional connectivity, the network achieved classification accuracies of 81.08% and 74.55% on resting state and task state data, respectively. In addition, we found that the functional connectivity of ASD differed from TD primarily in the short-distance functional connectivity of the parietal and occipital lobes and in the distant connections from the right temporoparietal junction region to the left posterior temporal lobe. CONCLUSIONS This paper provides a new perspective for better utilizing EEG to understand ASD. The method proposed in our study is expected to be a reliable tool to assist in the diagnosis of ASD.
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Affiliation(s)
- Yongjie Xu
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zengjie Yu
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yisheng Li
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yuehan Liu
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ye Li
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yishan Wang
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
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12
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Fernandez-Bermejo J, Martinez-Del-Rincon J, Dorado J, Toro XD, Santofimia MJ, Lopez JC. Edge Computing Transformers for Fall Detection in Older Adults. Int J Neural Syst 2024; 34:2450026. [PMID: 38490957 DOI: 10.1142/s0129065724500266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
The global trend of increasing life expectancy introduces new challenges with far-reaching implications. Among these, the risk of falls among older adults is particularly significant, affecting individual health and the quality of life, and placing an additional burden on healthcare systems. Existing fall detection systems often have limitations, including delays due to continuous server communication, high false-positive rates, low adoption rates due to wearability and comfort issues, and high costs. In response to these challenges, this work presents a reliable, wearable, and cost-effective fall detection system. The proposed system consists of a fit-for-purpose device, with an embedded algorithm and an Inertial Measurement Unit (IMU), enabling real-time fall detection. The algorithm combines a Threshold-Based Algorithm (TBA) and a neural network with low number of parameters based on a Transformer architecture. This system demonstrates notable performance with 95.29% accuracy, 93.68% specificity, and 96.66% sensitivity, while only using a 0.38% of the trainable parameters used by the other approach.
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Affiliation(s)
- Jesús Fernandez-Bermejo
- Faculty of Social Science and Information Technology, University of Castilla-La Mancha, 45600 Talavera de la Reina, Toledo, Spain
| | - Jesús Martinez-Del-Rincon
- The Centre for Secure Information Technologies (CSIT), Institute of Electronics, Communications & Information Technology, Queen's University of Belfast, Belfast BT3 9DT, UK
| | - Javier Dorado
- School of Computer Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Ciudad Real, Spain
| | - Xavier Del Toro
- School of Computer Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Ciudad Real, Spain
| | - María J Santofimia
- School of Computer Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Ciudad Real, Spain
| | - Juan C Lopez
- School of Computer Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Ciudad Real, Spain
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13
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Kanwal A, Javed K, Ali S, Rubab S, Khan MA, Alasiry A, Marzougui M, Shabaz M. A hybrid framework for detection of autism using ConvNeXt-T and embedding clusters. THE JOURNAL OF SUPERCOMPUTING 2024; 80:8156-8178. [DOI: 10.1007/s11227-023-05761-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/26/2023] [Indexed: 08/25/2024]
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14
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Kang E, Heo DW, Lee J, Suk HI. A Learnable Counter-Condition Analysis Framework for Functional Connectivity-Based Neurological Disorder Diagnosis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1377-1387. [PMID: 38019623 DOI: 10.1109/tmi.2023.3337074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc analyses via explainable models to discover disease-related biomarkers. Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately. However, if the results at each stage lack reliability, it can cause misdiagnosis and incorrect analysis in afterward stages. In this study, we propose a novel unified framework that systemically integrates diagnoses (i.e., feature selection and feature extraction) and explanations. Notably, we devised an adaptive attention network as a feature selection approach to identify individual-specific disease-related connections. We also propose a functional network relational encoder that summarizes the global topological properties of FC by learning the inter-network relations without pre-defined edges between functional networks. Last but not least, our framework provides a novel explanatory power for neuroscientific interpretation, also termed counter-condition analysis. We simulated the FC that reverses the diagnostic information (i.e., counter-condition FC): converting a normal brain to be abnormal and vice versa. We validated the effectiveness of our framework by using two large resting-state functional magnetic resonance imaging (fMRI) datasets, Autism Brain Imaging Data Exchange (ABIDE) and REST-meta-MDD, and demonstrated that our framework outperforms other competing methods for disease identification. Furthermore, we analyzed the disease-related neurological patterns based on counter-condition analysis.
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15
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Koehler JC, Dong MS, Song DY, Bong G, Koutsouleris N, Yoo H, Falter-Wagner CM. Classifying autism in a clinical population based on motion synchrony: a proof-of-concept study using real-life diagnostic interviews. Sci Rep 2024; 14:5663. [PMID: 38453972 PMCID: PMC10920641 DOI: 10.1038/s41598-024-56098-y] [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: 07/20/2023] [Accepted: 03/01/2024] [Indexed: 03/09/2024] Open
Abstract
Predictive modeling strategies are increasingly studied as a means to overcome clinical bottlenecks in the diagnostic classification of autism spectrum disorder. However, while some findings are promising in the light of diagnostic marker research, many of these approaches lack the scalability for adequate and effective translation to everyday clinical practice. In this study, our aim was to explore the use of objective computer vision video analysis of real-world autism diagnostic interviews in a clinical sample of children and young individuals in the transition to adulthood to predict diagnosis. Specifically, we trained a support vector machine learning model on interpersonal synchrony data recorded in Autism Diagnostic Observation Schedule (ADOS-2) interviews of patient-clinician dyads. Our model was able to classify dyads involving an autistic patient (n = 56) with a balanced accuracy of 63.4% against dyads including a patient with other psychiatric diagnoses (n = 38). Further analyses revealed no significant associations between our classification metrics with clinical ratings. We argue that, given the above-chance performance of our classifier in a highly heterogeneous sample both in age and diagnosis, with few adjustments this highly scalable approach presents a viable route for future diagnostic marker research in autism.
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Affiliation(s)
- Jana Christina Koehler
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany.
| | - Mark Sen Dong
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Max Planck Institute of Psychiatry, Munich, Germany
| | - Da-Yea Song
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Guiyoung Bong
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Max Planck Institute of Psychiatry, Munich, Germany
- Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Heejeong Yoo
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
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16
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Li N, Xiao J, Mao N, Cheng D, Chen X, Zhao F, Shi Z. Joint learning of multi-level dynamic brain networks for autism spectrum disorder diagnosis. Comput Biol Med 2024; 171:108054. [PMID: 38350396 DOI: 10.1016/j.compbiomed.2024.108054] [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] [Received: 11/15/2023] [Revised: 01/08/2024] [Accepted: 01/26/2024] [Indexed: 02/15/2024]
Abstract
Graph convolutional networks (GCNs), with their powerful ability to model non-Euclidean graph data, have shown advantages in learning representations of brain networks. However, considering the complexity, multilayeredness, and spatio-temporal dynamics of brain activities, we have identified two limitations in current GCN-based research on brain networks: 1) Most studies have focused on unidirectional information transmission across brain network levels, neglecting joint learning or bidirectional information exchange among networks. 2) Most of the existing models determine node neighborhoods by thresholding or simply binarizing the brain network, which leads to the loss of edge weight information and weakens the model's sensitivity to important information in the brain network. To address the above issues, we propose a multi-level dynamic brain network joint learning architecture based on GCN for autism spectrum disorder (ASD) diagnosis. Specifically, firstly, constructing different-level dynamic brain networks. Then, utilizing joint learning based on GCN for interactive information exchange among these multi-level brain networks. Finally, designing an edge self-attention mechanism to assign different edge weights to inter-node connections, which allows us to pick out the crucial features for ASD diagnosis. Our proposed method achieves an accuracy of 81.5 %. The results demonstrate that our method enables bidirectional transfer of high-order and low-order information, facilitating information complementarity between different levels of brain networks. Additionally, the use of edge weights enhances the representation capability of ASD-related features.
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Affiliation(s)
- Na Li
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China; Department of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shanxi, China
| | - Jinjie Xiao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Dapeng Cheng
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Xiaobo Chen
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.
| | - Zhenghao Shi
- Department of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shanxi, China.
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17
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Zhao R, Xie Z, Zhuang Y, L H Yu P. Automated Quality Evaluation of Large-Scale Benchmark Datasets for Vision-Language Tasks. Int J Neural Syst 2024; 34:2450009. [PMID: 38318751 DOI: 10.1142/s0129065724500096] [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: 02/07/2024]
Abstract
Large-scale benchmark datasets are crucial in advancing research within the computer science communities. They enable the development of more sophisticated AI models and serve as "golden" benchmarks for evaluating their performance. Thus, ensuring the quality of these datasets is of utmost importance for academic research and the progress of AI systems. For the emerging vision-language tasks, some datasets have been created and frequently used, such as Flickr30k, COCO, and NoCaps, which typically contain a large number of images paired with their ground-truth textual descriptions. In this paper, an automatic method is proposed to assess the quality of large-scale benchmark datasets designed for vision-language tasks. In particular, a new cross-modal matching model is developed, which is capable of automatically scoring the textual descriptions of visual images. Subsequently, this model is employed to evaluate the quality of vision-language datasets by automatically assigning a score to each 'ground-truth' description for every image picture. With a good agreement between manual and automated scoring results on the datasets, our findings reveal significant disparities in the quality of the ground-truth descriptions included in the benchmark datasets. Even more surprising, it is evident that a small portion of the descriptions are unsuitable for serving as reliable ground-truth references. These discoveries emphasize the need for careful utilization of these publicly accessible benchmark databases.
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Affiliation(s)
- Ruibin Zhao
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, P. R. China
- School of Computer Science and Information Engineering, Chuzhou University, Chuzhou, P. R. China
| | - Zhiwei Xie
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, P. R. China
| | - Yipeng Zhuang
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, P. R. China
| | - Philip L H Yu
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, P. R. China
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18
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Minissi ME, Altozano A, Marín-Morales J, Chicchi Giglioli IA, Mantovani F, Alcañiz M. Biosignal comparison for autism assessment using machine learning models and virtual reality. Comput Biol Med 2024; 171:108194. [PMID: 38428095 DOI: 10.1016/j.compbiomed.2024.108194] [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] [Received: 09/05/2023] [Revised: 02/08/2024] [Accepted: 02/18/2024] [Indexed: 03/03/2024]
Abstract
Clinical assessment procedures encounter challenges in terms of objectivity because they rely on subjective data. Computational psychiatry proposes overcoming this limitation by introducing biosignal-based assessments able to detect clinical biomarkers, while virtual reality (VR) can offer ecological settings for measurement. Autism spectrum disorder (ASD) is a neurodevelopmental disorder where many biosignals have been tested to improve assessment procedures. However, in ASD research there is a lack of studies systematically comparing biosignals for the automatic classification of ASD when recorded simultaneously in ecological settings, and comparisons among previous studies are challenging due to methodological inconsistencies. In this study, we examined a VR screening tool consisting of four virtual scenes, and we compared machine learning models based on implicit (motor skills and eye movements) and explicit (behavioral responses) biosignals. Machine learning models were developed for each biosignal within the virtual scenes and then combined into a final model per biosignal. A linear support vector classifier with recursive feature elimination was used and tested using nested cross-validation. The final model based on motor skills exhibited the highest robustness in identifying ASD, achieving an AUC of 0.89 (SD = 0.08). The best behavioral model showed an AUC of 0.80, while further research is needed for the eye-movement models due to limitations with the eye-tracking glasses. These findings highlight the potential of motor skills in enhancing objectivity and reliability in the early assessment of ASD compared to other biosignals.
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Affiliation(s)
- Maria Eleonora Minissi
- Instituto Universitario de Investigación en Tecnología Centrada en El Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain.
| | - Alberto Altozano
- Instituto Universitario de Investigación en Tecnología Centrada en El Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain
| | - Javier Marín-Morales
- Instituto Universitario de Investigación en Tecnología Centrada en El Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain
| | - Irene Alice Chicchi Giglioli
- Instituto Universitario de Investigación en Tecnología Centrada en El Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain
| | - Fabrizia Mantovani
- Centre for Studies in Communication Sciences "Luigi Anolli" (CESCOM), Department of Human Sciences for Education ''Riccardo Massa'', University of Milano - Bicocca, Building U16, Via Tomas Mann, 20162, Milan, Italy
| | - Mariano Alcañiz
- Instituto Universitario de Investigación en Tecnología Centrada en El Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain
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19
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Zhu H, Xu Y, Wu Y, Shen N, Wang L, Chen C, Chen W. A Sequential End-to-End Neonatal Sleep Staging Model with Squeeze and Excitation Blocks and Sequential Multi-Scale Convolution Neural Networks. Int J Neural Syst 2024; 34:2450013. [PMID: 38369905 DOI: 10.1142/s0129065724500138] [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: 02/20/2024]
Abstract
Automatic sleep staging offers a quick and objective assessment for quantitatively interpreting sleep stages in neonates. However, most of the existing studies either do not encompass any temporal information, or simply apply neural networks to exploit temporal information at the expense of high computational overhead and modeling ambiguity. This limits the application of these methods to multiple scenarios. In this paper, a sequential end-to-end sleep staging model, SeqEESleepNet, which is competent for parallelly processing sequential epochs and has a fast training rate to adapt to different scenarios, is proposed. SeqEESleepNet consists of a sequence epoch generation (SEG) module, a sequential multi-scale convolution neural network (SMSCNN) and squeeze and excitation (SE) blocks. The SEG module expands independent epochs into sequential signals, enabling the model to learn the temporal information between sleep stages. SMSCNN is a multi-scale convolution neural network that can extract both multi-scale features and temporal information from the signal. Subsequently, the followed SE block can reassign the weights of features through mapping and pooling. Experimental results exhibit that in a clinical dataset, the proposed method outperforms the state-of-the-art approaches, achieving an overall accuracy, F1-score, and Kappa coefficient of 71.8%, 71.8%, and 0.684 on a three-class classification task with a single channel EEG signal. Based on our overall results, we believe the proposed method could pave the way for convenient multi-scenario neonatal sleep staging methods.
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Affiliation(s)
- Hangyu Zhu
- Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, P. R. China
| | - Yan Xu
- Department of Neurology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, P. R. China
| | - Yonglin Wu
- Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, P. R. China
| | - Ning Shen
- Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, P. R. China
| | - Laishuan Wang
- Department of Neurology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, P. R. China
| | - Chen Chen
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Shanghai 201203, P. R. China
| | - Wei Chen
- Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, P. R. China
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20
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Valizadeh A, Moassefi M, Nakhostin-Ansari A, Heidari Some'eh S, Hosseini-Asl H, Saghab Torbati M, Aghajani R, Maleki Ghorbani Z, Menbari-Oskouie I, Aghajani F, Mirzamohamadi A, Ghafouri M, Faghani S, Memari AH. Automated diagnosis of autism with artificial intelligence: State of the art. Rev Neurosci 2024; 35:141-163. [PMID: 37678819 DOI: 10.1515/revneuro-2023-0050] [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] [Received: 04/26/2023] [Accepted: 07/28/2023] [Indexed: 09/09/2023]
Abstract
Autism spectrum disorder (ASD) represents a panel of conditions that begin during the developmental period and result in impairments of personal, social, academic, or occupational functioning. Early diagnosis is directly related to a better prognosis. Unfortunately, the diagnosis of ASD requires a long and exhausting subjective process. We aimed to review the state of the art for automated autism diagnosis and recognition in this research. In February 2022, we searched multiple databases and sources of gray literature for eligible studies. We used an adapted version of the QUADAS-2 tool to assess the risk of bias in the studies. A brief report of the methods and results of each study is presented. Data were synthesized for each modality separately using the Split Component Synthesis (SCS) method. We assessed heterogeneity using the I 2 statistics and evaluated publication bias using trim and fill tests combined with ln DOR. Confidence in cumulative evidence was assessed using the GRADE approach for diagnostic studies. We included 344 studies from 186,020 participants (51,129 are estimated to be unique) for nine different modalities in this review, from which 232 reported sufficient data for meta-analysis. The area under the curve was in the range of 0.71-0.90 for all the modalities. The studies on EEG data provided the best accuracy, with the area under the curve ranging between 0.85 and 0.93. We found that the literature is rife with bias and methodological/reporting flaws. Recommendations are provided for future research to provide better studies and fill in the current knowledge gaps.
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Affiliation(s)
- Amir Valizadeh
- Neuroscience Institute, Tehran University of Medical Sciences, PO: 1419733141, Tehran, Iran
| | - Mana Moassefi
- Neuroscience Institute, Tehran University of Medical Sciences, PO: 1419733141, Tehran, Iran
| | - Amin Nakhostin-Ansari
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
| | - Soheil Heidari Some'eh
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Hossein Hosseini-Asl
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | | | - Reyhaneh Aghajani
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Zahra Maleki Ghorbani
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Iman Menbari-Oskouie
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
| | - Faezeh Aghajani
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Research Development Center, Arash Women's Hospital, Tehran University of Medical Sciences, PO: 14695542, Tehran, Iran
| | - Alireza Mirzamohamadi
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Mohammad Ghafouri
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
| | - Shahriar Faghani
- Shariati Hospital, Department of Radiology, Tehran University of Medical Sciences, PO: 1411713135, Tehran, Iran
- Interdisciplinary Neuroscience Research Program (INRP), Tehran University of Medical Sciences, PO: 1416634793, Tehran, Iran
| | - Amir Hossein Memari
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
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21
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Zhang X, Noah JA, Singh R, McPartland JC, Hirsch J. Support vector machine prediction of individual Autism Diagnostic Observation Schedule (ADOS) scores based on neural responses during live eye-to-eye contact. Sci Rep 2024; 14:3232. [PMID: 38332184 PMCID: PMC10853508 DOI: 10.1038/s41598-024-53942-z] [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: 08/31/2023] [Accepted: 02/06/2024] [Indexed: 02/10/2024] Open
Abstract
Social difficulties during interactions with others are central to autism spectrum disorder (ASD). Understanding the links between these social difficulties and their underlying neural processes is a primary aim focused on improved diagnosis and treatment. In keeping with this goal, we have developed a multivariate classification method based on neural data acquired by functional near infrared spectroscopy, fNIRS, during live eye-to-eye contact with adults who were either typically developed (TD) or individuals with ASD. The ASD diagnosis was based on the gold-standard Autism Diagnostic Observation Schedule (ADOS) which also provides an index of symptom severity. Using a nested cross-validation method, a support vector machine (SVM) was trained to discriminate between ASD and TD groups based on the neural responses during eye-to-eye contact. ADOS scores were not applied in the classification training. To test the hypothesis that SVM identifies neural activity patterns related to one of the neural mechanisms underlying the behavioral symptoms of ASD, we determined the correlation coefficient between the SVM scores and the individual ADOS scores. Consistent with the hypothesis, the correlation between observed and predicted ADOS scores was 0.72 (p < 0.002). Findings suggest that multivariate classification methods combined with the live interaction paradigm of eye-to-eye contact provide a promising approach to link neural processes and social difficulties in individuals with ASD.
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Affiliation(s)
- Xian Zhang
- Brain Function Laboratory, Department of Psychiatry, Yale School of Medicine, 300 George St., Suite 902, New Haven, CT, USA
| | - J Adam Noah
- Brain Function Laboratory, Department of Psychiatry, Yale School of Medicine, 300 George St., Suite 902, New Haven, CT, USA
| | - Rahul Singh
- Brain Function Laboratory, Department of Psychiatry, Yale School of Medicine, 300 George St., Suite 902, New Haven, CT, USA
- Wu Tsai Institute, Yale University New Haven, New Haven, CT, 06511, USA
| | - James C McPartland
- Yale Child Study Center, Nieson Irving Harris Building, 230 South Frontage Road, Floor G, Suite 100A, New Haven, CT, 06519, USA
- Center for Brain and Mind Health, Yale School of Medicine, New Haven, CT, 06511, USA
| | - Joy Hirsch
- Brain Function Laboratory, Department of Psychiatry, Yale School of Medicine, 300 George St., Suite 902, New Haven, CT, USA.
- Wu Tsai Institute, Yale University New Haven, New Haven, CT, 06511, USA.
- Center for Brain and Mind Health, Yale School of Medicine, New Haven, CT, 06511, USA.
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, 06511, USA.
- Department of Comparative Medicine, Yale School of Medicine, New Haven, CT, 06511, USA.
- Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, UK.
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22
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Colonnese F, Di Luzio F, Rosato A, Panella M. Bimodal Feature Analysis with Deep Learning for Autism Spectrum Disorder Detection. Int J Neural Syst 2024; 34:2450005. [PMID: 38063381 DOI: 10.1142/s0129065724500059] [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: 01/27/2024]
Abstract
Autism Spectrum Disorder (ASD) is a complex and heterogeneous neurodevelopmental disorder which affects a significant proportion of the population, with estimates suggesting that about 1 in 100 children worldwide are affected by ASD. This study introduces a new Deep Neural Network for identifying ASD in children through gait analysis, using features extracted from frames composing video recordings of their walking patterns. The innovative method presented herein is based on imagery and combines gait analysis and deep learning, offering a noninvasive and objective assessment of neurodevelopmental disorders while delivering high accuracy in ASD detection. Our model proposes a bimodal approach based on the concatenation of two distinct Convolutional Neural Networks processing two feature sets extracted from the same videos. The features obtained from the convolutions of both networks are subsequently flattened and merged into a single vector, serving as input for the fully connected layers in the binary classification process. This approach demonstrates the potential for effective ASD detection in children through the combination of gait analysis and deep learning techniques.
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Affiliation(s)
- Federica Colonnese
- Department of Information Engineering, Electronics and Telecommunications, University of Rome "La Sapienza" Via Eudossiana 18, 00184 Rome, Italy
| | - Francesco Di Luzio
- Department of Information Engineering, Electronics and Telecommunications, University of Rome "La Sapienza" Via Eudossiana 18, 00184 Rome, Italy
| | - Antonello Rosato
- Department of Information Engineering, Electronics and Telecommunications, University of Rome "La Sapienza" Via Eudossiana 18, 00184 Rome, Italy
| | - Massimo Panella
- Department of Information Engineering, Electronics and Telecommunications, University of Rome "La Sapienza" Via Eudossiana 18, 00184 Rome, Italy
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23
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Greco C, Raimo G, Amorese T, Cuciniello M, Mcconvey G, Cordasco G, Faundez-Zanuy M, Vinciarelli A, Callejas-Carrion Z, Esposito A. Discriminative Power of Handwriting and Drawing Features in Depression. Int J Neural Syst 2024; 34:2350069. [PMID: 38009869 DOI: 10.1142/s0129065723500697] [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: 11/29/2023]
Abstract
This study contributes knowledge on the detection of depression through handwriting/drawing features, to identify quantitative and noninvasive indicators of the disorder for implementing algorithms for its automatic detection. For this purpose, an original online approach was adopted to provide a dynamic evaluation of handwriting/drawing performance of healthy participants with no history of any psychiatric disorders ([Formula: see text]), and patients with a clinical diagnosis of depression ([Formula: see text]). Both groups were asked to complete seven tasks requiring either the writing or drawing on a paper while five handwriting/drawing features' categories (i.e. pressure on the paper, time, ductus, space among characters, and pen inclination) were recorded by using a digitalized tablet. The collected records were statistically analyzed. Results showed that, except for pressure, all the considered features, successfully discriminate between depressed and nondepressed subjects. In addition, it was observed that depression affects different writing/drawing functionalities. These findings suggest the adoption of writing/drawing tasks in the clinical practice as tools to support the current depression detection methods. This would have important repercussions on reducing the diagnostic times and treatment formulation.
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Affiliation(s)
- Claudia Greco
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
| | - Gennaro Raimo
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
| | - Terry Amorese
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
| | - Marialucia Cuciniello
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
| | - Gavin Mcconvey
- Action Mental Health, 27 Jubilee Rd, BT23 4YH, Newtownards, UK
| | - Gennaro Cordasco
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
| | - Marcos Faundez-Zanuy
- Tecnocampus Universitat Pompeu Fabra, Carrer d'Ernest Lluch 32 Mataro, Barcelona 08302, Spain
| | - Alessandro Vinciarelli
- University of Glasgow, School of Computing Science, 18 Lilybank Gardens Glasgow, G12,8RZ, Scotland
| | - Zoraida Callejas-Carrion
- Department of Languages and Computer Systems, Universidad de Granada, Periodista Daniel Saucedo Aranda Granada, 18071, Spain
| | - Anna Esposito
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
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24
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Menaka R, Karthik R, Saranya S, Niranjan M, Kabilan S. An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG. Clin EEG Neurosci 2024; 55:43-51. [PMID: 37246419 DOI: 10.1177/15500594231178274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Autism is a neurodevelopmental disorder that cannot be completely cured, but early intervention during childhood can improve outcomes. Identifying autism spectrum disorder (ASD) has relied on subjective detection methods that involve questionnaires, medical professionals, and therapists and are subject to observer variability. The need for early diagnosis and the limitations of subjective detection methods has led researchers to explore machine learning-based approaches, such as Random Forests, K-Nearest Neighbors, Naive Bayes, and Support Vector Machines, to predict ASD meltdowns. In recent years, deep learning techniques have gained traction for early ASD detection. This study evaluates the performance of various deep learning networks, including AlexNet, VGG16, and ResNet50, using 5 cepstral coefficient features for ASD detection. The main contributions of this study are the utilization of Cepstral Coefficients in the processing stage to construct spectrograms and the modification of the AlexNet architecture for precise classification. Experimental observations indicate that the AlexNet with Linear Frequency Cepstral Coefficients (LFCC) yields the highest accuracy of 85.1%, while a customized AlexNet with LFCC achieves 90% accuracy.
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Affiliation(s)
- R Menaka
- Centre for Cyber Physical Systems, School of Electronics Engineering, Vellore Institute of Technology, Chennai, India
| | - R Karthik
- Centre for Cyber Physical Systems, School of Electronics Engineering, Vellore Institute of Technology, Chennai, India
| | - S Saranya
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, India
| | - M Niranjan
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, India
| | - S Kabilan
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, India
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25
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Alharthi AG, Alzahrani SM. Do it the transformer way: A comprehensive review of brain and vision transformers for autism spectrum disorder diagnosis and classification. Comput Biol Med 2023; 167:107667. [PMID: 37939407 DOI: 10.1016/j.compbiomed.2023.107667] [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] [Received: 06/17/2023] [Revised: 10/25/2023] [Accepted: 10/31/2023] [Indexed: 11/10/2023]
Abstract
Autism spectrum disorder (ASD) is a condition observed in children who display abnormal patterns of interaction, behavior, and communication with others. Despite extensive research efforts, the underlying causes of this neurodevelopmental disorder and its biomarkers remain unknown. However, advancements in artificial intelligence and machine learning have improved clinicians' ability to diagnose ASD. This review paper investigates various MRI modalities to identify distinct features that characterize individuals with ASD compared to typical control subjects. The review then moves on to explore deep learning models for ASD diagnosis, including convolutional neural networks (CNNs), autoencoders, graph convolutions, attention networks, and other models. CNNs and their variations are particularly effective due to their capacity to learn structured image representations and identify reliable biomarkers for brain disorders. Computer vision transformers often employ CNN architectures with transfer learning techniques like fine-tuning and layer freezing to enhance image classification performance, surpassing traditional machine learning models. This review paper contributes in three main ways. Firstly, it provides a comprehensive overview of a recommended architecture for using vision transformers in the systematic ASD diagnostic process. To this end, the paper investigates various pre-trained vision architectures such as VGG, ResNet, Inception, InceptionResNet, DenseNet, and Swin models that were fine-tuned for ASD diagnosis and classification. Secondly, it discusses the vision transformers of 2020th like BiT, ViT, MobileViT, and ConvNeXt, and applying transfer learning methods in relation to their prospective practicality in ASD classification. Thirdly, it explores brain transformers that are pre-trained on medically rich data and MRI neuroimaging datasets. The paper recommends a systematic architecture for ASD diagnosis using brain transformers. It also reviews recently developed brain transformer-based models, such as METAFormer, Com-BrainTF, Brain Network, ST-Transformer, STCAL, BolT, and BrainFormer, discussing their deep transfer learning architectures and results in ASD detection. Additionally, the paper summarizes and discusses brain-related transformers for various brain disorders, such as MSGTN, STAGIN, and MedTransformer, in relation to their potential usefulness in ASD. The study suggests that developing specialized transformer-based models, following the success of natural language processing (NLP), can offer new directions for image classification problems in ASD brain biomarkers learning and classification. By incorporating the attention mechanism, treating MRI modalities as sequence prediction tasks trained on brain disorder classification problems, and fine-tuned on ASD datasets, brain transformers can show a great promise in ASD diagnosis.
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Affiliation(s)
- Asrar G Alharthi
- Department of Computer Science, College of Computers and Information Technology, Taif University, Saudi Arabia.
| | - Salha M Alzahrani
- Department of Computer Science, College of Computers and Information Technology, Taif University, Saudi Arabia
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26
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Hu J, Yu C, Yi Z, Zhang H. Enhancing Robustness of Medical Image Segmentation Model with Neural Memory Ordinary Differential Equation. Int J Neural Syst 2023; 33:2350060. [PMID: 37743765 DOI: 10.1142/s0129065723500600] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Deep neural networks (DNNs) have emerged as a prominent model in medical image segmentation, achieving remarkable advancements in clinical practice. Despite the promising results reported in the literature, the effectiveness of DNNs necessitates substantial quantities of high-quality annotated training data. During experiments, we observe a significant decline in the performance of DNNs on the test set when there exists disruption in the labels of the training dataset, revealing inherent limitations in the robustness of DNNs. In this paper, we find that the neural memory ordinary differential equation (nmODE), a recently proposed model based on ordinary differential equations (ODEs), not only addresses the robustness limitation but also enhances performance when trained by the clean training dataset. However, it is acknowledged that the ODE-based model tends to be less computationally efficient compared to the conventional discrete models due to the multiple function evaluations required by the ODE solver. Recognizing the efficiency limitation of the ODE-based model, we propose a novel approach called the nmODE-based knowledge distillation (nmODE-KD). The proposed method aims to transfer knowledge from the continuous nmODE to a discrete layer, simultaneously enhancing the model's robustness and efficiency. The core concept of nmODE-KD revolves around enforcing the discrete layer to mimic the continuous nmODE by minimizing the KL divergence between them. Experimental results on 18 organs-at-risk segmentation tasks demonstrate that nmODE-KD exhibits improved robustness compared to ODE-based models while also mitigating the efficiency limitation.
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Affiliation(s)
- Junjie Hu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
| | - Chengrong Yu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
| | - Zhang Yi
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
| | - Haixian Zhang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
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27
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Ali MT, Gebreil A, ElNakieb Y, Elnakib A, Shalaby A, Mahmoud A, Sleman A, Giridharan GA, Barnes G, Elbaz AS. A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework. Sci Rep 2023; 13:17048. [PMID: 37813914 PMCID: PMC10562430 DOI: 10.1038/s41598-023-43478-z] [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: 03/09/2023] [Accepted: 09/25/2023] [Indexed: 10/11/2023] Open
Abstract
Autism Spectrum Disorder (ASD) is characterized as a neurodevelopmental disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical presentations. In this study, we dissect Autism Spectrum Disorder (ASD) into its behavioral components, mirroring the diagnostic process used in clinical settings. Morphological features are extracted from magnetic resonance imaging (MRI) scans, found in the publicly available dataset ABIDE II, identifying the most discriminative features that differentiate ASD within various behavioral domains. Then, each subject is categorized as having severe, moderate, or mild ASD, or typical neurodevelopment (TD), based on the behavioral domains of the Social Responsiveness Scale (SRS). Through this study, multiple artificial intelligence (AI) models are utilized for feature selection and classifying each ASD severity and behavioural group. A multivariate feature selection algorithm, investigating four different classifiers with linear and non-linear hypotheses, is applied iteratively while shuffling the training-validation subjects to find the set of cortical regions with statistically significant association with ASD. A set of six classifiers are optimized and trained on the selected set of features using 5-fold cross-validation for the purpose of severity classification for each behavioural group. Our AI-based model achieved an average accuracy of 96%, computed as the mean accuracy across the top-performing AI models for feature selection and severity classification across the different behavioral groups. The proposed AI model has the ability to accurately differentiate between the functionalities of specific brain regions, such as the left and right caudal middle frontal regions. We propose an AI-based model that dissects ASD into behavioral components. For each behavioral component, the AI-based model is capable of identifying the brain regions which are associated with ASD as well as utilizing those regions for diagnosis. The proposed system can increase the speed and accuracy of the diagnostic process and result in improved outcomes for individuals with ASD, highlighting the potential of AI in this area.
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Affiliation(s)
- Mohamed T Ali
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
- UT Southwestern Medical Center, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ahmad Gebreil
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | - Yaser ElNakieb
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
- UT Southwestern Medical Center, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ahmed Elnakib
- Electrical and Computer Engineering, Penn State Erie-The Behrend College, Erie, PA, 16563, USA
| | - Ahmed Shalaby
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | - Ahmed Sleman
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | | | - Gregory Barnes
- Department of Neurology and Pediatric Research Institute, University of Louisville, Louisville, KY, 40202, USA
| | - Ayman S Elbaz
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA.
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28
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You R, He F, Lin W. Decoupled Edge Guidance Network for Automatic Checkout. Int J Neural Syst 2023; 33:2350049. [PMID: 37567859 DOI: 10.1142/s0129065723500491] [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: 08/13/2023]
Abstract
Automatic checkout (ACO) aims at correctly generating complete shopping lists from checkout images. However, the domain gap between the single product in training data and multiple products in checkout images endows ACO tasks with a major difficulty. Despite remarkable advancements in recent years, resolving the significant domain gap remains challenging. It is possibly because networks trained solely on synthesized images may struggle to generalize well to realistic checkout scenarios. To this end, we propose a decoupled edge guidance network (DEGNet), which integrates synthesized and checkout images via a supervised domain adaptation approach and further learns common domain representations using a domain adapter. Specifically, an edge embedding module is designed for generating edge embedding images to introduce edge information. On this basis, we develop a decoupled feature extractor that takes original images and edge embedding images as input to jointly utilize image information and edge information. Furthermore, a novel proposal divide-and-conquer strategy (PDS) is proposed for the purpose of augmenting high-quality samples. Through experimental evaluation, DEGNet achieves state-of-the-art performance on the retail product checkout (RPC) dataset, with checkout accuracy (cAcc) results of 93.47% and 95.25% in the average mode of faster RCNN and cascade RCNN frameworks, respectively. Codes are available at https://github.com/yourbikun/DEGNet.
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Affiliation(s)
- Rongbiao You
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, P. R. China
| | - Fuxiong He
- School of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, P. R. China
| | - Weiming Lin
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, P. R. China
- Fujian Key Laboratory of Communication Network and Information Processing, Xiamen University of Technology, Xiamen 361024, P. R. China
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29
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Saleh H, Amer E, Abuhmed T, Ali A, Al-Fuqaha A, El-Sappagh S. Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data. Sci Rep 2023; 13:16336. [PMID: 37770490 PMCID: PMC10539296 DOI: 10.1038/s41598-023-42796-6] [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] [Received: 03/24/2023] [Accepted: 09/14/2023] [Indexed: 09/30/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia. Early and accurate detection of AD is crucial to plan for disease modifying therapies that could prevent or delay the conversion to sever stages of the disease. As a chronic disease, patient's multivariate time series data including neuroimaging, genetics, cognitive scores, and neuropsychological battery provides a complete profile about patient's status. This data has been used to build machine learning and deep learning (DL) models for the early detection of the disease. However, these models still have limited performance and are not stable enough to be trusted in real medical settings. Literature shows that DL models outperform classical machine learning models, but ensemble learning has proven to achieve better results than standalone models. This study proposes a novel deep stacking framework which combines multiple DL models to accurately predict AD at an early stage. The study uses long short-term memory (LSTM) models as base models over patient's multivariate time series data to learn the deep longitudinal features. Each base LSTM classifier has been optimized using the Bayesian optimizer using different feature sets. As a result, the final optimized ensembled model employed heterogeneous base models that are trained on heterogeneous data. The performance of the resulting ensemble model has been explored using a cohort of 685 patients from the University of Washington's National Alzheimer's Coordinating Center dataset. Compared to the classical machine learning models and base LSTM classifiers, the proposed ensemble model achieves the highest testing results (i.e., 82.02, 82.25, 82.02, and 82.12 for accuracy, precision, recall, and F1-score, respectively). The resulting model enhances the performance of the state-of-the-art literature, and it could be used to build an accurate clinical decision support tool that can assist domain experts for AD progression detection.
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Affiliation(s)
- Hager Saleh
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada, Egypt
| | - Eslam Amer
- Communications and Information Technology, The Institute of Electronics, Queen's University of Belfast, Belfast, UK
| | - Tamer Abuhmed
- Information Laboratory (InfoLab), College of Computing and Informatics, Sungkyunkwan University, Seoul, Suwon, 16419, South Korea.
| | - Amjad Ali
- Information and Computing Technology (ICT) Division, College of Science and Engineering (CSE), Hamad Bin Khalifa University, Doha, Qatar
| | - Ala Al-Fuqaha
- Information and Computing Technology (ICT) Division, College of Science and Engineering (CSE), Hamad Bin Khalifa University, Doha, Qatar
| | - Shaker El-Sappagh
- Information Laboratory (InfoLab), College of Computing and Informatics, Sungkyunkwan University, Seoul, Suwon, 16419, South Korea.
- Faculty of Computer Science and Engineering, Galala University, Suez, 435611, Egypt.
- Faculty of Computers and Artificial Intelligence, Benha University, Banha, 13518, Egypt.
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30
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Washington P, Wall DP. A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism. Annu Rev Biomed Data Sci 2023; 6:211-228. [PMID: 37137169 PMCID: PMC11093217 DOI: 10.1146/annurev-biodatasci-020722-125454] [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] [Indexed: 05/05/2023]
Abstract
Autism spectrum disorder (autism) is a neurodevelopmental delay that affects at least 1 in 44 children. Like many neurological disorder phenotypes, the diagnostic features are observable, can be tracked over time, and can be managed or even eliminated through proper therapy and treatments. However, there are major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking pipelines for autism and related neurodevelopmental delays, creating an opportunity for novel data science solutions to augment and transform existing workflows and provide increased access to services for affected families. Several efforts previously conducted by a multitude of research labs have spawned great progress toward improved digital diagnostics and digital therapies for children with autism. We review the literature on digital health methods for autism behavior quantification and beneficial therapies using data science. We describe both case-control studies and classification systems for digital phenotyping. We then discuss digital diagnostics and therapeutics that integrate machine learning models of autism-related behaviors, including the factors that must be addressed for translational use. Finally, we describe ongoing challenges and potential opportunities for the field of autism data science. Given the heterogeneous nature of autism and the complexities of the relevant behaviors, this review contains insights that are relevant to neurological behavior analysis and digital psychiatry more broadly.
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Affiliation(s)
- Peter Washington
- Department of Information and Computer Sciences, University of Hawai'i at Mānoa, Honolulu, Hawai'i, USA
| | - Dennis P Wall
- Departments of Pediatrics (Systems Medicine), Biomedical Data Science, and Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA;
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31
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Helmy E, Elnakib A, ElNakieb Y, Khudri M, Abdelrahim M, Yousaf J, Ghazal M, Contractor S, Barnes GN, El-Baz A. Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey. Biomedicines 2023; 11:1858. [PMID: 37509498 PMCID: PMC10376963 DOI: 10.3390/biomedicines11071858] [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: 05/26/2023] [Revised: 06/22/2023] [Accepted: 06/27/2023] [Indexed: 07/30/2023] Open
Abstract
Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with social skills, repetitive activities, speech, and nonverbal communication. The Centers for Disease Control (CDC) estimates that 1 in 44 American children currently suffer from ASD. The current gold standard for ASD diagnosis is based on behavior observational tests by clinicians, which suffer from being subjective and time-consuming and afford only late detection (a child must have a mental age of at least two to apply for an observation report). Alternatively, brain imaging-more specifically, magnetic resonance imaging (MRI)-has proven its ability to assist in fast, objective, and early ASD diagnosis and detection. With the recent advances in artificial intelligence (AI) and machine learning (ML) techniques, sufficient tools have been developed for both automated ASD diagnosis and early detection. More recently, the development of deep learning (DL), a young subfield of AI based on artificial neural networks (ANNs), has successfully enabled the processing of brain MRI data with improved ASD diagnostic abilities. This survey focuses on the role of AI in autism diagnostics and detection based on two basic MRI modalities: diffusion tensor imaging (DTI) and functional MRI (fMRI). In addition, the survey outlines the basic findings of DTI and fMRI in autism. Furthermore, recent techniques for ASD detection using DTI and fMRI are summarized and discussed. Finally, emerging tendencies are described. The results of this study show how useful AI is for early, subjective ASD detection and diagnosis. More AI solutions that have the potential to be used in healthcare settings will be introduced in the future.
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Affiliation(s)
- Eman Helmy
- Department of Diagnostic Radiology, Faculty of Medicine, Mansoura University, Elgomheryia Street, Mansoura 3512, Egypt;
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (A.E.); (Y.E.); (M.K.); (M.A.)
| | - Yaser ElNakieb
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (A.E.); (Y.E.); (M.K.); (M.A.)
| | - Mohamed Khudri
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (A.E.); (Y.E.); (M.K.); (M.A.)
| | - Mostafa Abdelrahim
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (A.E.); (Y.E.); (M.K.); (M.A.)
| | - Jawad Yousaf
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (J.Y.); (M.G.)
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (J.Y.); (M.G.)
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA;
| | - Gregory Neal Barnes
- Department of Neurology, Pediatric Research Institute, University of Louisville, Louisville, KY 40202, USA;
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (A.E.); (Y.E.); (M.K.); (M.A.)
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32
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Farooq MS, Tehseen R, Sabir M, Atal Z. Detection of autism spectrum disorder (ASD) in children and adults using machine learning. Sci Rep 2023; 13:9605. [PMID: 37311766 DOI: 10.1038/s41598-023-35910-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 05/25/2023] [Indexed: 06/15/2023] Open
Abstract
Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects. In this article, FL technique has been uniquely applied for autism detection by training two different ML classifiers including logistic regression and support vector machine locally for classification of ASD factors and detection of ASD in children and adults. Due to FL, results obtained from these classifiers have been transmitted to central server where meta classifier is trained to determine which approach is most accurate in the detection of ASD in children and adults. Four different ASD patient datasets, each containing more than 600 records of effected children and adults have been obtained from different repository for features extraction. The proposed model predicted ASD with 98% accuracy (in children) and 81% accuracy (in adults).
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Affiliation(s)
- Muhammad Shoaib Farooq
- Department of Artificial Intelligence, University of Management and Technology, Lahore, 54000, Pakistan
| | - Rabia Tehseen
- Department of Computer Science, University of Central Punjab, Lahore, 54000, Pakistan
| | - Maidah Sabir
- Department of Artificial Intelligence, University of Management and Technology, Lahore, 54000, Pakistan
| | - Zabihullah Atal
- Department of Computer Science, Kardan University, Kabul, 1007, Afghanistan.
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33
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Ling W, Zhao G, Wang W, Wang C, Zhang L, Zhang H, Lu D, Ruan S, Zhang A, Liu Q, Jiang J, Jiang G. Metallomic profiling and natural copper isotopic signatures of childhood autism in serum and red blood cells. CHEMOSPHERE 2023; 330:138700. [PMID: 37076087 DOI: 10.1016/j.chemosphere.2023.138700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 03/29/2023] [Accepted: 04/13/2023] [Indexed: 05/03/2023]
Abstract
Excessive exposure to metals directly threatens human health, including neurodeve lopment. Autism spectrum disorder (ASD) is a neurodevelopmental disorder, leaving great harms to children themselves, their families, and even society. In view of this, it is critical to develop reliable biomarkers for ASD in early childhood. Here we used inductively coupled plasma mass spectrometry (ICP-MS) to identify the abnormalities in ASD-associated metal elements in children blood. Multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) was applied to detect isotopic differences in copper (Cu) for further assessment on account of its core role in the brain. We also developed a machine learning classification method for unknown samples based on a support vector machine (SVM) algorithm. The results indicated significant differences in the blood metallome (chromium (Cr), manganese (Mn), cobalt (Co), magnesium (Mg), and arsenic (As)) between cases and controls, and a significantly lower Zn/Cu ratio was observed in the ASD cases. Interestingly, we found a strong association of serum copper isotopic composition (δ65Cu) with autistic serum. SVM was successfully applied to discriminate cases and controls based on the two-dimensional Cu signatures (Cu concentration and δ65Cu) with a high accuracy (94.4%). Overall, our findings revealed a new biomarker for potential early diagnosis and screening of ASD, and the significant alterations in the blood metallome also helped to understand the potential pathogenesis of ASD in terms of metallomics.
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Affiliation(s)
- Weibo Ling
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Gang Zhao
- Department of Child Health Care, Maternity and Child Healthcare Hospital of Nanshan District, 1 Wanxia Road, Nanshan District, Shenzhen, 518067, China
| | - Weichao Wang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chao Wang
- Shenzhen Center for Disease Control and Prevention, Shenzhen, 518000, China
| | - Luyao Zhang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Huazhou Zhang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Dawei Lu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
| | - Shasha Ruan
- Shenzhen Center for Disease Control and Prevention, Shenzhen, 518000, China
| | - Aiqian Zhang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qian Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Taishan Institute for Ecology and Environment (TIEE), Jinan, 250100, China.
| | - Jie Jiang
- Shenzhen Center for Disease Control and Prevention, Shenzhen, 518000, China.
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China; University of Chinese Academy of Sciences, Beijing, 100049, China
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Ay Ş, Ekinci E, Garip Z. A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of heart-related diseases. THE JOURNAL OF SUPERCOMPUTING 2023; 79:11797-11826. [PMID: 37304052 PMCID: PMC9983547 DOI: 10.1007/s11227-023-05132-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/21/2023] [Indexed: 06/13/2023]
Abstract
This study aims to use a machine learning (ML)-based enhanced diagnosis and survival model to predict heart disease and survival in heart failure by combining the cuckoo search (CS), flower pollination algorithm (FPA), whale optimization algorithm (WOA), and Harris hawks optimization (HHO) algorithms, which are meta-heuristic feature selection algorithms. To achieve this, experiments are conducted on the Cleveland heart disease dataset and the heart failure dataset collected from the Faisalabad Institute of Cardiology published at UCI. CS, FPA, WOA, and HHO algorithms for feature selection are applied for different population sizes and are realized based on the best fitness values. For the original dataset of heart disease, the maximum prediction F-score of 88% is obtained using K-nearest neighbour (KNN) when compared to logistic regression (LR), support vector machine (SVM), Gaussian Naive Bayes (GNB), and random forest (RF). With the proposed approach, the heart disease prediction F-score of 99.72% is obtained using KNN for population sizes 60 with FPA by selecting eight features. For the original dataset of heart failure, the maximum prediction F-score of 70% is obtained using LR and RF compared to SVM, GNB, and KNN. With the proposed approach, the heart failure prediction F-score of 97.45% is obtained using KNN for population sizes 10 with HHO by selecting five features. Experimental findings show that the applied meta-heuristic algorithms with ML algorithms significantly improve prediction performances compared to performances obtained from the original datasets. The motivation of this paper is to select the most critical and informative feature subset through meta-heuristic algorithms to improve classification accuracy.
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Affiliation(s)
- Şevket Ay
- Computer Engineering Department, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, 54187 Turkey
| | - Ekin Ekinci
- Computer Engineering Department, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, 54187 Turkey
| | - Zeynep Garip
- Computer Engineering Department, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, 54187 Turkey
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35
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Zhu H, Wang J, Wang SH, Raman R, Górriz JM, Zhang YD. An Evolutionary Attention-Based Network for Medical Image Classification. Int J Neural Syst 2023; 33:2350010. [PMID: 36655400 DOI: 10.1142/s0129065723500107] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Deep learning has become a primary choice in medical image analysis due to its powerful representation capability. However, most existing deep learning models designed for medical image classification can only perform well on a specific disease. The performance drops dramatically when it comes to other diseases. Generalizability remains a challenging problem. In this paper, we propose an evolutionary attention-based network (EDCA-Net), which is an effective and robust network for medical image classification tasks. To extract task-related features from a given medical dataset, we first propose the densely connected attentional network (DCA-Net) where feature maps are automatically channel-wise weighted, and the dense connectivity pattern is introduced to improve the efficiency of information flow. To improve the model capability and generalizability, we introduce two types of evolution: intra- and inter-evolution. The intra-evolution optimizes the weights of DCA-Net, while the inter-evolution allows two instances of DCA-Net to exchange training experience during training. The evolutionary DCA-Net is referred to as EDCA-Net. The EDCA-Net is evaluated on four publicly accessible medical datasets of different diseases. Experiments showed that the EDCA-Net outperforms the state-of-the-art methods on three datasets and achieves comparable performance on the last dataset, demonstrating good generalizability for medical image classification.
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Affiliation(s)
- Hengde Zhu
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Jian Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Shui-Hua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Rajeev Raman
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Juan M Górriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada 52005, Spain
| | - Yu-Dong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
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36
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Qin X, Niu Y, Zhou H, Li X, Jia W, Zheng Y. Driver Drowsiness EEG Detection Based on Tree Federated Learning and Interpretable Network. Int J Neural Syst 2023; 33:2350009. [PMID: 36655401 DOI: 10.1142/s0129065723500090] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Accurate identification of driver's drowsiness state through Electroencephalogram (EEG) signals can effectively reduce traffic accidents, but EEG signals are usually stored in various clients in the form of small samples. This study attempts to construct an efficient and accurate privacy-preserving drowsiness monitoring system, and proposes a fusion model based on tree Federated Learning (FL) and Convolutional Neural Network (CNN), which can not only identify and explain the driver's drowsiness state, but also integrate the information of different clients under the premise of privacy protection. Each client uses CNN with the Global Average Pooling (GAP) layer and shares model parameters. The tree FL transforms communication relationships into a graph structure, and model parameters are transmitted in parallel along connected branches of the graph. Moreover, the Class Activation Mapping (CAM) is used to find distinctive EEG features for representing specific classes. On EEG data of 11 subjects, it is found that this method has higher average accuracy, F1-score and AUC than the traditional classification method, reaching 73.56%, 73.26% and 78.23%, respectively. Compared with the traditional FL algorithm, this method better protects the driver's privacy and improves communication efficiency.
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Affiliation(s)
- Xue Qin
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, P. R. China
| | - Yi Niu
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, P. R. China
| | - Huiyu Zhou
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK
| | - Xiaojie Li
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, P. R. China
| | - Weikuan Jia
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, P. R. China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, P. R. China
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37
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Maisano R, Foresti GL. A Sentiment Analysis Anomaly Detection System for Cyber Intelligence. Int J Neural Syst 2023; 33:2350003. [PMID: 36585854 DOI: 10.1142/s012906572350003x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Considering the 2030 United Nations intent of world connection, Cyber Intelligence becomes the main area of the human dimension able of inflicting changes in geopolitical dynamics. In cyberspace, the new battlefield is the mind of people including new weapons like abuse of social media with information manipulation, deception by activists and misinformation. In this paper, a Sentiment Analysis system with Anomaly Detection (SAAD) capability is proposed. The system, scalable and modular, uses an OSINT-Deep Learning approach to investigate on social media sentiment in order to predict suspicious anomaly trend in Twitter posts. Anomaly detection is investigated with a new semi-supervised process that is able to detect potentially dangerous situations in critical areas. The main contributions of the paper are the system suitability for working in different areas and domains, the anomaly detection procedure in sentiment context and a time-dependent confusion matrix to address model evaluation with unbalanced dataset. Real experiments and tests were performed on Sahel Region. The detected anomalies in negative sentiment have been checked by experts of Sahel area, proving true links between the models results and real situations observable from the tweets.
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Affiliation(s)
- Roberta Maisano
- Computer Science Centre, University of Messina, Piazza Antonello, 2, 98122 Messina, Italy
| | - Gian Luca Foresti
- Department of Mathematics, Computer Science and Physics, University of Udine, Viale delle Scienze, 206, 33100 Udine, Italy
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38
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Xian R, Lugu R, Peng H, Yang Q, Luo X, Wang J. Edge Detection Method Based on Nonlinear Spiking Neural Systems. Int J Neural Syst 2023; 33:2250060. [PMID: 36328966 DOI: 10.1142/s0129065722500605] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Nonlinear spiking neural P (NSNP) systems are a class of neural-like computational models inspired from the nonlinear mechanism of spiking neurons. NSNP systems have a distinguishing feature: nonlinear spiking mechanism. To handle edge detection of images, this paper proposes a variant, nonlinear spiking neural P (NSNP) systems with two outputs (TO), termed as NSNP-TO systems. Based on NSNP-TO system, an edge detection framework is developed, termed as ED-NSNP detector. The detection ability of ED-NSNP detector relies on two convolutional kernels. To obtain good detection performance, particle swarm optimization (PSO) is used to optimize the parameters of the two convolutional kernels. The proposed ED-NSNP detector is evaluated on several open benchmark images and compared with seven baseline edge detection methods. The comparison results indicate the availability and effectiveness of the proposed ED-NSNP detector.
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Affiliation(s)
- Ronghao Xian
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Rikong Lugu
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Hong Peng
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Qian Yang
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Xiaohui Luo
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Jun Wang
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, P. R. China
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Selcuk Nogay H, Adeli H. Diagnostic of autism spectrum disorder based on structural brain MRI images using, grid search optimization, and convolutional neural networks. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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40
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Zhai J, Li X, Zhou Y, Fan L, Xia W, Wang X, Li Y, Hou M, Wang J, Wu L. Correlation and predictive ability of sensory characteristics and social interaction in children with autism spectrum disorder. Front Psychiatry 2023; 14:1056051. [PMID: 37091701 PMCID: PMC10117963 DOI: 10.3389/fpsyt.2023.1056051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 03/13/2023] [Indexed: 04/25/2023] Open
Abstract
Background Individuals with autism spectrum disorder (ASD) often have different social characteristics and particular sensory processing patterns, and these sensory behaviors may affect their social functioning. The objective of our study is to investigate the sensory profiles of children with ASD and their association with social behavior. Specifically, we aim to identify the predictive role of sensory processing in social functioning. Methods The Short Sensory Profile (SSP) was utilized to analyze sensory differences between ASD children and their peers. The Social Responsiveness Scale (SRS) and other clinical scales were employed to assess the social functioning of children with ASD. Additionally, the predictive ability of sensory perception on social performance was discussed using random forest and support vector machine (SVM) models. Results The SSP scores of ASD children were lower than those of the control group, and there was a significant negative correlation between SSP scores and clinical scale scores (P < 0.05). The random forest and SVM models, using all the features, showed higher sensitivity, while the random forest model with 7-feature factors had the highest specificity. The area under the receiver operating characteristic (ROC) curve (AUC) for all the models was higher than 0.8. Conclusion Autistic children in our study have different patterns of sensory processing than their peers, which are significantly related to their patterns of social functioning. Sensory features can serve as a good predictor of social functioning in individuals with ASD.
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Affiliation(s)
- Jinhe Zhai
- School of Public Health, Harbin Medical University, Harbin, China
| | - Xiaoxue Li
- School of Public Health, Harbin Medical University, Harbin, China
| | - Yong Zhou
- Heilongjiang Provincial Center for Disease Control and Prevention, Harbin, China
| | - Lili Fan
- School of Public Health, Harbin Medical University, Harbin, China
| | - Wei Xia
- School of Public Health, Harbin Medical University, Harbin, China
| | - Xiaomin Wang
- School of Public Health, Harbin Medical University, Harbin, China
| | - Yutong Li
- School of Public Health, Harbin Medical University, Harbin, China
| | - Meiru Hou
- School of Public Health, Harbin Medical University, Harbin, China
| | - Jia Wang
- School of Public Health, Harbin Medical University, Harbin, China
- *Correspondence: Jia Wang,
| | - Lijie Wu
- School of Public Health, Harbin Medical University, Harbin, China
- Lijie Wu,
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41
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Pang C, Zhang Y, Xue Z, Bao J, Keong Li B, Liu Y, Liu Y, Sheng M, Peng B, Dai Y. Improving model robustness via enhanced feature representation and sample distribution based on cascaded classifiers for computer-aided diagnosis of brain disease. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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42
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Wang J, Zhang L, Zhang Y. Mixture 2D Convolutions for 3D Medical Image Segmentation. Int J Neural Syst 2023; 33:2250059. [PMID: 36328969 DOI: 10.1142/s0129065722500599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Three-dimensional (3D) medical image segmentation plays a crucial role in medical care applications. Although various two-dimensional (2D) and 3D neural network models have been applied to 3D medical image segmentation and achieved impressive results, a trade-off remains between efficiency and accuracy. To address this issue, a novel mixture convolutional network (MixConvNet) is proposed, in which traditional 2D/3D convolutional blocks are replaced with novel MixConv blocks. In the MixConv block, 3D convolution is decomposed into a mixture of 2D convolutions from different views. Therefore, the MixConv block fully utilizes the advantages of 2D convolution and maintains the learning ability of 3D convolution. It acts as 3D convolutions and thus can process volumetric input directly and learn intra-slice features, which are absent in the traditional 2D convolutional block. By contrast, the proposed MixConv block only contains 2D convolutions; hence, it has significantly fewer trainable parameters and less computation budget than a block containing 3D convolutions. Furthermore, the proposed MixConvNet is pre-trained with small input patches and fine-tuned with large input patches to improve segmentation performance further. In experiments on the Decathlon Heart dataset and Sliver07 dataset, the proposed MixConvNet outperformed the state-of-the-art methods such as UNet3D, VNet, and nnUnet.
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Affiliation(s)
- Jianyong Wang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, P. R. China
| | - Lei Zhang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, P. R. China
| | - Yi Zhang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, P. R. China
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43
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Wang J, Ge X, Shi Y, Sun M, Gong Q, Wang H, Huang W. Dual-Modal Information Bottleneck Network for Seizure Detection. Int J Neural Syst 2023; 33:2250061. [PMID: 36599663 DOI: 10.1142/s0129065722500617] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
In recent years, deep learning has shown very competitive performance in seizure detection. However, most of the currently used methods either convert electroencephalogram (EEG) signals into spectral images and employ 2D-CNNs, or split the one-dimensional (1D) features of EEG signals into many segments and employ 1D-CNNs. Moreover, these investigations are further constrained by the absence of consideration for temporal links between time series segments or spectrogram images. Therefore, we propose a Dual-Modal Information Bottleneck (Dual-modal IB) network for EEG seizure detection. The network extracts EEG features from both time series and spectrogram dimensions, allowing information from different modalities to pass through the Dual-modal IB, requiring the model to gather and condense the most pertinent information in each modality and only share what is necessary. Specifically, we make full use of the information shared between the two modality representations to obtain key information for seizure detection and to remove irrelevant feature between the two modalities. In addition, to explore the intrinsic temporal dependencies, we further introduce a bidirectional long-short-term memory (BiLSTM) for Dual-modal IB model, which is used to model the temporal relationships between the information after each modality is extracted by convolutional neural network (CNN). For CHB-MIT dataset, the proposed framework can achieve an average segment-based sensitivity of 97.42%, specificity of 99.32%, accuracy of 98.29%, and an average event-based sensitivity of 96.02%, false detection rate (FDR) of 0.70/h. We release our code at https://github.com/LLLL1021/Dual-modal-IB.
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Affiliation(s)
- Jiale Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Xinting Ge
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Yunfeng Shi
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Mengxue Sun
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Qingtao Gong
- Ulsan Ship and Ocean College, Ludong University, Yantai 264025, P. R. China
| | - Haipeng Wang
- Institute of Information Fusion, Naval, Aviation University, Yantai 264001, P. R. China
| | - Wenhui Huang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
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44
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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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45
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Bahathiq RA, Banjar H, Bamaga AK, Jarraya SK. Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging. Front Neuroinform 2022; 16:949926. [PMID: 36246393 PMCID: PMC9554556 DOI: 10.3389/fninf.2022.949926] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects approximately 1% of the population and causes significant burdens. ASD's pathogenesis remains elusive; hence, diagnosis is based on a constellation of behaviors. Structural magnetic resonance imaging (sMRI) studies have shown several abnormalities in volumetric and geometric features of the autistic brain. However, inconsistent findings prevented most contributions from being translated into clinical practice. Establishing reliable biomarkers for ASD using sMRI is crucial for the correct diagnosis and treatment. In recent years, machine learning (ML) and specifically deep learning (DL) have quickly extended to almost every sector, notably in disease diagnosis. Thus, this has led to a shift and improvement in ASD diagnostic methods, fulfilling most clinical diagnostic requirements. However, ASD discovery remains difficult. This review examines the ML-based ASD diagnosis literature over the past 5 years. A literature-based taxonomy of the research landscape has been mapped, and the major aspects of this topic have been covered. First, we provide an overview of ML's general classification pipeline and the features of sMRI. Next, representative studies are highlighted and discussed in detail with respect to methods, and biomarkers. Finally, we highlight many common challenges and make recommendations for future directions. In short, the limited sample size was the main obstacle; Thus, comprehensive data sets and rigorous methods are necessary to check the generalizability of the results. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians soon.
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Affiliation(s)
- Reem Ahmed Bahathiq
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Haneen Banjar
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
- Center of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmed K. Bamaga
- Neuromuscular Medicine Unit, Department of Pediatric, Faculty of Medicine and King Abdulaziz University Hospital, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Salma Kammoun Jarraya
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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46
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Shi C, Xin X, Zhang J. A novel multigranularity feature-selection method based on neighborhood mutual information and its application in autistic patient identification. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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47
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Liu L, Wang YP, Wang Y, Zhang P, Xiong S. An enhanced multi-modal brain graph network for classifying neuropsychiatric disorders. Med Image Anal 2022; 81:102550. [PMID: 35872360 DOI: 10.1016/j.media.2022.102550] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 07/06/2022] [Accepted: 07/13/2022] [Indexed: 10/17/2022]
Abstract
It has been proven that neuropsychiatric disorders (NDs) can be associated with both structures and functions of brain regions. Thus, data about structures and functions could be usefully combined in a comprehensive analysis. While brain structural MRI (sMRI) images contain anatomic and morphological information about NDs, functional MRI (fMRI) images carry complementary information. However, efficient extraction and fusion of sMRI and fMRI data remains challenging. In this study, we develop an enhanced multi-modal graph convolutional network (MME-GCN) in a binary classification between patients with NDs and healthy controls, based on the fusion of the structural and functional graphs of the brain region. First, based on the same brain atlas, we construct structural and functional graphs from sMRI and fMRI data, respectively. Second, we use machine learning to extract important features from the structural graph network. Third, we use these extracted features to adjust the corresponding edge weights in the functional graph network. Finally, we train a multi-layer GCN and use it in binary classification task. MME-GCN achieved 93.71% classification accuracy on the open data set provided by the Consortium for Neuropsychiatric Phenomics. In addition, we analyzed the important features selected from the structural graph and verified them in the functional graph. Using MME-GCN, we found several specific brain connections important to NDs.
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Affiliation(s)
- Liangliang Liu
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China.
| | - Yu-Ping Wang
- Dthe Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA
| | - Yi Wang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China
| | - Pei Zhang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China
| | - Shufeng Xiong
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China
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48
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Cao P, Wen G, Liu X, Yang J, Zaiane OR. Modeling the dynamic brain network representation for autism spectrum disorder diagnosis. Med Biol Eng Comput 2022; 60:1897-1913. [DOI: 10.1007/s11517-022-02558-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 03/29/2022] [Indexed: 10/18/2022]
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Li S, Tang Z, Jin N, Yang Q, Liu G, Liu T, Hu J, Liu S, Wang P, Hao J, Zhang Z, Zhang X, Li J, Wang X, Li Z, Wang Y, Yang B, Ma L. Uncovering Brain Differences in Preschoolers and Young Adolescents with Autism Spectrum Disorder using Deep Learning. Int J Neural Syst 2022; 32:2250044. [DOI: 10.1142/s0129065722500447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Wu T, Neri F, Pan L. On the tuning of the computation capability of spiking neural membrane systems with communication on request. Int J Neural Syst 2022; 32:2250037. [DOI: 10.1142/s012906572250037x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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