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Ahmadi-Dastgerdi N, Hosseini-Nejad H, Amiri H, Shoeibi A, Gorriz JM. A Vector Quantization-Based Spike Compression Approach Dedicated to Multichannel Neural Recording Microsystems. Int J Neural Syst 2021; 32:2250001. [PMID: 34931938 DOI: 10.1142/s0129065722500010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Implantable high-density multichannel neural recording microsystems provide simultaneous recording of brain activities. Wireless transmission of the entire recorded data causes high bandwidth usage, which is not tolerable for implantable applications. As a result, a hardware-friendly compression module is required to reduce the amount of data before it is transmitted. This paper presents a novel compression approach that utilizes a spike extractor and a vector quantization (VQ)-based spike compressor. In this approach, extracted spikes are vector quantized using an unsupervised learning process providing a high spike compression ratio (CR) of 10-80. A combination of extracting and compressing neural spikes results in a significant data reduction as well as preserving the spike waveshapes. The compression performance of the proposed approach was evaluated under variant conditions. We also developed new architectures such that the hardware blocks of our approach can be implemented more efficiently. The compression module was implemented in a 180-nm standard CMOS process achieving a SNDR of 14.49[Formula: see text]dB and a classification accuracy (CA) of 99.62% at a CR of 20, while consuming 4[Formula: see text][Formula: see text]W power and 0.16[Formula: see text]mm2 chip area per channel.
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Affiliation(s)
| | | | - Hadi Amiri
- School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran
| | - Afshin Shoeibi
- Faculty of Electrical Engineering, FPGA Research Lab K. N. Toosi, University of Technology, Tehran, Iran
| | - Juan Manuel Gorriz
- Department of Signal Processing Networking and Communications, University of Granada, Granada, Spain.,Department of Psychiatry, University of Cambridge, Cambridge, UK
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Balasubramanian K, Np A, K R. Prediction of neuro-degenerative disorders using sunflower optimisation algorithm and Kernel extreme learning machine: A case-study with Parkinson's and Alzheimer's disease. Proc Inst Mech Eng H 2021; 236:438-453. [PMID: 34923855 DOI: 10.1177/09544119211060989] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Parkinson's and Alzheimer's Disease are believed to be most prevalent and common in older people. Several data-mining approaches are employed on the neuro-degenerative data in predicting the disease. A novel method has been built and developed to diagnose Alzheimer's (AD) and Parkinson's (PD) in early stages, which includes image acquisition, pre-processing, feature extraction and selection, followed by classification. The challenge lies in selecting the optimal feature subset for classification. In this work, the Sunflower Optimisation Algorithm (SFO) is employed to select the optimal feature set, which is then fed to the Kernel Extreme Learning Machine (KELM) for classification. The method is tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and local dataset for AD, the University of California, Irvine (UCI) machine learning repository and the Istanbul dataset for PD. Experimental outcomes have demonstrated a high accuracy level in both AD and PD diagnosis. For AD diagnosis, the highest classification rate is obtained for the AD versus NC classification using the ADNI dataset (99.32%) and local dataset (98.65%). For PD diagnosis, the highest accuracy of 99.52% and 99.45% is achieved on the UCI and Istanbul datasets, respectively. To show the robustness of the method, the method is compared with other similar methods of feature selection and classification with 10-fold cross-validation (CV) and with unseen data. The method proposed has an excellent prospect, bringing greater convenience to clinicians in making a better solid decision in clinical diagnosis of neuro-degenerative diseases.
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Affiliation(s)
| | - Ananthamoorthy Np
- Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India
| | - Ramya K
- P A College of Engineering and Technology, Pollachi, Tamil Nadu, India
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53
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Wang L, Wang H, Huang Y, Yan B, Chang Z, Liu Z, Zhao M, Cui L, Song J, Li F. Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020. Eur J Radiol 2021; 146:110069. [PMID: 34847395 DOI: 10.1016/j.ejrad.2021.110069] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/10/2021] [Accepted: 11/22/2021] [Indexed: 12/21/2022]
Abstract
PURPOSE To evaluate the general rules and future trajectories of deep learning (DL) networks in medical image analysis through bibliometric and hot spot analysis of original articles published between 2012 and 2020. METHODS Original articles related to DL and medical imaging were retrieved from the PubMed database. For the analysis, data regarding radiological subspecialties; imaging techniques; DL networks; sample size; study purposes, setting, origins and design; statistical analysis; funding sources; authors; and first authors' affiliation was manually extracted from each article. The Bibliographic Item Co-Occurrence Matrix Builder and VOSviewer were used to identify the research topics of the included articles and illustrate the future trajectories of studies. RESULTS The study included 2685 original articles. The number of publications on DL and medical imaging has increased substantially since 2017, accounting for 97.2% of all included articles. We evaluated the rules of the application of 47 DL networks to eight radiological tasks on 11 human organ sites. Neuroradiology, thorax, and abdomen were frequent research subjects, while thyroid was under-represented. Segmentation and classification tasks were the primary purposes. U-Net, ResNet, and VGG were the most frequently used Convolutional neural network-derived networks. GAN-derived networks were widely developed and applied in 2020, and transfer learning was highlighted in the COVID-19 studies. Brain, prostate, and diabetic retinopathy-related studies were mature research topics in the field. Breast- and lung-related studies were in a stage of rapid development. CONCLUSIONS This study evaluates the general rules and future trajectories of DL network application in medical image analyses and provides guidance for future studies.
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Affiliation(s)
- Lu Wang
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, PR China
| | - Hairui Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, PR China
| | - Yingna Huang
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, PR China
| | - Baihui Yan
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, PR China
| | - Zhihui Chang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, PR China
| | - Zhaoyu Liu
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, PR China
| | - Mingfang Zhao
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, PR China
| | - Lei Cui
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, PR China
| | - Jiangdian Song
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, PR China.
| | - Fan Li
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, PR China
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54
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Dyrba M, Hanzig M, Altenstein S, Bader S, Ballarini T, Brosseron F, Buerger K, Cantré D, Dechent P, Dobisch L, Düzel E, Ewers M, Fliessbach K, Glanz W, Haynes JD, Heneka MT, Janowitz D, Keles DB, Kilimann I, Laske C, Maier F, Metzger CD, Munk MH, Perneczky R, Peters O, Preis L, Priller J, Rauchmann B, Roy N, Scheffler K, Schneider A, Schott BH, Spottke A, Spruth EJ, Weber MA, Ertl-Wagner B, Wagner M, Wiltfang J, Jessen F, Teipel SJ. Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer's disease. Alzheimers Res Ther 2021; 13:191. [PMID: 34814936 PMCID: PMC8611898 DOI: 10.1186/s13195-021-00924-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 10/25/2021] [Indexed: 11/29/2022]
Abstract
Background Although convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer’s disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap as they allow the visualization of key input image features that drive the decision of the model. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge. Methods We trained a CNN for the detection of AD in N = 663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including in total N = 1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps, thereby allowing intuitive model inspection. Results Across the three independent datasets, group separation showed high accuracy for AD dementia versus controls (AUC ≥ 0.91) and moderate accuracy for amnestic MCI versus controls (AUC ≈ 0.74). Relevance maps indicated that hippocampal atrophy was considered the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson’s r ≈ −0.86, p < 0.001). Conclusion The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels. The high hippocampus relevance scores as well as the high performance achieved in independent samples support the validity of the CNN models in the detection of AD-related MRI abnormalities. The presented data-driven and hypothesis-free CNN modeling approach might provide a useful tool to automatically derive discriminative features for complex diagnostic tasks where clear clinical criteria are still missing, for instance for the differential diagnosis between various types of dementia. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-021-00924-2.
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Affiliation(s)
- Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.
| | - Moritz Hanzig
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Institute of Visual and Analytic Computing, University of Rostock, Rostock, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany
| | - Sebastian Bader
- Institute of Visual and Analytic Computing, University of Rostock, Rostock, Germany
| | | | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University, Munich, Germany
| | - Daniel Cantré
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Peter Dechent
- MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University, Goettingen, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University, Munich, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | | | - Michael T Heneka
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University, Munich, Germany
| | - Deniz B Keles
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany.,Section for Dementia Research, Hertie Institute for Clinical Brain Research, Tuebingen, Germany.,Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany
| | - Franziska Maier
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Coraline D Metzger
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany.,Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany.,Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany.,Systems Neurophysiology, Department of Biology, Darmstadt University of Technology, Darmstadt, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Ludwig Maximilian University, Munich, Germany.,Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Lukas Preis
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany.,Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
| | - Boris Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University, Munich, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Björn H Schott
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, Goettingen, Germany.,Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurology, University Hospital Bonn, Bonn, Germany
| | - Eike J Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany
| | - Marc-André Weber
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Birgit Ertl-Wagner
- Institute for Clinical Radiology, Ludwig Maximilian University, Munich, Germany.,Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, Goettingen, Germany.,Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany.,Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
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Deep learning based pipelines for Alzheimer's disease diagnosis: A comparative study and a novel deep-ensemble method. Comput Biol Med 2021; 141:105032. [PMID: 34838263 DOI: 10.1016/j.compbiomed.2021.105032] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 10/23/2021] [Accepted: 11/10/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Alzheimer's disease is a chronic neurodegenerative disease that destroys brain cells, causing irreversible degeneration of cognitive functions and dementia. Its causes are not yet fully understood, and there is no curative treatment. However, neuroimaging tools currently offer help in clinical diagnosis, and, recently, deep learning methods have rapidly become a key methodology applied to these tools. The reason is that they require little or no image preprocessing and can automatically infer an optimal representation of the data from raw images without requiring prior feature selection, resulting in a more objective and less biased process. However, training a reliable model is challenging due to the significant differences in brain image types. METHODS We aim to contribute to the research and study of Alzheimer's disease through computer-aided diagnosis (CAD) by comparing different deep learning models. In this work, there are three main objectives: i) to present a fully automated deep-ensemble approach for dementia-level classification from brain images, ii) to compare different deep learning architectures to obtain the most suitable one for the task, and (iii) evaluate the robustness of the proposed strategy in a deep learning framework to detect Alzheimer's disease and recognise different levels of dementia. The proposed approach is specifically designed to be potential support for clinical care based on patients' brain images. RESULTS Our strategy was developed and tested on three MRI and one fMRI public datasets with heterogeneous characteristics. By performing a comprehensive analysis of binary classification (Alzheimer's disease status or not) and multiclass classification (recognising different levels of dementia), the proposed approach can exceed state of the art in both tasks, reaching an accuracy of 98.51% in the binary case, and 98.67% in the multiclass case averaged over the four different data sets. CONCLUSION We strongly believe that integrating the proposed deep-ensemble approach will result in robust and reliable CAD systems, considering the numerous cross-dataset experiments performed. Being tested on MRIs and fMRIs, our strategy can be easily extended to other imaging techniques. In conclusion, we found that our deep-ensemble strategy could be efficiently applied for this task with a considerable potential benefit for patient management.
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Zhang P, Lin S, Qiao J, Tu Y. Diagnosis of Alzheimer's Disease with Ensemble Learning Classifier and 3D Convolutional Neural Network. SENSORS 2021; 21:s21227634. [PMID: 34833710 PMCID: PMC8623279 DOI: 10.3390/s21227634] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 11/06/2021] [Accepted: 11/15/2021] [Indexed: 12/21/2022]
Abstract
Alzheimer’s disease (AD), the most common type of dementia, is a progressive disease beginning with mild memory loss, possibly leading to loss of the ability to carry on a conversation and respond to environments. It can seriously affect a person’s ability to carry out daily activities. Therefore, early diagnosis of AD is conducive to better treatment and avoiding further deterioration of the disease. Magnetic resonance imaging (MRI) has become the main tool for humans to study brain tissues. It can clearly reflect the internal structure of a brain and plays an important role in the diagnosis of Alzheimer’s disease. MRI data is widely used for disease diagnosis. In this paper, based on MRI data, a method combining a 3D convolutional neural network and ensemble learning is proposed to improve the diagnosis accuracy. Then, a data denoising module is proposed to reduce boundary noise. The experimental results on ADNI dataset demonstrate that the model proposed in this paper improves the training speed of the neural network and achieves 95.2% accuracy in AD vs. NC (normal control) task and 77.8% accuracy in sMCI (stable mild cognitive impairment) vs. pMCI (progressive mild cognitive impairment) task in the diagnosis of Alzheimer’s disease.
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Agarwal D, Marques G, de la Torre-Díez I, Franco Martin MA, García Zapiraín B, Martín Rodríguez F. Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:7259. [PMID: 34770565 PMCID: PMC8587338 DOI: 10.3390/s21217259] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 10/27/2021] [Accepted: 10/27/2021] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.
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Affiliation(s)
- Deevyankar Agarwal
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; (G.M.); (I.d.l.T.-D.)
| | - Gonçalo Marques
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; (G.M.); (I.d.l.T.-D.)
- Polytechnic of Coimbra, ESTGOH, Rua General Santos Costa, 3400-124 Oliveira do Hospital, Portugal
| | - Isabel de la Torre-Díez
- Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; (G.M.); (I.d.l.T.-D.)
| | - Manuel A. Franco Martin
- Psychiatric Department, University Rio Hortega Hospital–Valladolid, 47011 Valladolid, Spain;
| | - Begoña García Zapiraín
- eVIDA Laboratory, University of Deusto, Avenida de las Universidades 24, 48007 Bilbao, Spain;
| | - Francisco Martín Rodríguez
- Advanced Clinical Simulation Center, School of Medicine, University of Valladolid, 47011 Valladolid, Spain;
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Formoso MA, Ortiz A, Martinez-Murcia FJ, Gallego N, Luque JL. Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis. SENSORS 2021; 21:s21217061. [PMID: 34770378 PMCID: PMC8588444 DOI: 10.3390/s21217061] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/21/2021] [Accepted: 10/22/2021] [Indexed: 01/07/2023]
Abstract
Objective Dyslexia diagnosis is a challenging task, since traditional diagnosis methods are not based on biological markers but on behavioural tests. Although dyslexia diagnosis has been addressed by these tests in clinical practice, it is difficult to extract information about the brain processes involved in the different tasks and, then, to go deeper into its biological basis. Thus, the use of biomarkers can contribute not only to the diagnosis but also to a better understanding of specific learning disorders such as dyslexia. In this work, we use Electroencephalography (EEG) signals to discover differences among controls and dyslexic subjects using signal processing and artificial intelligence techniques. Specifically, we measure phase synchronization among channels, to reveal the functional brain network activated during auditory processing. On the other hand, to explore synchronicity patterns risen by low-level auditory processing, we used specific stimuli consisting in band-limited white noise, modulated in amplitude at different frequencies. The differential information contained in the functional (i.e., synchronization) network has been processed by an anomaly detection system that addresses the problem of subjects variability by an outlier-detection method based on vector quantization. The results, obtained for 7 years-old children, show that the proposed method constitutes an useful tool for clinical use, with the area under ROC curve (AUC) values up to 0.95 in differential diagnosis tasks.
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Affiliation(s)
- Marco A. Formoso
- Communications Engineering Department, University of Málaga, 29071 Málaga, Spain; (M.A.F.); (N.G.)
| | - Andrés Ortiz
- Communications Engineering Department, University of Málaga, 29071 Málaga, Spain; (M.A.F.); (N.G.)
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), 18014 Granada, Spain;
- Correspondence: ; Tel.: +34-952133353
| | - Francisco J. Martinez-Murcia
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), 18014 Granada, Spain;
- Department of Signal Theory, Networking and Communications, University of Granada, 18014 Granada, Spain
| | - Nicolás Gallego
- Communications Engineering Department, University of Málaga, 29071 Málaga, Spain; (M.A.F.); (N.G.)
| | - Juan L. Luque
- Department of Basic Psychology, University of Malaga, 29019 Málaga, Spain;
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Qi YL, Wang HR, Chen LL, Guo L, Cao YY, Yang YS, Duan YT, Zhu HL. Recent advances in reaction-based fluorescent probes for the detection of central nervous system-related pathologies in vivo. Coord Chem Rev 2021. [DOI: 10.1016/j.ccr.2021.214068] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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60
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Sawada Y, Sato T, Nagaya M, Saito C, Yoshihara H, Banno C, Matsumoto Y, Matsuda Y, Yoshikai K, Sawada T, Ukita N, Sugiura-Ogasawara M. Evaluation of artificial intelligence using time-lapse images of IVF embryos to predict live birth. Reprod Biomed Online 2021; 43:843-852. [PMID: 34521598 DOI: 10.1016/j.rbmo.2021.05.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 03/18/2021] [Accepted: 05/05/2021] [Indexed: 10/21/2022]
Abstract
RESEARCH QUESTION Can artificial intelligence (AI) improve the prediction of live births based on embryo images? DESIGN The AI system was created by using the Attention Branch Network associated with deep learning to predict the probability of live birth from 141,444 images recorded by time-lapse imaging of 470 transferred embryos, of which 91 resulted in live birth and 379 resulted in non-live birth that included implantation failure, biochemical pregnancy and clinical miscarriage. The possibility that the calculated confidence scores of each embryo and the focused areas visualized in each embryo image can help predict subsequent live birth was examined. RESULTS The AI system for the first time successfully visualized embryo features in focused areas that had potential to distinguish between live and non-live births. No visual feature of embryos were visualized that were associated with live or non-live births, although there were many images in which high-focused areas existed around the zona pellucida. When a cut-off level for the confidence score was set at 0.341, the live birth rate was significantly greater for embryos with a score higher than the cut-off level than for those with a score lower than the cut-off level (P < 0.001). In addition, the live birth rate of embryos with good morphological quality and confidence scores higher than 0.341 was 41.1%. CONCLUSIONS The authors have created an AI system with a confidence score that is useful for non-invasive selection of embryos that could result in live birth. Further study is necessary to improve selection accuracy.
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Affiliation(s)
- Yuki Sawada
- Department of Obstetrics and Gynecology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Takeshi Sato
- Department of Obstetrics and Gynecology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
| | - Masashi Nagaya
- Intelligent Information Media Lab, Graduate School of Engineering, Toyota Technological Institute, Nagoya, Japan
| | - Chieko Saito
- Department of Obstetrics and Gynecology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Hiroyuki Yoshihara
- Department of Obstetrics and Gynecology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Chihiro Banno
- Department of Obstetrics and Gynecology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Yosuke Matsumoto
- Department of Obstetrics and Gynecology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | | | | | | | - Norimichi Ukita
- Intelligent Information Media Lab, Graduate School of Engineering, Toyota Technological Institute, Nagoya, Japan
| | - Mayumi Sugiura-Ogasawara
- Department of Obstetrics and Gynecology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
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61
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Guo Y, Song Q, Jiang M, Guo Y, Xu P, Zhang Y, Fu CC, Fang Q, Zeng M, Yao X. Histological Subtypes Classification of Lung Cancers on CT Images Using 3D Deep Learning and Radiomics. Acad Radiol 2021; 28:e258-e266. [PMID: 32622740 DOI: 10.1016/j.acra.2020.06.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Revised: 06/05/2020] [Accepted: 06/05/2020] [Indexed: 12/24/2022]
Abstract
RATIONALE AND OBJECTIVES Histological subtypes of lung cancers are critical for clinical treatment decision. In this study, we attempt to use 3D deep learning and radiomics methods to automatically distinguish lung adenocarcinomas (ADC), squamous cell carcinomas (SCC), and small cell lung cancers (SCLC) respectively on Computed Tomography images, and then compare their performance. MATERIALS AND METHODS 920 patients (mean age 61.2, range, 17-87; 340 Female and 580 Male) with lung cancer, including 554 patients with ADC, 175 patients with lung SCC and 191 patients with SCLC, were included in this retrospective study from January 2013 to August 2018. Histopathologic analysis was available for every patient. The classification models based on 3D deep learning (named the ProNet) and radiomics (named com_radNet) were designed to classify lung cancers into the three types mentioned above according to histopathologic results. The training, validation and testing cohorts counted 0.70, 0.15, and 0.15 of the whole datasets respectively. RESULTS The ProNet model used to classify the three types of lung cancers achieved the F1-scores of 90.0%, 72.4%, 83.7% in ADC, SCC, and SCLC respectively, and the weighted average F1-score of 73.2%. For com_radNet, the F1-scores achieved 83.1%, 75.4%, 85.1% in ADC, SCC, and SCLC, and the weighted average F1-score was 72.2%. The area under the receiver operating characteristic curve of the ProNet model and com_radNet were 0.840 and 0.789, and the accuracy were 71.6% and 74.7% respectively. CONCLUSION The ProNet and com_radNet models we developed can achieve high performance in distinguishing ADC, SCC, and SCLC and may be promising approaches for non-invasive predicting histological subtypes of lung cancers.
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Martinez-Murcia FJ, Ortiz A, Ramírez J, Górriz JM, Cruz R. Deep residual transfer learning for automatic diagnosis and grading of diabetic retinopathy. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.04.148] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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63
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Ganesh K, Umapathy S, Thanaraj Krishnan P. Deep learning techniques for automated detection of autism spectrum disorder based on thermal imaging. Proc Inst Mech Eng H 2021; 235:1113-1127. [PMID: 34105405 DOI: 10.1177/09544119211024778] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Children with autism spectrum disorder have impairments in emotional processing which leads to the inability in recognizing facial expressions. Since emotion is a vital criterion for having fine socialisation, it is incredibly important for the autistic children to recognise emotions. In our study, we have chosen the facial skin temperature as a biomarker to measure emotions. To assess the facial skin temperature, the thermal imaging modality has been used in this study, since it has been recognised as a promising technique to evaluate emotional responses. The aim of this study was the following: (1) to compare the facial skin temperature of autistic and non-autistic children by using thermal imaging across various emotions; (2) to classify the thermal images obtained from the study using the customised convolutional neural network compared with the ResNet 50 network. Fifty autistic and fifty non-autistic participants were included for the study. Thermal imaging was used to obtain the temperature of specific facial regions such as the eyes, cheek, forehead and nose while we evoked emotions (Happiness, anger and sadness) in children using an audio-visual stimulus. Among the emotions considered, the emotion anger had the highest temperature difference between the autistic and non-autistic participants in the region's eyes (1.9%), cheek (2.38%) and nose (12.6%). The accuracy obtained by classifying the thermal images of the autistic and non-autistic children using Customised Neural Network and ResNet 50 Network was 96% and 90% respectively. This computer aided diagnostic tool can be a predictable and a steadfast method in the diagnosis of the autistic individuals.
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Affiliation(s)
- Kavya Ganesh
- Department of Biomedical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
| | - Snekhalatha Umapathy
- Department of Biomedical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
| | - Palani Thanaraj Krishnan
- Department of Electronics and Instrumentation Engineering, St. Joseph's College of Engineering, Anna University, Chennai, Tamil Nadu, India
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64
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A new deep belief network-based multi-task learning for diagnosis of Alzheimer’s disease. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06149-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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65
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You P, Li X, Wang Z, Wang H, Dong B, Li Q. Characterization of Brain Iron Deposition Pattern and Its Association With Genetic Risk Factor in Alzheimer's Disease Using Susceptibility-Weighted Imaging. Front Hum Neurosci 2021; 15:654381. [PMID: 34163341 PMCID: PMC8215439 DOI: 10.3389/fnhum.2021.654381] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 04/27/2021] [Indexed: 11/30/2022] Open
Abstract
The presence of iron is an important factor for normal brain functions, whereas excessive deposition of iron may impair normal cognitive function in the brain and lead to Alzheimer’s disease (AD). MRI has been widely applied to characterize brain structural and functional changes caused by AD. However, the effectiveness of using susceptibility-weighted imaging (SWI) for the analysis of brain iron deposition is still unclear, especially within the context of early AD diagnosis. Thus, in this study, we aim to explore the relationship between brain iron deposition measured by SWI with the progression of AD using various feature selection and classification methods. The proposed model was evaluated on a 69-subject SWI imaging dataset consisting of 24 AD patients, 21 mild cognitive impairment patients, and 24 normal controls. The identified AD progression-related regions were then compared with the regions reported from previous genetic association studies, and we observed considerable overlap between these two. Further, we have identified a new potential AD-related gene (MEF2C) closely related to the interaction between iron deposition and AD progression in the brain.
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Affiliation(s)
- Peiting You
- Beijing International Center for Mathematical Research, Peking University, Beijing, China.,Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Xiang Li
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Zhijiang Wang
- Peking University Institute of Mental Health (Sixth Hospital), Beijing, China.,National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China.,Beijing Municipal Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, China
| | - Huali Wang
- Peking University Institute of Mental Health (Sixth Hospital), Beijing, China.,National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China.,Beijing Municipal Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, China
| | - Bin Dong
- Beijing International Center for Mathematical Research, Peking University, Beijing, China
| | - Quanzheng Li
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
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Sheng J, Wang B, Zhang Q, Zhou R, Wang L, Xin Y. Identifying and characterizing different stages toward Alzheimer's disease using ordered core features and machine learning. Heliyon 2021; 7:e07287. [PMID: 34189320 PMCID: PMC8220177 DOI: 10.1016/j.heliyon.2021.e07287] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 02/18/2021] [Accepted: 06/07/2021] [Indexed: 11/23/2022] Open
Abstract
Based on the joint HCPMMP parcellation method we developed before, which divides the cortical brain into 360 regions, the concept of ordered core features (OCF) is first proposed to reveal the functional brain connectivity relationship among different cohorts of Alzheimer's disease (AD), late mild cognitive impairment (LMCI), early mild cognitive impairment (EMCI) and healthy controls (HC). A set of core network features that change significantly under the specifically progressive relationship were extracted and used as supervised machine learning classifiers. The network nodes in this set mainly locate in the frontal lobe and insular, forming a narrow band, which are responsible for cognitive impairment as suggested by previous finding. By using these features, the accuracy ranged from 86.0% to 95.5% in binary classification between any pair of cohorts, higher than 70.1%-91.0% when using all network features. In multi-group classification, the average accuracy was 75% or 78% for HC, EMCI, LMCI or EMCI, LMCI, AD against baseline of 33%, and 53.3% for HC, EMCI, LMCI and AD against baseline of 25%. In addition, the recognition rate was lower when combining EMCI and LMCI patients into one group of mild cognitive impairment (MCI) for classification, suggesting that there exists a big difference between early and late MCI patients. This finding supports the EMCI/LMCI inclusion criteria introduced by ADNI based on neuropsychological assessments.
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Affiliation(s)
- Jinhua Sheng
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China
- Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
| | - Bocheng Wang
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China
- Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
- Communication University of Zhejiang, Hangzhou, Zhejiang, 310018, China
| | - Qiao Zhang
- Beijing Hospital, Beijing, 100730, China
| | - Rougang Zhou
- Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
- College of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China
- Mstar Technologies Inc., Hangzhou, Zhejiang, 310018, China
| | - Luyun Wang
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China
- Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
| | - Yu Xin
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China
- Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
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Constantino AC, Sisterson ND, Zaher N, Urban A, Richardson RM, Kokkinos V. Expert-Level Intracranial Electroencephalogram Ictal Pattern Detection by a Deep Learning Neural Network. Front Neurol 2021; 12:603868. [PMID: 34012415 PMCID: PMC8126697 DOI: 10.3389/fneur.2021.603868] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 04/08/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Decision-making in epilepsy surgery is strongly connected to the interpretation of the intracranial EEG (iEEG). Although deep learning approaches have demonstrated efficiency in processing extracranial EEG, few studies have addressed iEEG seizure detection, in part due to the small number of seizures per patient typically available from intracranial investigations. This study aims to evaluate the efficiency of deep learning methodology in detecting iEEG seizures using a large dataset of ictal patterns collected from epilepsy patients implanted with a responsive neurostimulation system (RNS). Methods: Five thousand two hundred and twenty-six ictal events were collected from 22 patients implanted with RNS. A convolutional neural network (CNN) architecture was created to provide personalized seizure annotations for each patient. Accuracy of seizure identification was tested in two scenarios: patients with seizures occurring following a period of chronic recording (scenario 1) and patients with seizures occurring immediately following implantation (scenario 2). The accuracy of the CNN in identifying RNS-recorded iEEG ictal patterns was evaluated against human neurophysiology expertise. Statistical performance was assessed via the area-under-precision-recall curve (AUPRC). Results: In scenario 1, the CNN achieved a maximum mean binary classification AUPRC of 0.84 ± 0.19 (95%CI, 0.72-0.93) and mean regression accuracy of 6.3 ± 1.0 s (95%CI, 4.3-8.5 s) at 30 seed samples. In scenario 2, maximum mean AUPRC was 0.80 ± 0.19 (95%CI, 0.68-0.91) and mean regression accuracy was 6.3 ± 0.9 s (95%CI, 4.8-8.3 s) at 20 seed samples. We obtained near-maximum accuracies at seed size of 10 in both scenarios. CNN classification failures can be explained by ictal electro-decrements, brief seizures, single-channel ictal patterns, highly concentrated interictal activity, changes in the sleep-wake cycle, and progressive modulation of electrographic ictal features. Conclusions: We developed a deep learning neural network that performs personalized detection of RNS-derived ictal patterns with expert-level accuracy. These results suggest the potential for automated techniques to significantly improve the management of closed-loop brain stimulation, including during the initial period of recording when the device is otherwise naïve to a given patient's seizures.
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Affiliation(s)
- Alexander C Constantino
- Brain Modulation Lab, Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Nathaniel D Sisterson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, United States
| | - Naoir Zaher
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.,University of Pittsburgh Comprehensive Epilepsy Center, Pittsburgh, PA, United States
| | - Alexandra Urban
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.,University of Pittsburgh Comprehensive Epilepsy Center, Pittsburgh, PA, United States
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Vasileios Kokkinos
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
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68
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Er F, Goularas D. Predicting the Prognosis of MCI Patients Using Longitudinal MRI Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1164-1173. [PMID: 32813661 DOI: 10.1109/tcbb.2020.3017872] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The aim of this study is to develop a computer-aided diagnosis system with a deep-learning approach for distinguishing "Mild Cognitive Impairment (MCI) due to Alzheimer's Disease (AD)" patients among a list of MCI patients. In this system we are using the power of longitudinal data extracted from magnetic resonance (MR). For this work, a total of 294 MCI patients were selected from the ADNI database. Among them, 125 patients developed AD during their follow-up and the rest remained stable. The proposed computer-aided diagnosis system (CAD) attempts to identify brain regions that are significant for the prediction of developing AD. The longitudinal data were constructed using a 3D Jacobian-based method aiming to track the brain differences between two consecutive follow-ups. The proposed CAD system distinguishes MCI patients who developed AD from those who remained stable with an accuracy of 87.2 percent. Moreover, it does not depend on data acquired by invasive methods or cognitive tests. This work demonstrates that the use of data in different time periods contains information that is beneficial for prognosis prediction purposes that outperform similar methods and are slightly inferior only to those systems that use invasive methods or neuropsychological tests.
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69
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Yang S, Zhu F, Ling X, Liu Q, Zhao P. Intelligent Health Care: Applications of Deep Learning in Computational Medicine. Front Genet 2021; 12:607471. [PMID: 33912213 PMCID: PMC8075004 DOI: 10.3389/fgene.2021.607471] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 03/05/2021] [Indexed: 12/24/2022] Open
Abstract
With the progress of medical technology, biomedical field ushered in the era of big data, based on which and driven by artificial intelligence technology, computational medicine has emerged. People need to extract the effective information contained in these big biomedical data to promote the development of precision medicine. Traditionally, the machine learning methods are used to dig out biomedical data to find the features from data, which generally rely on feature engineering and domain knowledge of experts, requiring tremendous time and human resources. Different from traditional approaches, deep learning, as a cutting-edge machine learning branch, can automatically learn complex and robust feature from raw data without the need for feature engineering. The applications of deep learning in medical image, electronic health record, genomics, and drug development are studied, where the suggestion is that deep learning has obvious advantage in making full use of biomedical data and improving medical health level. Deep learning plays an increasingly important role in the field of medical health and has a broad prospect of application. However, the problems and challenges of deep learning in computational medical health still exist, including insufficient data, interpretability, data privacy, and heterogeneity. Analysis and discussion on these problems provide a reference to improve the application of deep learning in medical health.
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Affiliation(s)
- Sijie Yang
- School of Computer Science and Technology, Soochow University, Suzhou, China
| | - Fei Zhu
- School of Computer Science and Technology, Soochow University, Suzhou, China
| | - Xinghong Ling
- School of Computer Science and Technology, Soochow University, Suzhou, China
- WenZheng College of Soochow University, Suzhou, China
| | - Quan Liu
- School of Computer Science and Technology, Soochow University, Suzhou, China
| | - Peiyao Zhao
- School of Computer Science and Technology, Soochow University, Suzhou, China
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70
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Foresti GL. Editorial: From Pioneering Artificial Neural Networks to Deep Learning and Beyond. Int J Neural Syst 2021; 31:2103004. [PMID: 33622197 DOI: 10.1142/s0129065721030040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- Gian Luca Foresti
- Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy
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71
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Tzimourta KD, Christou V, Tzallas AT, Giannakeas N, Astrakas LG, Angelidis P, Tsalikakis D, Tsipouras MG. Machine Learning Algorithms and Statistical Approaches for Alzheimer's Disease Analysis Based on Resting-State EEG Recordings: A Systematic Review. Int J Neural Syst 2021; 31:2130002. [PMID: 33588710 DOI: 10.1142/s0129065721300023] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.
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Affiliation(s)
- Katerina D Tzimourta
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani, GR50100, Greece.,Department of Medical Physics, Medical School, University of Ioannina, Ioannina GR45110, Greece
| | - Vasileios Christou
- Q Base R&D, Science & Technology Park of Epirus, University of Ioannina Campus, Ioannina GR45110, Greece.,Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Alexandros T Tzallas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Loukas G Astrakas
- Department of Medical Physics, Medical School, University of Ioannina, Ioannina GR45110, Greece
| | - Pantelis Angelidis
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
| | - Dimitrios Tsalikakis
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
| | - Markos G Tsipouras
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
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Raza K, Singh NK. A Tour of Unsupervised Deep Learning for Medical Image Analysis. Curr Med Imaging 2021; 17:1059-1077. [PMID: 33504314 DOI: 10.2174/1573405617666210127154257] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 11/17/2020] [Accepted: 12/16/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Interpretation of medical images for the diagnosis and treatment of complex diseases from high-dimensional and heterogeneous data remains a key challenge in transforming healthcare. In the last few years, both supervised and unsupervised deep learning achieved promising results in the area of medical image analysis. Several reviews on supervised deep learning are published, but hardly any rigorous review on unsupervised deep learning for medical image analysis is available. OBJECTIVES The objective of this review is to systematically present various unsupervised deep learning models, tools, and benchmark datasets applied to medical image analysis. Some of the discussed models are autoencoders and its other variants, Restricted Boltzmann machines (RBM), Deep belief networks (DBN), Deep Boltzmann machine (DBM), and Generative adversarial network (GAN). Further, future research opportunities and challenges of unsupervised deep learning techniques for medical image analysis are also discussed. CONCLUSION Currently, interpretation of medical images for diagnostic purposes is usually performed by human experts that may be replaced by computer-aided diagnosis due to advancement in machine learning techniques, including deep learning, and the availability of cheap computing infrastructure through cloud computing. Both supervised and unsupervised machine learning approaches are widely applied in medical image analysis, each of them having certain pros and cons. Since human supervisions are not always available or inadequate or biased, therefore, unsupervised learning algorithms give a big hope with lots of advantages for biomedical image analysis.
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Affiliation(s)
- Khalid Raza
- Department of Computer Science, Jamia Millia Islamia, New Delhi. India
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73
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Fan S, Zhao Z, Yu H, Wang L, Zheng C, Huang X, Yang Z, Xing M, Lu Q, Luo Y. Applying probability calibration to ensemble methods to predict 2-year mortality in patients with DLBCL. BMC Med Inform Decis Mak 2021; 21:14. [PMID: 33413321 PMCID: PMC7791789 DOI: 10.1186/s12911-020-01354-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 11/26/2020] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Under the influences of chemotherapy regimens, clinical staging, immunologic expressions and other factors, the survival rates of patients with diffuse large B-cell lymphoma (DLBCL) are different. The accurate prediction of mortality hazards is key to precision medicine, which can help clinicians make optimal therapeutic decisions to extend the survival times of individual patients with DLBCL. Thus, we have developed a predictive model to predict the mortality hazard of DLBCL patients within 2 years of treatment. METHODS We evaluated 406 patients with DLBCL and collected 17 variables from each patient. The predictive variables were selected by the Cox model, the logistic model and the random forest algorithm. Five classifiers were chosen as the base models for ensemble learning: the naïve Bayes, logistic regression, random forest, support vector machine and feedforward neural network models. We first calibrated the biased outputs from the five base models by using probability calibration methods (including shape-restricted polynomial regression, Platt scaling and isotonic regression). Then, we aggregated the outputs from the various base models to predict the 2-year mortality of DLBCL patients by using three strategies (stacking, simple averaging and weighted averaging). Finally, we assessed model performance over 300 hold-out tests. RESULTS Gender, stage, IPI, KPS and rituximab were significant factors for predicting the deaths of DLBCL patients within 2 years of treatment. The stacking model that first calibrated the base model by shape-restricted polynomial regression performed best (AUC = 0.820, ECE = 8.983, MCE = 21.265) in all methods. In contrast, the performance of the stacking model without undergoing probability calibration is inferior (AUC = 0.806, ECE = 9.866, MCE = 24.850). In the simple averaging model and weighted averaging model, the prediction error of the ensemble model also decreased with probability calibration. CONCLUSIONS Among all the methods compared, the proposed model has the lowest prediction error when predicting the 2-year mortality of DLBCL patients. These promising results may indicate that our modeling strategy of applying probability calibration to ensemble learning is successful.
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Affiliation(s)
- Shuanglong Fan
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Zhiqiang Zhao
- Department of Hematology, Shanxi Cancer Hospital, Taiyuan, China
| | - Hongmei Yu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Lei Wang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Chuchu Zheng
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Xueqian Huang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Zhenhuan Yang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Meng Xing
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Qing Lu
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, USA.
| | - Yanhong Luo
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
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Manokaran J, Zabihollahy F, Hamilton-Wright A, Ukwatta E. Detection of COVID-19 from chest x-ray images using transfer learning. J Med Imaging (Bellingham) 2021; 8:017503. [PMID: 34435075 PMCID: PMC8382139 DOI: 10.1117/1.jmi.8.s1.017503] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 08/06/2021] [Indexed: 12/18/2022] Open
Abstract
Purpose: The objective of this study is to develop and evaluate a fully automated, deep learning-based method for detection of COVID-19 infection from chest x-ray images. Approach: The proposed model was developed by replacing the final classifier layer in DenseNet201 with a new network consisting of global averaging layer, batch normalization layer, a dense layer with ReLU activation, and a final classification layer. Then, we performed an end-to-end training using the initial pretrained weights on all the layers. Our model was trained using a total of 8644 images with 4000 images each in normal and pneumonia cases and 644 in COVID-19 cases representing a large real dataset. The proposed method was evaluated based on accuracy, sensitivity, specificity, ROC curve, and F 1 -score using a test dataset comprising 1729 images (129 COVID-19, 800 normal, and 800 pneumonia). As a benchmark, we also compared the results of our method with those of seven state-of-the-art pretrained models and with a lightweight CNN architecture designed from scratch. Results: The proposed model based on DenseNet201 was able to achieve an accuracy of 94% in detecting COVID-19 and an overall accuracy of 92.19%. The model was able to achieve an AUC of 0.99 for COVID-19, 0.97 for normal, and 0.97 for pneumonia. The model was able to outperform alternative models in terms of overall accuracy, sensitivity, and specificity. Conclusions: Our proposed automated diagnostic model yielded an accuracy of 94% in the initial screening of COVID-19 patients and an overall accuracy of 92.19% using chest x-ray images.
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Affiliation(s)
- Jenita Manokaran
- University of Guelph, School of Engineering, Biomedical Engineering, Guelph, Ontario, Canada
| | - Fatemeh Zabihollahy
- The Johns Hopkins University, School of Medicine, Department of Radiation Oncology and Molecular Radiation Sciences, Baltimore, Maryland, United States
| | | | - Eranga Ukwatta
- University of Guelph, School of Engineering, Biomedical Engineering, Guelph, Ontario, Canada
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75
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Ahmed S, Kim BC, Lee KH, Jung HY. Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging. PLoS One 2020; 15:e0242712. [PMID: 33290403 PMCID: PMC7723284 DOI: 10.1371/journal.pone.0242712] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 11/07/2020] [Indexed: 11/26/2022] Open
Abstract
Patches from three orthogonal views of selected cerebral regions can be utilized to learn convolutional neural network (CNN) models for staging the Alzheimer disease (AD) spectrum including preclinical AD, mild cognitive impairment due to AD, and dementia due to AD and normal controls. Hippocampi, amygdalae and insulae were selected from the volumetric analysis of structured magnetic resonance images (MRIs). Three-view patches (TVPs) from these regions were fed to the CNN for training. MRIs were classified with the SoftMax-normalized scores of individual model predictions on TVPs. The significance of each region of interest (ROI) for staging the AD spectrum was evaluated and reported. The results of the ensemble classifier are compared with state-of-the-art methods using the same evaluation metrics. Patch-based ROI ensembles provide comparable diagnostic performance for AD staging. In this work, TVP-based ROI analysis using a CNN provides informative landmarks in cerebral MRIs and may have significance in clinical studies and computer-aided diagnosis system design.
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Affiliation(s)
- Samsuddin Ahmed
- Department of Computer Engineering, Chosun University, Gwangju, South Korea
| | - Byeong C. Kim
- Gwangju Alzheimer’s disease and Related Dementias Cohort Research Center, Chosun University, Gwangju, Korea
- Department of Neurology, Chonnam National University Medical School, Gwangju, South Korea
| | - Kun Ho Lee
- Gwangju Alzheimer’s disease and Related Dementias Cohort Research Center, Chosun University, Gwangju, Korea
- Department of Biomedical Science, Chosun University, Gwangju, South Korea
- Korea Brain Research Institute, Daegu, Korea
| | - Ho Yub Jung
- Department of Computer Engineering, Chosun University, Gwangju, South Korea
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Qian X, Fu H, Shi W, Chen T, Fu Y, Shan F, Xue X. M 3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening From CT Imaging. IEEE J Biomed Health Inform 2020; 24:3539-3550. [PMID: 33048773 PMCID: PMC8545176 DOI: 10.1109/jbhi.2020.3030853] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 09/26/2020] [Accepted: 10/06/2020] [Indexed: 11/10/2022]
Abstract
To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic. Considering the limited training cases and resources (e.g, time and budget), we propose a Multi-task Multi-slice Deep Learning System (M 3Lung-Sys) for multi-class lung pneumonia screening from CT imaging, which only consists of two 2D CNN networks, i.e., slice- and patient-level classification networks. The former aims to seek the feature representations from abundant CT slices instead of limited CT volumes, and for the overall pneumonia screening, the latter one could recover the temporal information by feature refinement and aggregation between different slices. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3Lung-Sys also be able to locate the areas of relevant lesions, without any pixel-level annotation. To further demonstrate the effectiveness of our model, we conduct extensive experiments on a chest CT imaging dataset with a total of 734 patients (251 healthy people, 245 COVID-19 patients, 105 H1N1 patients, and 133 CAP patients). The quantitative results with plenty of metrics indicate the superiority of our proposed model on both slice- and patient-level classification tasks. More importantly, the generated lesion location maps make our system interpretable and more valuable to clinicians.
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Affiliation(s)
- Xuelin Qian
- Shanghai Key Lab of Intelligent Information Processing, School of Computer ScienceFudan UniversityShanghai200433China
| | - Huazhu Fu
- Inception Institute of Artificial IntelligenceAbu DhabiUAE
| | - Weiya Shi
- Department of Radiology, Shanghai Public Health Clinical CenterFudan UniversityShanghai201508China
| | - Tao Chen
- School of Information Science and TechnologyFudan UniversityShanghai200433China
| | - Yanwei Fu
- School of Data Science, MOE Frontiers Center for Brain Science, Shanghai Key Lab of Intelligent Information ProcessingFudan UniversityShanghai200433China
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical CenterFudan UniversityShanghai201508China
| | - Xiangyang Xue
- Shanghai Key Lab of Intelligent Information Processing, School of Computer ScienceFudan UniversityShanghai200433China
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Hussain L, Nguyen T, Li H, Abbasi AA, Lone KJ, Zhao Z, Zaib M, Chen A, Duong TQ. Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection. Biomed Eng Online 2020; 19:88. [PMID: 33239006 PMCID: PMC7686836 DOI: 10.1186/s12938-020-00831-x] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 11/17/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. PURPOSE The study aimed at developing an AI imaging analysis tool to classify COVID-19 lung infection based on portable CXRs. MATERIALS AND METHODS Public datasets of COVID-19 (N = 130), bacterial pneumonia (N = 145), non-COVID-19 viral pneumonia (N = 145), and normal (N = 138) CXRs were analyzed. Texture and morphological features were extracted. Five supervised machine-learning AI algorithms were used to classify COVID-19 from other conditions. Two-class and multi-class classification were performed. Statistical analysis was done using unpaired two-tailed t tests with unequal variance between groups. Performance of classification models used the receiver-operating characteristic (ROC) curve analysis. RESULTS For the two-class classification, the accuracy, sensitivity and specificity were, respectively, 100%, 100%, and 100% for COVID-19 vs normal; 96.34%, 95.35% and 97.44% for COVID-19 vs bacterial pneumonia; and 97.56%, 97.44% and 97.67% for COVID-19 vs non-COVID-19 viral pneumonia. For the multi-class classification, the combined accuracy and AUC were 79.52% and 0.87, respectively. CONCLUSION AI classification of texture and morphological features of portable CXRs accurately distinguishes COVID-19 lung infection in patients in multi-class datasets. Deep-learning methods have the potential to improve diagnostic efficiency and accuracy for portable CXRs.
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Affiliation(s)
- Lal Hussain
- Department of Computer Science and IT, King Abdullah Campus, University of Azad Jammu and Kashmir, Muzaffarabad, 13100, Azad Kashmir, Pakistan.
- Department of Computer Science and IT, Neelum Campus, University of Azad Jammu and Kashmir, Athmuqam, 13230, Azad Kashmir, Pakistan.
| | - Tony Nguyen
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA
| | - Haifang Li
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA
| | - Adeel A Abbasi
- Department of Computer Science and IT, King Abdullah Campus, University of Azad Jammu and Kashmir, Muzaffarabad, 13100, Azad Kashmir, Pakistan
| | - Kashif J Lone
- Department of Computer Science and IT, King Abdullah Campus, University of Azad Jammu and Kashmir, Muzaffarabad, 13100, Azad Kashmir, Pakistan
| | - Zirun Zhao
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA
| | - Mahnoor Zaib
- Department of Computer Science and IT, Neelum Campus, University of Azad Jammu and Kashmir, Athmuqam, 13230, Azad Kashmir, Pakistan
| | - Anne Chen
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA
| | - Tim Q Duong
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA
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Ramírez J, Górriz JM, Ortiz A, Cole JH, Dyrba M. Editorial: Deep Learning in Aging Neuroscience. Front Neuroinform 2020; 14:573974. [PMID: 33209104 PMCID: PMC7649163 DOI: 10.3389/fninf.2020.573974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 09/16/2020] [Indexed: 11/30/2022] Open
Affiliation(s)
- Javier Ramírez
- Department Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Juan M Górriz
- Department Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Andrés Ortiz
- Department of Communications Engineering, University of Málaga, Málaga, Spain
| | - James H Cole
- Department of Computer Science, Centre for Medical Image Computing, University College London, London, United Kingdom.,Dementia Research Centre, Institute of Neurology, University College London, London, United Kingdom
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
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Zhang X, Yao L, Wang X, Monaghan JJM, Mcalpine D, Zhang Y. A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. J Neural Eng 2020; 18. [PMID: 33171452 DOI: 10.1088/1741-2552/abc902] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 11/10/2020] [Indexed: 12/25/2022]
Abstract
Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide a comprehensive survey of the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.
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Affiliation(s)
- Xiang Zhang
- Harvard University, Cambridge, Massachusetts, UNITED STATES
| | - Lina Yao
- University of New South Wales, Sydney, New South Wales, AUSTRALIA
| | - Xianzhi Wang
- Faculty of Engineering and IT, University of Technology Sydney, 81 Broadway, Ultimo, Sydney, New South Wales, 2007, AUSTRALIA
| | | | - David Mcalpine
- Macquarie University, Sydney, New South Wales, AUSTRALIA
| | - Yu Zhang
- Stanford University, Stanford, California, 94305-6104, UNITED STATES
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80
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Nanni L, Interlenghi M, Brahnam S, Salvatore C, Papa S, Nemni R, Castiglioni I. Comparison of Transfer Learning and Conventional Machine Learning Applied to Structural Brain MRI for the Early Diagnosis and Prognosis of Alzheimer's Disease. Front Neurol 2020; 11:576194. [PMID: 33250847 PMCID: PMC7674838 DOI: 10.3389/fneur.2020.576194] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 09/30/2020] [Indexed: 01/22/2023] Open
Abstract
Alzheimer's Disease (AD) is the most common neurodegenerative disease, with 10% prevalence in the elder population. Conventional Machine Learning (ML) was proven effective in supporting the diagnosis of AD, while very few studies investigated the performance of deep learning and transfer learning in this complex task. In this paper, we evaluated the potential of ensemble transfer-learning techniques, pretrained on generic images and then transferred to structural brain MRI, for the early diagnosis and prognosis of AD, with respect to a fusion of conventional-ML approaches based on Support Vector Machine directly applied to structural brain MRI. Specifically, more than 600 subjects were obtained from the ADNI repository, including AD, Mild Cognitive Impaired converting to AD (MCIc), Mild Cognitive Impaired not converting to AD (MCInc), and cognitively-normal (CN) subjects. We used T1-weighted cerebral-MRI studies to train: (1) an ensemble of five transfer-learning architectures pretrained on generic images; (2) a 3D Convolutional Neutral Network (CNN) trained from scratch on MRI volumes; and (3) a fusion of two conventional-ML classifiers derived from different feature extraction/selection techniques coupled to SVM. The AD-vs-CN, MCIc-vs-CN, MCIc-vs-MCInc comparisons were investigated. The ensemble transfer-learning approach was able to effectively discriminate AD from CN with 90.2% AUC, MCIc from CN with 83.2% AUC, and MCIc from MCInc with 70.6% AUC, showing comparable or slightly lower results with the fusion of conventional-ML systems (AD from CN with 93.1% AUC, MCIc from CN with 89.6% AUC, and MCIc from MCInc with AUC in the range of 69.1-73.3%). The deep-learning network trained from scratch obtained lower performance than either the fusion of conventional-ML systems and the ensemble transfer-learning, due to the limited sample of images used for training. These results open new prospective on the use of transfer learning combined with neuroimages for the automatic early diagnosis and prognosis of AD, even if pretrained on generic images.
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Affiliation(s)
- Loris Nanni
- Department of Information Engineering, University of Padua, Padua, Italy
| | - Matteo Interlenghi
- Institute of Molecular Bioimaging and Physiology, National Research Council of Italy (IBFM-CNR), Milan, Italy
| | - Sheryl Brahnam
- Department of IT and Cybersecurity, Missouri State University, Springfield, MO, United States
| | - Christian Salvatore
- Department of Science, Technology and Society, Scuola Universitaria Superiore IUSS Pavia, Pavia, Italy
- DeepTrace Technologies S.R.L., Milan, Italy
| | - Sergio Papa
- Centro Diagnostico Italiano S.p.A., Milan, Italy
| | | | - Isabella Castiglioni
- Institute of Molecular Bioimaging and Physiology, National Research Council of Italy (IBFM-CNR), Milan, Italy
- Department of Physics “G. Occhialini”, University of Milano Bicocca, Milan, Italy
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81
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Choudhury A, Renjilian E, Asan O. Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review. JAMIA Open 2020; 3:459-471. [PMID: 33215079 PMCID: PMC7660963 DOI: 10.1093/jamiaopen/ooaa034] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 06/26/2020] [Accepted: 07/11/2020] [Indexed: 12/13/2022] Open
Abstract
Objectives Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients’ (age 65 years and above) functional ability, physical health, and cognitive well-being. The majority of these patients suffer from multiple chronic conditions and require special attention. Recently, hospitals utilize various artificial intelligence (AI) systems to improve care for elderly patients. The purpose of this systematic literature review is to understand the current use of AI systems, particularly machine learning (ML), in geriatric clinical care for chronic diseases. Materials and Methods We restricted our search to eight databases, namely PubMed, WorldCat, MEDLINE, ProQuest, ScienceDirect, SpringerLink, Wiley, and ERIC, to analyze research articles published in English between January 2010 and June 2019. We focused on studies that used ML algorithms in the care of geriatrics patients with chronic conditions. Results We identified 35 eligible studies and classified in three groups: psychological disorder (n = 22), eye diseases (n = 6), and others (n = 7). This review identified the lack of standardized ML evaluation metrics and the need for data governance specific to health care applications. Conclusion More studies and ML standardization tailored to health care applications are required to confirm whether ML could aid in improving geriatric clinical care.
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Affiliation(s)
- Avishek Choudhury
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Emily Renjilian
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Onur Asan
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, New Jersey, USA
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Sekhar LN, Juric-Sekhar G, Qazi Z, Patel A, McGrath LB, Pridgeon J, Kalavakonda N, Hannaford B. The Future of Skull Base Surgery: A View Through Tinted Glasses. World Neurosurg 2020; 142:29-42. [PMID: 32599213 PMCID: PMC7319930 DOI: 10.1016/j.wneu.2020.06.172] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 06/19/2020] [Accepted: 06/21/2020] [Indexed: 01/06/2023]
Abstract
In the present report, we have broadly outlined the potential advances in the field of skull base surgery, which might occur within the next 20 years based on the many areas of current research in biology and technology. Many of these advances will also be broadly applicable to other areas of neurosurgery. We have grounded our predictions for future developments in an exploration of what patients and surgeons most desire as outcomes for care. We next examined the recent developments in the field and outlined several promising areas of future improvement in skull base surgery, per se, as well as identifying the new hospital support systems needed to accommodate these changes. These include, but are not limited to, advances in imaging, Raman spectroscopy and microscopy, 3-dimensional printing and rapid prototyping, master-slave and semiautonomous robots, artificial intelligence applications in all areas of medicine, telemedicine, and green technologies in hospitals. In addition, we have reviewed the therapeutic approaches using nanotechnology, genetic engineering, antitumor antibodies, and stem cell technologies to repair damage caused by traumatic injuries, tumors, and iatrogenic injuries to the brain and cranial nerves. Additionally, we have discussed the training requirements for future skull base surgeons and stressed the need for adaptability and change. However, the essential requirements for skull base surgeons will remain unchanged, including knowledge, attention to detail, technical skill, innovation, judgment, and compassion. We believe that active involvement in these rapidly evolving technologies will enable us to shape some of the future of our discipline to address the needs of both patients and our profession.
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Affiliation(s)
- Laligam N Sekhar
- Department of Neurosurgery, University of Washington, Seattle, Washington, USA.
| | | | - Zeeshan Qazi
- Department of Neurosurgery, University of Washington, Seattle, Washington, USA
| | - Anoop Patel
- Department of Neurosurgery, University of Washington, Seattle, Washington, USA
| | - Lynn B McGrath
- Department of Neurosurgery, University of Washington, Seattle, Washington, USA
| | - James Pridgeon
- Department of Neurosurgery, University of Washington, Seattle, Washington, USA
| | - Niveditha Kalavakonda
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
| | - Blake Hannaford
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
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Miyagi Y, Habara T, Hirata R, Hayashi N. Predicting a live birth by artificial intelligence incorporating both the blastocyst image and conventional embryo evaluation parameters. Artif Intell Med Imaging 2020. [DOI: 10.35711/wjbc.v1.i3.87] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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Miyagi Y, Habara T, Hirata R, Hayashi N. Predicting a live birth by artificial intelligence incorporating both the blastocyst image and conventional embryo evaluation parameters. Artif Intell Med Imaging 2020; 1:94-107. [DOI: 10.35711/aimi.v1.i3.94] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 09/15/2020] [Accepted: 09/19/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The achievement of live birth is the goal of assisted reproductive technology in reproductive medicine. When the selected blastocyst is transferred to the uterus, the degree of implantation of the blastocyst is evaluated by microscopic inspection, and the result is only about 30%-40%, and the method of predicting live birth from the blastocyst image is unknown. Live births correlate with several clinical conventional embryo evaluation parameters (CEE), such as maternal age. Therefore, it is necessary to develop artificial intelligence (AI) that combines blastocyst images and CEE to predict live births.
AIM To develop an AI classifier for blastocyst images and CEE to predict the probability of achieving a live birth.
METHODS A total of 5691 images of blastocysts on the fifth day after oocyte retrieval obtained from consecutive patients from January 2009 to April 2017 with fully deidentified data were retrospectively enrolled with explanations to patients and a website containing additional information with an opt-out option. We have developed a system in which the original architecture of the deep learning neural network is used to predict the probability of live birth from a blastocyst image and CEE.
RESULTS The live birth rate was 0.387 (= 1587/4104 cases). The number of independent clinical information for predicting live birth is 10, which significantly avoids multicollinearity. A single AI classifier is composed of ten layers of convolutional neural networks, and each elementwise layer of ten factors is developed and obtained with 42792 as the number of training data points and 0.001 as the L2 regularization value. The accuracy, sensitivity, specificity, negative predictive value, positive predictive value, Youden J index, and area under the curve values for predicting live birth are 0.743, 0.638, 0.789, 0.831, 0.573, 0.427, and 0.740, respectively. The optimal cut-off point of the receiver operator characteristic curve is 0.207.
CONCLUSION AI classifiers have the potential of predicting live births that humans cannot predict. Artificial intelligence may make progress in assisted reproductive technology.
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Affiliation(s)
- Yasunari Miyagi
- Department of Artificial Intelligence, Medical Data Labo, Okayama 703-8267, Japan
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, Hidaka 350-1298, Saitama, Japan
| | - Toshihiro Habara
- Department of Reproduction, Okayama Couples' Clinic, Okayama 701-1152, Japan
| | - Rei Hirata
- Department of Reproduction, Okayama Couples' Clinic, Okayama 701-1152, Japan
| | - Nobuyoshi Hayashi
- Department of Reproduction, Okayama Couples' Clinic, Okayama 701-1152, Japan
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Hendrickx JO, van Gastel J, Leysen H, Martin B, Maudsley S. High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases. Pharmacol Rev 2020; 72:191-217. [PMID: 31843941 DOI: 10.1124/pr.119.017921] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.
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Affiliation(s)
- Jhana O Hendrickx
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Jaana van Gastel
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Hanne Leysen
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Bronwen Martin
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Stuart Maudsley
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
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Zhu M, Yang Q, Dong J, Zhang G, Gou X, Rong H, Paul P, Neri F. An Adaptive Optimization Spiking Neural P System for Binary Problems. Int J Neural Syst 2020; 31:2050054. [PMID: 32938261 DOI: 10.1142/s0129065720500549] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Optimization Spiking Neural P System (OSNPS) is the first membrane computing model to directly derive an approximate solution of combinatorial problems with a specific reference to the 0/1 knapsack problem. OSNPS is composed of a family of parallel Spiking Neural P Systems (SNPS) that generate candidate solutions of the binary combinatorial problem and a Guider algorithm that adjusts the spiking probabilities of the neurons of the P systems. Although OSNPS is a pioneering structure in membrane computing optimization, its performance is competitive with that of modern and sophisticated metaheuristics for the knapsack problem only in low dimensional cases. In order to overcome the limitations of OSNPS, this paper proposes a novel Dynamic Guider algorithm which employs an adaptive learning and a diversity-based adaptation to control its moving operators. The resulting novel membrane computing model for optimization is here named Adaptive Optimization Spiking Neural P System (AOSNPS). Numerical result shows that the proposed approach is effective to solve the 0/1 knapsack problems and outperforms multiple various algorithms proposed in the literature to solve the same class of problems even for a large number of items (high dimensionality). Furthermore, case studies show that a AOSNPS is effective in fault sections estimation of power systems in different types of fault cases: including a single fault, multiple faults and multiple faults with incomplete and uncertain information in the IEEE 39 bus system and IEEE 118 bus system.
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Affiliation(s)
- Ming Zhu
- School of Control Engineering, Chengdu University of Information Technology, Chengdu 610225, P. R. China
| | - Qiang Yang
- School of Control Engineering, Chengdu University of Information Technology, Chengdu 610225, P. R. China
| | - Jianping Dong
- College of Information Science and Technology, Chengdu University of Technology, Chengdu 610059, P. R. China
| | - Gexiang Zhang
- College of Information Science and Technology, Chengdu University of Technology, Chengdu 610059, P. R. China
| | - Xiantai Gou
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, P. R. China
| | - Haina Rong
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, P. R. China
| | - Prithwineel Paul
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, P. R. China
| | - Ferrante Neri
- COL Laboratory, School of Computer Science, University of Nottingham, Nottingham, UK
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87
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Graña M, Aguilar-Moreno M, De Lope Asiain J, Araquistain IB, Garmendia X. Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals. Int J Neural Syst 2020; 30:2050053. [DOI: 10.1142/s0129065720500537] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous analysis of behavioral ethogram descriptions and neural activity measurements. Wireless electroencephalography (EEG) and wireless inertial measurement units (IMU) allow the realization of experimental data recording with improved ecological validity where the subjects can be carrying out natural activities while data recording is minimally invasive. Specifically, we aim to show that EEG and IMU data fusion allows improved human activity recognition in a natural setting. We have defined an experimental protocol composed of natural sitting, standing and walking activities, and we have recruited subjects in two sites: in-house ([Formula: see text]) and out-house ([Formula: see text]) populations with different demographics. Experimental protocol data capture was carried out with validated commercial systems. Classifier model training and validation were carried out with scikit-learn open source machine learning python package. EEG features consist of the amplitude of the standard EEG frequency bands. Inertial features were the instantaneous position of the body tracked points after a moving average smoothing to remove noise. We carry out three validation processes: a 10-fold cross-validation process per experimental protocol repetition, (b) the inference of the ethograms, and (c) the transfer learning from each experimental protocol repetition to the remaining repetitions. The in-house accuracy results were lower and much more variable than the out-house sessions results. In general, random forest was the best performing classifier model. Best cross-validation results, ethogram accuracy, and transfer learning were achieved from the fusion of EEG and IMUs data. Transfer learning behaved poorly compared to classification on the same protocol repetition, but it has accuracy still greater than 0.75 on average for the out-house data sessions. Transfer leaning accuracy among repetitions of the same subject was above 0.88 on average. Ethogram prediction accuracy was above 0.96 on average. Therefore, we conclude that wireless EEG and IMUs allow for the definition of natural experimental designs with high ecological validity toward human computational neuroethology research. The fusion of both EEG and IMUs signals improves activity and ethogram recognition.
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Affiliation(s)
- Manuel Graña
- Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain
| | - Marina Aguilar-Moreno
- Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain
| | - Javier De Lope Asiain
- Department of Artificial Intelligence, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | | | - Xavier Garmendia
- Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain
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88
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Pan Y, Liu M, Lian C, Xia Y, Shen D. Spatially-Constrained Fisher Representation for Brain Disease Identification With Incomplete Multi-Modal Neuroimages. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2965-2975. [PMID: 32217472 PMCID: PMC7485604 DOI: 10.1109/tmi.2020.2983085] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Multi-modal neuroimages, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), can provide complementary structural and functional information of the brain, thus facilitating automated brain disease identification. Incomplete data problem is unavoidable in multi-modal neuroimage studies due to patient dropouts and/or poor data quality. Conventional methods usually discard data-missing subjects, thus significantly reducing the number of training samples. Even though several deep learning methods have been proposed, they usually rely on pre-defined regions-of-interest in neuroimages, requiring disease-specific expert knowledge. To this end, we propose a spatially-constrained Fisher representation framework for brain disease diagnosis with incomplete multi-modal neuroimages. We first impute missing PET images based on their corresponding MRI scans using a hybrid generative adversarial network. With the complete (after imputation) MRI and PET data, we then develop a spatially-constrained Fisher representation network to extract statistical descriptors of neuroimages for disease diagnosis, assuming that these descriptors follow a Gaussian mixture model with a strong spatial constraint (i.e., images from different subjects have similar anatomical structures). Experimental results on three databases suggest that our method can synthesize reasonable neuroimages and achieve promising results in brain disease identification, compared with several state-of-the-art methods.
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Affiliation(s)
- Yongsheng Pan
- Y. Pan and Y. Xia are with the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China. M. Liu, C. Lian, and D. Shen are with the Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
| | - Mingxia Liu
- Y. Pan and Y. Xia are with the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China. M. Liu, C. Lian, and D. Shen are with the Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
| | - Chunfeng Lian
- Y. Pan and Y. Xia are with the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China. M. Liu, C. Lian, and D. Shen are with the Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
| | - Yong Xia
- Y. Pan and Y. Xia are with the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China. M. Liu, C. Lian, and D. Shen are with the Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
| | - Dinggang Shen
- Y. Pan and Y. Xia are with the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China. M. Liu, C. Lian, and D. Shen are with the Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
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89
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Wang M, Lian C, Yao D, Zhang D, Liu M, Shen D. Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network. IEEE Trans Biomed Eng 2020; 67:2241-2252. [PMID: 31825859 PMCID: PMC7439279 DOI: 10.1109/tbme.2019.2957921] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Early identification of dementia at the stage of mild cognitive impairment (MCI) is crucial for timely diagnosis and intervention of Alzheimer's disease (AD). Although several pioneering studies have been devoted to automated AD diagnosis based on resting-state functional magnetic resonance imaging (rs-fMRI), their performance is somewhat limited due to non-effective mining of spatial-temporal dependency. Besides, few of these existing approaches consider the explicit detection and modeling of discriminative brain regions (i.e., network hubs) that are sensitive to AD progression. In this paper, we propose a unique Spatial-Temporal convolutional-recurrent neural Network (STNet) for automated prediction of AD progression and network hub detection from rs-fMRI time series. Our STNet incorporates the spatial-temporal information mining and AD-related hub detection into an end-to-end deep learning model. Specifically, we first partition rs-fMRI time series into a sequence of overlapping sliding windows. A sequence of convolutional components are then designed to capture the local-to-global spatially-dependent patterns within each sliding window, based on which we are able to identify discriminative hubs and characterize their unique contributions to disease diagnosis. A recurrent component with long short-term memory (LSTM) units is further employed to model the whole-brain temporal dependency from the spatially-dependent pattern sequences, thus capturing the temporal dynamics along time. We evaluate the proposed method on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, with results suggesting the effectiveness of our method in both tasks of disease progression prediction and AD-related hub detection.
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Affiliation(s)
- Mingliang Wang
- M. Wang and D. Zhang are with the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China. D. Yao is with Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. C. Lian, M. Liu and D. Shen are with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Chunfeng Lian
- M. Wang and D. Zhang are with the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China. D. Yao is with Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. C. Lian, M. Liu and D. Shen are with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Dongren Yao
- M. Wang and D. Zhang are with the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China. D. Yao is with Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. C. Lian, M. Liu and D. Shen are with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Daoqiang Zhang
- M. Wang and D. Zhang are with the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China. D. Yao is with Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. C. Lian, M. Liu and D. Shen are with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Mingxia Liu
- M. Wang and D. Zhang are with the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China. D. Yao is with Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. C. Lian, M. Liu and D. Shen are with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Dinggang Shen
- M. Wang and D. Zhang are with the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China. D. Yao is with Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. C. Lian, M. Liu and D. Shen are with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599, USA. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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90
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Apicella A, Isgrò F, Prevete R, Tamburrini G. Middle-Level Features for the Explanation of Classification Systems by Sparse Dictionary Methods. Int J Neural Syst 2020; 30:2050040. [PMID: 32727317 DOI: 10.1142/s0129065720500409] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Machine learning (ML) systems are affected by a pervasive lack of transparency. The eXplainable Artificial Intelligence (XAI) research area addresses this problem and the related issue of explaining the behavior of ML systems in terms that are understandable to human beings. In many explanation of XAI approaches, the output of ML systems are explained in terms of low-level features of their inputs. However, these approaches leave a substantive explanatory burden with human users, insofar as the latter are required to map low-level properties into more salient and readily understandable parts of the input. To alleviate this cognitive burden, an alternative model-agnostic framework is proposed here. This framework is instantiated to address explanation problems in the context of ML image classification systems, without relying on pixel relevance maps and other low-level features of the input. More specifically, one obtains sets of middle-level properties of classification inputs that are perceptually salient by applying sparse dictionary learning techniques. These middle-level properties are used as building blocks for explanations of image classifications. The achieved explanations are parsimonious, for their reliance on a limited set of middle-level image properties. And they can be contrastive, because the set of middle-level image properties can be used to explain why the system advanced the proposed classification over other antagonist classifications. In view of its model-agnostic character, the proposed framework is adaptable to a variety of other ML systems and explanation problems.
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Affiliation(s)
- A Apicella
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, 80125 Napoli, Italy
| | - F Isgrò
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, 80125 Napoli, Italy
| | - R Prevete
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, 80125 Napoli, Italy
| | - G Tamburrini
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, 80125 Napoli, Italy
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91
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Identification of Epileptic EEG Signals Using Convolutional Neural Networks. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10124089] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Epilepsy is one of the chronic neurological disorders that is characterized by a sudden burst of excess electricity in the brain. This abnormality appears as a seizure, the detection of which is an important research topic. An important tool used to study brain activity features, neurological disorders and particularly epileptic seizures, is known as electroencephalography (EEG). The visual inspection of epileptic abnormalities in EEG signals by neurologists is time-consuming. Different scientific approaches have been used to accurately detect epileptic seizures from EEG signals, and most of those approaches have obtained good performance. In this study, deep learning based on convolutional neural networks (CNN) was considered to increase the performance of the identification system of epileptic seizures. We applied a cross-validation technique in the design phase of the system. For efficiency, comparative results between other machine-learning approaches and deep CNNs have been obtained. The experiments were performed using standard datasets. The results obtained indicate the efficiency of using CNN in the detection of epilepsy.
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92
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Yamanakkanavar N, Choi JY, Lee B. MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer's Disease: A Survey. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3243. [PMID: 32517304 PMCID: PMC7313699 DOI: 10.3390/s20113243] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 05/25/2020] [Accepted: 06/03/2020] [Indexed: 02/07/2023]
Abstract
Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer's disease (AD) early so that preventative measures can be taken. A detailed analysis of the tissue structures from segmented MRI leads to a more accurate classification of specific brain disorders. Several segmentation methods to diagnose AD have been proposed with varying complexity. Segmentation of the brain structure and classification of AD using deep learning approaches has gained attention as it can provide effective results over a large set of data. Hence, deep learning methods are now preferred over state-of-the-art machine learning methods. We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD. Here, we report how convolutional neural network architectures are used to analyze the anatomical brain structure and diagnose AD, discuss how brain MRI segmentation improves AD classification, describe the state-of-the-art approaches, and summarize their results using publicly available datasets. Finally, we provide insight into current issues and discuss possible future research directions in building a computer-aided diagnostic system for AD.
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Affiliation(s)
- Nagaraj Yamanakkanavar
- Department of Information and Communications Engineering, Chosun University, Gwangju 61452, Korea;
| | - Jae Young Choi
- Division of Computer & Electronic Systems Engineering, Hankuk University of Foreign Studies, Yongin 17035, Korea;
| | - Bumshik Lee
- Department of Information and Communications Engineering, Chosun University, Gwangju 61452, Korea;
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93
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Schmidt D, Villalba Diez J, Ordieres-Meré J, Gevers R, Schwiep J, Molina M. Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning. SENSORS (BASEL, SWITZERLAND) 2020; 20:E2860. [PMID: 32443512 PMCID: PMC7288035 DOI: 10.3390/s20102860] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 05/12/2020] [Accepted: 05/12/2020] [Indexed: 02/06/2023]
Abstract
Achieving the shift towards Industry 4.0 is only feasible through the active integration of the shopfloor into the transformation process. Several shopfloor management (SM) systems can aid this conversion. They form two major factions. The first includes methodologies such as Balanced Scorecard (BSC). A defining feature is rigid structures to fixate on pre-defined goals. Other SM strategies instead concentrate on continuous improvement by giving directions. An example of this group is the "HOSHIN KANRI TREE" (HKT). One way of analyzing the dissimilarities, the advantages and disadvantages of these groups, is to examine the neurological patterns of workers as they are applying these. This paper aims to achieve this evaluation through non-invasive electroencephalography (EEG) sensors, which capture the electrical activity of the brain. A deep learning (DL) soft sensor is used to classify the recorded data with an accuracy of 96.5%. Through this result and an analysis using the correlations of the EEG signals, it has been possible to detect relevant characteristics and differences in the brain's activity. In conclusion, these findings are expected to help assess SM systems and give guidance to Industry 4.0 leaders.
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Affiliation(s)
- Daniel Schmidt
- Department of Business Intelligence, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, 28006 Madrid, Spain;
- Matthews International GmbH, Gutenbergstraße 1-3, 48691 Vreden, Germany; (R.G.); (J.S.)
| | - Javier Villalba Diez
- Department of Business Intelligence, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, 28006 Madrid, Spain;
- Hochschule Heilbronn, Fakultät Management und Vertrieb, Campus Schwäbisch Hall, 74523 Schwäbisch Hall, Germany
- Department of Artificial Intelligence, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Madrid, Spain;
| | - Joaquín Ordieres-Meré
- Department of Business Intelligence, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, 28006 Madrid, Spain;
| | - Roman Gevers
- Matthews International GmbH, Gutenbergstraße 1-3, 48691 Vreden, Germany; (R.G.); (J.S.)
| | - Joerg Schwiep
- Matthews International GmbH, Gutenbergstraße 1-3, 48691 Vreden, Germany; (R.G.); (J.S.)
| | - Martin Molina
- Department of Artificial Intelligence, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Madrid, Spain;
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94
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Pan D, Zeng A, Jia L, Huang Y, Frizzell T, Song X. Early Detection of Alzheimer's Disease Using Magnetic Resonance Imaging: A Novel Approach Combining Convolutional Neural Networks and Ensemble Learning. Front Neurosci 2020; 14:259. [PMID: 32477040 PMCID: PMC7238823 DOI: 10.3389/fnins.2020.00259] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 03/09/2020] [Indexed: 01/25/2023] Open
Abstract
Early detection is critical for effective management of Alzheimer's disease (AD) and screening for mild cognitive impairment (MCI) is common practice. Among several deep-learning techniques that have been applied to assessing structural brain changes on magnetic resonance imaging (MRI), convolutional neural network (CNN) has gained popularity due to its superb efficiency in automated feature learning with the use of a variety of multilayer perceptrons. Meanwhile, ensemble learning (EL) has shown to be beneficial in the robustness of learning-system performance via integrating multiple models. Here, we proposed a classifier ensemble developed by combining CNN and EL, i.e., the CNN-EL approach, to identify subjects with MCI or AD using MRI: i.e., classification between (1) AD and healthy cognition (HC), (2) MCIc (MCI patients who will convert to AD) and HC, and (3) MCIc and MCInc (MCI patients who will not convert to AD). For each binary classification task, a large number of CNN models were trained applying a set of sagittal, coronal, or transverse MRI slices; these CNN models were then integrated into a single ensemble. Performance of the ensemble was evaluated using stratified fivefold cross-validation method for 10 times. The number of the intersection points determined by the most discriminable slices separating two classes in a binary classification task among the sagittal, coronal, and transverse slice sets, transformed into the standard Montreal Neurological Institute (MNI) space, acted as an indicator to assess the ability of a brain region in which the points were located to classify AD. Thus, the brain regions with most intersection points were considered as those mostly contributing to the early diagnosis of AD. The result revealed an accuracy rate of 0.84 ± 0.05, 0.79 ± 0.04, and 0.62 ± 0.06, respectively, for classifying AD vs. HC, MCIc vs. HC, and MCIc vs. MCInc, comparable to previous reports and a 3D deep learning approach (3D-SENet) based on a more state-of-the-art and popular Squeeze-and-Excitation Networks model using channel attention mechanism. Notably, the intersection points accurately located the medial temporal lobe and several other structures of the limbic system, i.e., brain regions known to be struck early in AD. More interestingly, the classifiers disclosed multiple patterned MRI changes in the brain in AD and MCIc, involving these key regions. These results suggest that as a data-driven method, the combined CNN and EL approach can locate the most discriminable brain regions indicated by the trained ensemble model while the generalization ability of the ensemble model was maximized to successfully capture AD-related brain variations early in the disease process; it can also provide new insights into understanding the complex heterogeneity of whole-brain MRI changes in AD. Further research is needed to examine the clinical implication of the finding, capability of the advocated CNN-EL approach to help understand and evaluate an individual subject's disease status, symptom burden and progress, and the generalizability of the advocated CNN-EL approach to locate the most discriminable brain regions in the detection of other brain disorders such as schizophrenia, autism, and severe depression, in a data-driven way.
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Affiliation(s)
- Dan Pan
- School of Computers, Guangdong University of Technology, Guangzhou, China
| | - An Zeng
- School of Computers, Guangdong University of Technology, Guangzhou, China
- Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China
| | - Longfei Jia
- School of Computers, Guangdong University of Technology, Guangzhou, China
| | - Yin Huang
- School of Computers, Guangdong University of Technology, Guangzhou, China
| | - Tory Frizzell
- SFU ImageTech Lab, Surrey Memorial Hospital, Fraser Health, Surrey, BC, Canada
| | - Xiaowei Song
- SFU ImageTech Lab, Surrey Memorial Hospital, Fraser Health, Surrey, BC, Canada
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95
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Wen J, Thibeau-Sutre E, Diaz-Melo M, Samper-González J, Routier A, Bottani S, Dormont D, Durrleman S, Burgos N, Colliot O. Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation. Med Image Anal 2020; 63:101694. [PMID: 32417716 DOI: 10.1016/j.media.2020.101694] [Citation(s) in RCA: 196] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 03/23/2020] [Accepted: 03/27/2020] [Indexed: 10/24/2022]
Abstract
Numerous machine learning (ML) approaches have been proposed for automatic classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 papers have proposed to use convolutional neural networks (CNN) for AD classification from anatomical MRI. However, the classification performance is difficult to compare across studies due to variations in components such as participant selection, image preprocessing or validation procedure. Moreover, these studies are hardly reproducible because their frameworks are not publicly accessible and because implementation details are lacking. Lastly, some of these papers may report a biased performance due to inadequate or unclear validation or model selection procedures. In the present work, we aim to address these limitations through three main contributions. First, we performed a systematic literature review. We identified four main types of approaches: i) 2D slice-level, ii) 3D patch-level, iii) ROI-based and iv) 3D subject-level CNN. Moreover, we found that more than half of the surveyed papers may have suffered from data leakage and thus reported biased performance. Our second contribution is the extension of our open-source framework for classification of AD using CNN and T1-weighted MRI. The framework comprises previously developed tools to automatically convert ADNI, AIBL and OASIS data into the BIDS standard, and a modular set of image preprocessing procedures, classification architectures and evaluation procedures dedicated to deep learning. Finally, we used this framework to rigorously compare different CNN architectures. The data was split into training/validation/test sets at the very beginning and only the training/validation sets were used for model selection. To avoid any overfitting, the test sets were left untouched until the end of the peer-review process. Overall, the different 3D approaches (3D-subject, 3D-ROI, 3D-patch) achieved similar performances while that of the 2D slice approach was lower. Of note, the different CNN approaches did not perform better than a SVM with voxel-based features. The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://github.com/aramis-lab/AD-DL.
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Affiliation(s)
- Junhao Wen
- Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Inria, Aramis project-team, Paris F-75013, France
| | - Elina Thibeau-Sutre
- Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Inria, Aramis project-team, Paris F-75013, France
| | - Mauricio Diaz-Melo
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Jorge Samper-González
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Alexandre Routier
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Simona Bottani
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Didier Dormont
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Department of Neuroradiology, AP-HP, Hôpital de la PitiéSalpêtrière, Paris F-75013, France
| | - Stanley Durrleman
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Ninon Burgos
- Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Inria, Aramis project-team, Paris F-75013, France
| | - Olivier Colliot
- Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Inria, Aramis project-team, Paris F-75013, France; Department of Neuroradiology, AP-HP, Hôpital de la PitiéSalpêtrière, Paris F-75013, France; Department of Neurology, AP-HP, Hôpital de la PitiéSalpêtrière, Paris F-75013, France.
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96
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Abstract
Currently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many cases, one drug cannot target all necessary points of intervention. For example, in cancer therapy, a physician often meets a patient having a genomic profile including more than five molecular aberrations. Drug combination therapy has been an area of interest for a while, for example the classical work of Loewe devoted to the synergism of drugs was published in 1928-and it is still used in calculations for optimal drug combinations. More recently, over the past several years, there has been an explosion in the available information related to the properties of drugs and the biomedical parameters of patients. For the drugs, hundreds of 2D and 3D molecular descriptors for medicines are now available, while for patients, large data sets related to genetic/proteomic and metabolomics profiles of the patients are now available, as well as the more traditional data relating to the histology, history of treatments, pretreatment state of the organism, etc. Moreover, during disease progression, the genetic profile can change. Thus, the ability to optimize drug combinations for each patient is rapidly moving beyond the comprehension and capabilities of an individual physician. This is the reason, that biomedical informatics methods have been developed and one of the more promising directions in this field is the application of artificial intelligence (AI). In this review, we discuss several AI methods that have been successfully implemented in several instances of combination drug therapy from HIV, hypertension, infectious diseases to cancer. The data clearly show that the combination of rule-based expert systems with machine learning algorithms may be promising direction in this field.
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97
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Ebrahimighahnavieh MA, Luo S, Chiong R. Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 187:105242. [PMID: 31837630 DOI: 10.1016/j.cmpb.2019.105242] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 11/13/2019] [Accepted: 11/25/2019] [Indexed: 06/10/2023]
Abstract
Alzheimer's Disease (AD) is one of the leading causes of death in developed countries. From a research point of view, impressive results have been reported using computer-aided algorithms, but clinically no practical diagnostic method is available. In recent years, deep models have become popular, especially in dealing with images. Since 2013, deep learning has begun to gain considerable attention in AD detection research, with the number of published papers in this area increasing drastically since 2017. Deep models have been reported to be more accurate for AD detection compared to general machine learning techniques. Nevertheless, AD detection is still challenging, and for classification, it requires a highly discriminative feature representation to separate similar brain patterns. This paper reviews the current state of AD detection using deep learning. Through a systematic literature review of over 100 articles, we set out the most recent findings and trends. Specifically, we review useful biomarkers and features (personal information, genetic data, and brain scans), the necessary pre-processing steps, and different ways of dealing with neuroimaging data originating from single-modality and multi-modality studies. Deep models and their performance are described in detail. Although deep learning has achieved notable performance in detecting AD, there are several limitations, especially regarding the availability of datasets and training procedures.
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Affiliation(s)
| | - Suhuai Luo
- The University of Newcastle, University Drive, Callaghan 2308, Australia
| | - Raymond Chiong
- The University of Newcastle, University Drive, Callaghan 2308, Australia.
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98
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Alasal SA, AL Bashabsheh E, Najadat H. Overview of Positron Emission Tomography (PET) for Brain Functions Degeneration Classification. 2020 11TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION SYSTEMS (ICICS) 2020. [DOI: 10.1109/icics49469.2020.239500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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99
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Syed-Abdul S, Firdani RP, Chung HJ, Uddin M, Hur M, Park JH, Kim HW, Gradišek A, Dovgan E. Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data. Sci Rep 2020; 10:4583. [PMID: 32179774 PMCID: PMC7075908 DOI: 10.1038/s41598-020-61247-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 02/14/2020] [Indexed: 11/28/2022] Open
Abstract
Cell Population Data (CPD) provides various blood cell parameters that can be used for differential diagnosis. Data analytics using Machine Learning (ML) have been playing a pivotal role in revolutionizing medical diagnostics. This research presents a novel approach of using ML algorithms for screening hematologic malignancies using CPD. The data collection was done at Konkuk University Medical Center, Seoul. A total of (882 cases: 457 hematologic malignancy and 425 hematologic non-malignancy) were used for analysis. In our study, seven machine learning models, i.e., SGD, SVM, RF, DT, Linear model, Logistic regression, and ANN, were used. In order to measure the performance of our ML models, stratified 10-fold cross validation was performed, and metrics, such as accuracy, precision, recall, and AUC were used. We observed outstanding performance by the ANN model as compared to other ML models. The diagnostic ability of ANN achieved the highest accuracy, precision, recall, and AUC ± Standard Deviation as follows: 82.8%, 82.8%, 84.9%, and 93.5% ± 2.6 respectively. ANN algorithm based on CPD appeared to be an efficient aid for clinical laboratory screening of hematologic malignancies. Our results encourage further work of applying ML to wider field of clinical practice.
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Affiliation(s)
- Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan
| | - Rianda-Putra Firdani
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan
| | - Hee-Jung Chung
- Department of Laboratory Medicine, Konkuk University School of Medicine, Seoul, South Korea.
- CPBMI Consortium, Biomedical Informatics Training and Education Center of Seoul National University Hospital, Seoul, South Korea.
| | - Mohy Uddin
- Executive Office, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard - Health Affairs, Riyadh, Kingdom of Saudi Arabia
| | - Mina Hur
- Department of Laboratory Medicine, Konkuk University School of Medicine, Seoul, South Korea
| | - Jae Hyeon Park
- Department of Laboratory Medicine, Seoul National University Hospital, Seoul, South Korea
- CPBMI Consortium, Biomedical Informatics Training and Education Center of Seoul National University Hospital, Seoul, South Korea
| | - Hyung Woo Kim
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, South Korea
- CPBMI Consortium, Biomedical Informatics Training and Education Center of Seoul National University Hospital, Seoul, South Korea
| | - Anton Gradišek
- Department of Intelligent Systems, Jožef Stefan Institute, Jamova cesta 39, SI-1000, Ljubljana, Slovenia
| | - Erik Dovgan
- Department of Intelligent Systems, Jožef Stefan Institute, Jamova cesta 39, SI-1000, Ljubljana, Slovenia
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100
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Miyagi Y, Takehara K, Nagayasu Y, Miyake T. Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images combined with HPV types. Oncol Lett 2020; 19:1602-1610. [PMID: 31966086 PMCID: PMC6956417 DOI: 10.3892/ol.2019.11214] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 10/10/2019] [Indexed: 01/16/2023] Open
Abstract
The aim of the present study was to explore the feasibility of using deep learning, such as artificial intelligence (AI), to classify cervical squamous epithelial lesions (SILs) from colposcopy images combined with human papilloma virus (HPV) types. Among 330 patients who underwent colposcopy and biopsy performed by gynecological oncologists, a total of 253 patients with confirmed HPV typing tests were enrolled in the present study. Of these patients, 210 were diagnosed with high-grade SIL (HSIL) and 43 were diagnosed with low-grade SIL (LSIL). An original AI classifier with a convolutional neural network catenating with an HPV tensor was developed and trained. The accuracy of the AI classifier and gynecological oncologists was 0.941 and 0.843, respectively. The AI classifier performed better compared with the oncologists, although not significantly. The sensitivity, specificity, positive predictive value, negative predictive value, Youden's J index and the area under the receiver-operating characteristic curve ± standard error for AI colposcopy combined with HPV types and pathological results were 0.956 (43/45), 0.833 (5/6), 0.977 (43/44), 0.714 (5/7), 0.789 and 0.963±0.026, respectively. Although further study is required, the clinical use of AI for the classification of HSIL/LSIL by both colposcopy and HPV type may be feasible.
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Affiliation(s)
- Yasunari Miyagi
- Medical Data Labo, Okayama, Okayama 703-8267, Japan
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, Hidaka, Saitama 350-1298, Japan
- Miyake Ofuku Clinic, Okayama, Okayama 701-0204, Japan
| | - Kazuhiro Takehara
- Department of Gynecologic Oncology, National Hospital Organization Shikoku Cancer Center, Matsuyama, Ehime 791-0208, Japan
| | - Yoko Nagayasu
- Medical Data Labo, Okayama, Okayama 703-8267, Japan
- Department of Obstetrics and Gynecology, Osaka Medical College, Takatsuki, Osaka 569-0801, Japan
| | - Takahito Miyake
- Department of Obstetrics and Gynecology, Miyake Clinic, Okayama, Okayama 701-0204, Japan
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