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Uparela-Reyes MJ, Villegas-Trujillo LM, Cespedes J, Velásquez-Vera M, Rubiano AM. Usefulness of Artificial Intelligence in Traumatic Brain Injury: A Bibliometric Analysis and Mini-review. World Neurosurg 2024; 188:83-92. [PMID: 38759786 DOI: 10.1016/j.wneu.2024.05.065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 05/10/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Traumatic brain injury (TBI) has become a major source of disability worldwide, increasing the interest in algorithms that use artificial intelligence (AI) to optimize the interpretation of imaging studies, prognosis estimation, and critical care issues. In this study we present a bibliometric analysis and mini-review on the main uses that have been developed for TBI in AI. METHODS The results informing this review come from a Scopus database search as of April 15, 2023. The bibliometric analysis was carried out via the mapping bibliographic metrics method. Knowledge mapping was made in the VOSviewer software (V1.6.18), analyzing the "link strength" of networks based on co-occurrence of key words, countries co-authorship, and co-cited authors. In the mini-review section, we highlight the main findings and contributions of the studies. RESULTS A total of 495 scientific publications were identified from 2000 to 2023, with 9262 citations published since 2013. Among the 160 journals identified, The Journal of Neurotrauma, Frontiers in Neurology, and PLOS ONE were those with the greatest number of publications. The most frequently co-occurring key words were: "machine learning", "deep learning", "magnetic resonance imaging", and "intracranial pressure". The United States accounted for more collaborations than any other country, followed by United Kingdom and China. Four co-citation author clusters were found, and the top 20 papers were divided into reviews and original articles. CONCLUSIONS AI has become a relevant research field in TBI during the last 20 years, demonstrating great potential in imaging, but a more modest performance for prognostic estimation and neuromonitoring.
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Affiliation(s)
- Maria José Uparela-Reyes
- Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; Neurosurgery Section, Hospital Universitario del Valle, Cali, Colombia.
| | - Lina María Villegas-Trujillo
- Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; School of Biomedical Sciences, Universidad del Valle, Cali, Colombia
| | - Jorge Cespedes
- Comprehensive Epilepsy Center, Yale University, New Haven, Connecticut, USA
| | - Miguel Velásquez-Vera
- Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; Neurosurgery Section, Hospital Universitario del Valle, Cali, Colombia
| | - Andrés M Rubiano
- Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; Neurosurgery Section, Hospital Universitario del Valle, Cali, Colombia; INUB-Meditech Research Group, Neurosciences Institute, Universidad El Bosque, Bogotá, Colombia
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Gaus R, Pölsterl S, Greimel E, Schulte‐Körne G, Wachinger C. Can we diagnose mental disorders in children? A large-scale assessment of machine learning on structural neuroimaging of 6916 children in the adolescent brain cognitive development study. JCPP ADVANCES 2023; 3:e12184. [PMID: 38054056 PMCID: PMC10694548 DOI: 10.1002/jcv2.12184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 05/09/2023] [Indexed: 12/07/2023] Open
Abstract
Background Prediction of mental disorders based on neuroimaging is an emerging area of research with promising first results in adults. However, research on the unique demographic of children is underrepresented and it is doubtful whether findings obtained on adults can be transferred to children. Methods Using data from 6916 children aged 9-10 in the multicenter Adolescent Brain Cognitive Development study, we extracted 136 regional volume and thickness measures from structural magnetic resonance images to rigorously evaluate the capabilities of machine learning to predict 10 different psychiatric disorders: major depressive disorder, bipolar disorder (BD), psychotic symptoms, attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder, conduct disorder, post-traumatic stress disorder, obsessive-compulsive disorder, generalized anxiety disorder, and social anxiety disorder. For each disorder, we performed cross-validation and assessed whether models discovered a true pattern in the data via permutation testing. Results Two of 10 disorders can be detected with statistical significance when using advanced models that (i) allow for non-linear relationships between neuroanatomy and disorder, (ii) model interdependencies between disorders, and (iii) avoid confounding due to sociodemographic factors: ADHD (AUROC = 0.567, p = 0.002) and BD (AUROC = 0.551, p = 0.002). In contrast, traditional models perform consistently worse and predict only ADHD with statistical significance (AUROC = 0.529, p = 0.002). Conclusion While the modest absolute classification performance does not warrant application in the clinic, our results provide empirical evidence that embracing and explicitly accounting for the complexities of mental disorders via advanced machine learning models can discover patterns that would remain hidden with traditional models.
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Affiliation(s)
- Richard Gaus
- The Lab for Artificial Intelligence in Medical Imaging (AI‐Med)Department of Child and Adolescent PsychiatryLudwig‐Maximilians‐UniversitätMunichGermany
| | - Sebastian Pölsterl
- The Lab for Artificial Intelligence in Medical Imaging (AI‐Med)Department of Child and Adolescent PsychiatryLudwig‐Maximilians‐UniversitätMunichGermany
| | - Ellen Greimel
- Department of Child and Adolescent Psychiatry, Psychosomatics and PsychotherapyUniversity HospitalLudwig‐Maximilians‐UniversitätMunichGermany
| | - Gerd Schulte‐Körne
- Department of Child and Adolescent Psychiatry, Psychosomatics and PsychotherapyUniversity HospitalLudwig‐Maximilians‐UniversitätMunichGermany
| | - Christian Wachinger
- The Lab for Artificial Intelligence in Medical Imaging (AI‐Med)Department of Child and Adolescent PsychiatryLudwig‐Maximilians‐UniversitätMunichGermany
- Department of RadiologyTechnical University of MunichSchool of MedicineMunichGermany
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Vázquez-Marrufo M, Sarrias-Arrabal E, García-Torres M, Martín-Clemente R, Izquierdo G. A systematic review of the application of machine-learning algorithms in multiple sclerosis. Neurologia 2023; 38:577-590. [PMID: 35843587 DOI: 10.1016/j.nrleng.2020.10.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 10/11/2020] [Indexed: 10/17/2022] Open
Abstract
INTRODUCTION The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years. OBJECTIVE We present a systematic review of the application of ML algorithms in MS. MATERIALS AND METHODS We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords "machine learning" and "multiple sclerosis." We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected. CONCLUSIONS After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.
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Affiliation(s)
- M Vázquez-Marrufo
- Departamento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Sevilla, Spain.
| | - E Sarrias-Arrabal
- Departamento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Sevilla, Spain
| | - M García-Torres
- Escuela Politécnica Superior, Universidad Pablo de Olavide, Sevilla, Spain
| | - R Martín-Clemente
- Departamento de Teoría de la Señal y Comunicaciones, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Sevilla, Spain
| | - G Izquierdo
- Unidad de Esclerosis Múltiple, Hospital VITHAS, Sevilla, Spain
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Garcia Santa Cruz B, Husch A, Hertel F. Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions. Front Aging Neurosci 2023; 15:1216163. [PMID: 37539346 PMCID: PMC10394631 DOI: 10.3389/fnagi.2023.1216163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/28/2023] [Indexed: 08/05/2023] Open
Abstract
Parkinson's disease (PD) is a progressive and complex neurodegenerative disorder associated with age that affects motor and cognitive functions. As there is currently no cure, early diagnosis and accurate prognosis are essential to increase the effectiveness of treatment and control its symptoms. Medical imaging, specifically magnetic resonance imaging (MRI), has emerged as a valuable tool for developing support systems to assist in diagnosis and prognosis. The current literature aims to improve understanding of the disease's structural and functional manifestations in the brain. By applying artificial intelligence to neuroimaging, such as deep learning (DL) and other machine learning (ML) techniques, previously unknown relationships and patterns can be revealed in this high-dimensional data. However, several issues must be addressed before these solutions can be safely integrated into clinical practice. This review provides a comprehensive overview of recent ML techniques analyzed for the automatic diagnosis and prognosis of PD in brain MRI. The main challenges in applying ML to medical diagnosis and its implications for PD are also addressed, including current limitations for safe translation into hospitals. These challenges are analyzed at three levels: disease-specific, task-specific, and technology-specific. Finally, potential future directions for each challenge and future perspectives are discussed.
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Affiliation(s)
| | - Andreas Husch
- Imaging AI Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Frank Hertel
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg
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Kim H, Park M. Discovering fashion industry trends in the online news by applying text mining and time series regression analysis. Heliyon 2023; 9:e18048. [PMID: 37539308 PMCID: PMC10395361 DOI: 10.1016/j.heliyon.2023.e18048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 06/30/2023] [Accepted: 07/05/2023] [Indexed: 08/05/2023] Open
Abstract
The growth of digital media usage has accelerated the development of big data technology. According to the agenda-setting theory, news media inform the public regarding major agendas and business cycles. This study investigated 168,786 news documents from 2016 to 2020 related the South Korea fashion business using Python. A total of 19 topics were extracted through latent Dirichlet allocation and then transformed into structured data using a time series approach to analyze significant changes in trends. The results indicate that major fashion industry topics include business management strategies to increase sales, diversification of the retail structure, influence of CEOs, and merchandise marketing activities. Thereafter, statistically significant hot and cold topics were derived to identify the shifts in topic themes. This study expands the fashion business contexts with agenda-setting theory through big data time series analyses and can be referenced for the government agencies to support fashion industry policies.
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Choi H, Sunwoo L, Cho SJ, Baik SH, Bae YJ, Choi BS, Jung C, Kim JH. A Nationwide Web-Based Survey of Neuroradiologists' Perceptions of Artificial Intelligence Software for Neuro-Applications in Korea. Korean J Radiol 2023; 24:454-464. [PMID: 37133213 PMCID: PMC10157324 DOI: 10.3348/kjr.2022.0905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/19/2023] [Accepted: 03/06/2023] [Indexed: 05/04/2023] Open
Abstract
OBJECTIVE We aimed to investigate current expectations and clinical adoption of artificial intelligence (AI) software among neuroradiologists in Korea. MATERIALS AND METHODS In April 2022, a 30-item online survey was conducted by neuroradiologists from the Korean Society of Neuroradiology (KSNR) to assess current user experiences, perceptions, attitudes, and future expectations regarding AI for neuro-applications. Respondents with experience in AI software were further investigated in terms of the number and type of software used, period of use, clinical usefulness, and future scope. Results were compared between respondents with and without experience with AI software through multivariable logistic regression and mediation analyses. RESULTS The survey was completed by 73 respondents, accounting for 21.9% (73/334) of the KSNR members; 72.6% (53/73) were familiar with AI and 58.9% (43/73) had used AI software, with approximately 86% (37/43) using 1-3 AI software programs and 51.2% (22/43) having up to one year of experience with AI software. Among AI software types, brain volumetry software was the most common (62.8% [27/43]). Although 52.1% (38/73) assumed that AI is currently useful in practice, 86.3% (63/73) expected it to be useful for clinical practice within 10 years. The main expected benefits were reducing the time spent on repetitive tasks (91.8% [67/73]) and improving reading accuracy and reducing errors (72.6% [53/73]). Those who experienced AI software were more familiar with AI (adjusted odds ratio, 7.1 [95% confidence interval, 1.81-27.81]; P = 0.005). More than half of the respondents with AI software experience (55.8% [24/43]) agreed that AI should be included in training curriculums, while almost all (95.3% [41/43]) believed that radiologists should coordinate to improve its performance. CONCLUSION A majority of respondents experienced AI software and showed a proactive attitude toward adopting AI in clinical practice, suggesting that AI should be incorporated into training and active participation in AI development should be encouraged.
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Affiliation(s)
- Hyunsu Choi
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Leonard Sunwoo
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
- Center for Artificial Intelligence in Healthcare, Seoul National University Bundang Hospital, Seongnam, Korea.
| | - Se Jin Cho
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Sung Hyun Baik
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Yun Jung Bae
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Byung Se Choi
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Cheolkyu Jung
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Jae Hyoung Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
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Wang Z, Zhou X, Gui Y, Liu M, Lu H. Multiple measurement analysis of resting-state fMRI for ADHD classification in adolescent brain from the ABCD study. Transl Psychiatry 2023; 13:45. [PMID: 36746929 PMCID: PMC9902465 DOI: 10.1038/s41398-023-02309-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 12/22/2022] [Accepted: 01/06/2023] [Indexed: 02/08/2023] Open
Abstract
Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in school-aged children. Its accurate diagnosis looks after patients' interests well with effective treatment, which is important to them and their family. Resting-state functional magnetic resonance imaging (rsfMRI) has been widely used to characterize the abnormal brain function by computing the voxel-wise measures and Pearson's correlation (PC)-based functional connectivity (FC) for ADHD diagnosis. However, exploring the powerful measures of rsfMRI to improve ADHD diagnosis remains a particular challenge. To this end, this paper proposes an automated ADHD classification framework by fusion of multiple measures of rsfMRI in adolescent brain. First, we extract the voxel-wise measures and ROI-wise time series from the brain regions of rsfMRI after preprocessing. Then, to extract the multiple functional connectivities, we compute the PC-derived FCs including the topographical information-based high-order FC (tHOFC) and dynamics-based high-order FC (dHOFC), the sparse representation (SR)-derived FCs including the group SR (GSR), the strength and similarity guided GSR (SSGSR), and sparse low-rank (SLR). Finally, these measures are combined with multiple kernel learning (MKL) model for ADHD classification. The proposed method is applied to the Adolescent Brain and Cognitive Development (ABCD) dataset. The results show that the FCs of dHOFC and SLR perform better than the others. Fusing multiple measures achieves the best classification performance (AUC = 0.740, accuracy = 0.6916), superior to those from the single measure and the previous studies. We have identified the most discriminative FCs and brain regions for ADHD diagnosis, which are consistent with those of published literature.
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Affiliation(s)
- Zhaobin Wang
- grid.16821.3c0000 0004 0368 8293State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China ,grid.16821.3c0000 0004 0368 8293SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaocheng Zhou
- grid.16821.3c0000 0004 0368 8293State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Yuanyuan Gui
- grid.16821.3c0000 0004 0368 8293State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China ,grid.16821.3c0000 0004 0368 8293SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Manhua Liu
- MoE Key Laboratory of Artificial Intelligence, AI Institute, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Hui Lu
- State Key Lab of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. .,SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China. .,Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Center for Biomedical Informatics, Shanghai Children's Hospital, Shanghai, China.
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Seo YD, Lee DA, Park KM. Can Artificial Intelligence Diagnose Transient Global Amnesia Using Electroencephalography Data? J Clin Neurol 2023; 19:36-43. [PMID: 36606644 PMCID: PMC9833880 DOI: 10.3988/jcn.2023.19.1.36] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/20/2022] [Accepted: 07/24/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND AND PURPOSE This study aimed to determine the ability of deep learning using convolutional neural networks (CNNs) to diagnose transient global amnesia (TGA) based on electroencephalography (EEG) data, and to differentiate between patients with recurrent TGA events and those with a single TGA event. METHODS We retrospectively enrolled newly diagnosed patients with TGA and healthy controls. All patients with TGA and the healthy controls underwent EEG. The EEG signals were converted into images using time-frequency analysis with short-time Fourier transforms. We employed two CNN models (AlexNet and VGG19) to classify the patients with TGA and the healthy controls, and for further classification of patients with recurrent TGA events and those with a single TGA event. RESULTS We enrolled 171 patients with TGA and 68 healthy controls. The accuracy and area under the curve (AUC) of the AlexNet and VGG19 models in classifying patients with TGA and healthy controls were 70.4% and 71.8%, and 0.718 and 0.743, respectively. In addition, the accuracy and AUC of the AlexNet and VGG19 models in classifying patients with recurrent TGA events and those with a single TGA event were 71.1% and 88.4%, and 0.773 and 0.873, respectively. CONCLUSIONS We have successfully demonstrated the feasibility of deep learning in diagnosing TGA based on EEG data, and used two different CNN models to distinguish between patients with recurrent TGA events and those with a single TGA event.
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Affiliation(s)
- Young Deok Seo
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
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Ramasubramanian B, Reddy VS, Chellappan V, Ramakrishna S. Emerging Materials, Wearables, and Diagnostic Advancements in Therapeutic Treatment of Brain Diseases. BIOSENSORS 2022; 12:1176. [PMID: 36551143 PMCID: PMC9775999 DOI: 10.3390/bios12121176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/07/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
Among the most critical health issues, brain illnesses, such as neurodegenerative conditions and tumors, lower quality of life and have a significant economic impact. Implantable technology and nano-drug carriers have enormous promise for cerebral brain activity sensing and regulated therapeutic application in the treatment and detection of brain illnesses. Flexible materials are chosen for implantable devices because they help reduce biomechanical mismatch between the implanted device and brain tissue. Additionally, implanted biodegradable devices might lessen any autoimmune negative effects. The onerous subsequent operation for removing the implanted device is further lessened with biodegradability. This review expands on current developments in diagnostic technologies such as magnetic resonance imaging, computed tomography, mass spectroscopy, infrared spectroscopy, angiography, and electroencephalogram while providing an overview of prevalent brain diseases. As far as we are aware, there hasn't been a single review article that addresses all the prevalent brain illnesses. The reviewer also looks into the prospects for the future and offers suggestions for the direction of future developments in the treatment of brain diseases.
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Affiliation(s)
- Brindha Ramasubramanian
- Department of Mechanical Engineering, Center for Nanofibers & Nanotechnology, National University of Singapore, Singapore 117574, Singapore
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), #08-03, 2 Fusionopolis Way, Innovis, Singapore 138634, Singapore
| | - Vundrala Sumedha Reddy
- Department of Mechanical Engineering, Center for Nanofibers & Nanotechnology, National University of Singapore, Singapore 117574, Singapore
| | - Vijila Chellappan
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), #08-03, 2 Fusionopolis Way, Innovis, Singapore 138634, Singapore
| | - Seeram Ramakrishna
- Department of Mechanical Engineering, Center for Nanofibers & Nanotechnology, National University of Singapore, Singapore 117574, Singapore
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Bhattacharya S, Bennet L, Davidson JO, Unsworth CP. Multi-layer perceptron classification & quantification of neuronal survival in hypoxic-ischemic brain image slices using a novel gradient direction, grey level co-occurrence matrix image training. PLoS One 2022; 17:e0278874. [PMID: 36512546 PMCID: PMC9746996 DOI: 10.1371/journal.pone.0278874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 11/24/2022] [Indexed: 12/15/2022] Open
Abstract
Hypoxic ischemic encephalopathy (HIE) is a major global cause of neonatal death and lifelong disability. Large animal translational studies of hypoxic ischemic brain injury, such as those conducted in fetal sheep, have and continue to play a key role in furthering our understanding of the cellular and molecular mechanisms of injury and developing new treatment strategies for clinical translation. At present, the quantification of neurons in histological images consists of slow, manually intensive morphological assessment, requiring many repeats by an expert, which can prove to be time-consuming and prone to human error. Hence, there is an urgent need to automate the neuron classification and quantification process. In this article, we present a 'Gradient Direction, Grey level Co-occurrence Matrix' (GD-GLCM) image training method which outperforms and simplifies the standard training methodology using texture analysis to cell-classification. This is achieved by determining the Grey level Co-occurrence Matrix of the gradient direction of a cell image followed by direct passing to a classifier in the form of a Multilayer Perceptron (MLP). Hence, avoiding all texture feature computation steps. The proposed MLP is trained on both healthy and dying neurons that are manually identified by an expert and validated on unseen hypoxic-ischemic brain slice images from the fetal sheep in utero model. We compared the performance of our classifier using the gradient magnitude dataset as well as the gradient direction dataset. We also compare the performance of a perceptron, a 1-layer MLP, and a 2-layer MLP to each other. We demonstrate here a way of accurately identifying both healthy and dying cortical neurons obtained from brain slice images of the fetal sheep model under global hypoxia to high precision by identifying the most minimised MLP architecture, minimised input space (GLCM size) and minimised training data (GLCM representations) to achieve the highest performance over the standard methodology.
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Affiliation(s)
- Saheli Bhattacharya
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
- * E-mail:
| | - Laura Bennet
- Department of Physiology, The University of Auckland, Auckland, New Zealand
| | - Joanne O. Davidson
- Department of Physiology, The University of Auckland, Auckland, New Zealand
| | - Charles P. Unsworth
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
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Swanberg KM, Kurada AV, Prinsen H, Juchem C. Multiple sclerosis diagnosis and phenotype identification by multivariate classification of in vivo frontal cortex metabolite profiles. Sci Rep 2022; 12:13888. [PMID: 35974117 PMCID: PMC9381573 DOI: 10.1038/s41598-022-17741-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 07/29/2022] [Indexed: 12/04/2022] Open
Abstract
Multiple sclerosis (MS) is a heterogeneous autoimmune disease for which diagnosis continues to rely on subjective clinical judgment over a battery of tests. Proton magnetic resonance spectroscopy (1H MRS) enables the noninvasive in vivo detection of multiple small-molecule metabolites and is therefore in principle a promising means of gathering information sufficient for multiple sclerosis diagnosis and subtype classification. Here we show that supervised classification using 1H-MRS-visible normal-appearing frontal cortex small-molecule metabolites alone can indeed differentiate individuals with progressive MS from control (held-out validation sensitivity 79% and specificity 68%), as well as between relapsing and progressive MS phenotypes (held-out validation sensitivity 84% and specificity 74%). Post hoc assessment demonstrated the disproportionate contributions of glutamate and glutamine to identifying MS status and phenotype, respectively. Our finding establishes 1H MRS as a viable means of characterizing progressive multiple sclerosis disease status and paves the way for continued refinement of this method as an auxiliary or mainstay of multiple sclerosis diagnostics.
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Affiliation(s)
- Kelley M. Swanberg
- grid.25879.310000 0004 1936 8972Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, 351 Engineering Terrace, 1210 Amsterdam Avenue, Mail Code: 8904, New York, NY 10027 USA ,grid.47100.320000000419368710Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT USA
| | - Abhinav V. Kurada
- grid.25879.310000 0004 1936 8972Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, 351 Engineering Terrace, 1210 Amsterdam Avenue, Mail Code: 8904, New York, NY 10027 USA
| | - Hetty Prinsen
- grid.47100.320000000419368710Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT USA
| | - Christoph Juchem
- grid.25879.310000 0004 1936 8972Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, 351 Engineering Terrace, 1210 Amsterdam Avenue, Mail Code: 8904, New York, NY 10027 USA ,grid.47100.320000000419368710Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT USA ,grid.21729.3f0000000419368729Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY USA ,grid.47100.320000000419368710Department of Neurology, Yale University School of Medicine, New Haven, CT USA
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Erdoğan SB, Yükselen G. Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers. SENSORS (BASEL, SWITZERLAND) 2022; 22:5407. [PMID: 35891088 PMCID: PMC9322944 DOI: 10.3390/s22145407] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/20/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Diagnosis of most neuropsychiatric disorders relies on subjective measures, which makes the reliability of final clinical decisions questionable. The aim of this study was to propose a machine learning-based classification approach for objective diagnosis of three disorders of neuropsychiatric or neurological origin with functional near-infrared spectroscopy (fNIRS) derived biomarkers. Thirteen healthy adolescents and sixty-seven patients who were clinically diagnosed with migraine, obsessive compulsive disorder, or schizophrenia performed a Stroop task, while prefrontal cortex hemodynamics were monitored with fNIRS. Hemodynamic and cognitive features were extracted for training three supervised learning algorithms (naïve bayes (NB), linear discriminant analysis (LDA), and support vector machines (SVM)). The performance of each algorithm in correctly predicting the class of each participant across the four classes was tested with ten runs of a ten-fold cross-validation procedure. All algorithms achieved four-class classification performances with accuracies above 81% and specificities above 94%. SVM had the highest performance in terms of accuracy (85.1 ± 1.77%), sensitivity (84 ± 1.7%), specificity (95 ± 0.5%), precision (86 ± 1.6%), and F1-score (85 ± 1.7%). fNIRS-derived features have no subjective report bias when used for automated classification purposes. The presented methodology might have significant potential for assisting in the objective diagnosis of neuropsychiatric disorders associated with frontal lobe dysfunction.
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13
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Necesito IV, Velasco JMS, Jung J, Bae YH, Yoo Y, Kim S, Kim HS. Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices. Front Public Health 2022; 10:871354. [PMID: 35719622 PMCID: PMC9204014 DOI: 10.3389/fpubh.2022.871354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Most coronavirus disease 2019 (COVID-19) models use a combination of agent-based and equation-based models with only a few incorporating environmental factors in their prediction models. Many studies have shown that human and environmental factors play huge roles in disease transmission and spread, but few have combined the use of both factors, especially for SARS-CoV-2. In this study, both man-made policies (Stringency Index) and environment variables (Niño SST Index) were combined to predict the number of COVID-19 cases in South Korea. The performance indicators showed satisfactory results in modeling COVID-19 cases using the Non-linear Autoregressive Exogenous Model (NARX) as the modeling method, and Stringency Index (SI) and Niño Sea Surface Temperature (SST) as model variables. In this study, we showed that the accuracy of SARS-CoV-2 transmission forecasts may be further improved by incorporating both the Niño SST and SI variables and combining these variables with NARX may outperform other models. Future forecasting work by modelers should consider including climate or environmental variables (i.e., Niño SST) to enhance the prediction of transmission and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
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Affiliation(s)
- Imee V. Necesito
- Department of Civil Engineering, Inha University, Incheon, South Korea
- *Correspondence: Imee V. Necesito
| | - John Mark S. Velasco
- Department of Clinical Epidemiology, College of Medicine, University of the Philippines, Manila, Philippines
- Institute of Molecular Biology and Biotechnology, National Institutes of Health, University of the Philippines, Manila, Philippines
| | - Jaewon Jung
- Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology, Gyeonggi-do, South Korea
| | - Young Hye Bae
- Department of Civil Engineering, Inha University, Incheon, South Korea
| | - Younghoon Yoo
- Department of Civil Engineering, Inha University, Incheon, South Korea
| | - Soojun Kim
- Department of Civil Engineering, Inha University, Incheon, South Korea
| | - Hung Soo Kim
- Department of Civil Engineering, Inha University, Incheon, South Korea
- Hung Soo Kim
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14
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Algumaei AH, Algunaid RF, Rushdi MA, Yassine IA. Feature and decision-level fusion for schizophrenia detection based on resting-state fMRI data. PLoS One 2022; 17:e0265300. [PMID: 35609033 PMCID: PMC9129055 DOI: 10.1371/journal.pone.0265300] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Accepted: 02/28/2022] [Indexed: 12/01/2022] Open
Abstract
Mental disorders, especially schizophrenia, still pose a great challenge for diagnosis in early stages. Recently, computer-aided diagnosis techniques based on resting-state functional magnetic resonance imaging (Rs-fMRI) have been developed to tackle this challenge. In this work, we investigate different decision-level and feature-level fusion schemes for discriminating between schizophrenic and normal subjects. Four types of fMRI features are investigated, namely the regional homogeneity, voxel-mirrored homotopic connectivity, fractional amplitude of low-frequency fluctuations and amplitude of low-frequency fluctuations. Data denoising and preprocessing were first applied, followed by the feature extraction module. Four different feature selection algorithms were applied, and the best discriminative features were selected using the algorithm of feature selection via concave minimization (FSV). Support vector machine classifiers were trained and tested on the COBRE dataset formed of 70 schizophrenic subjects and 70 healthy subjects. The decision-level fusion method outperformed the single-feature-type approaches and achieved a 97.85% accuracy, a 98.33% sensitivity, a 96.83% specificity. Moreover, feature-fusion scheme resulted in a 98.57% accuracy, a 99.71% sensitivity, a 97.66% specificity, and an area under the ROC curve of 0.9984. In general, decision-level and feature-level fusion schemes boosted the performance of schizophrenia detectors based on fMRI features.
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Affiliation(s)
- Ali H. Algumaei
- Department of Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Giza, Egypt
| | - Rami F. Algunaid
- Department of Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Giza, Egypt
| | - Muhammad A. Rushdi
- Department of Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Giza, Egypt
| | - Inas A. Yassine
- Department of Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Giza, Egypt
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15
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Hong J, Huang Y, Ye J, Wang J, Xu X, Wu Y, Li Y, Zhao J, Li R, Kang J, Lai X. 3D FRN-ResNet: An Automated Major Depressive Disorder Structural Magnetic Resonance Imaging Data Identification Framework. Front Aging Neurosci 2022; 14:912283. [PMID: 35645776 PMCID: PMC9136074 DOI: 10.3389/fnagi.2022.912283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Major Depressive Disorder (MDD) is the most prevalent psychiatric disorder, seriously affecting people's quality of life. Manually identifying MDD from structural magnetic resonance imaging (sMRI) images is laborious and time-consuming due to the lack of clear physiological indicators. With the development of deep learning, many automated identification methods have been developed, but most of them stay in 2D images, resulting in poor performance. In addition, the heterogeneity of MDD also results in slightly different changes reflected in patients' brain imaging, which constitutes a barrier to the study of MDD identification based on brain sMRI images. We propose an automated MDD identification framework in sMRI data (3D FRN-ResNet) to comprehensively address these challenges, which uses 3D-ResNet to extract features and reconstruct them based on feature maps. Notably, the 3D FRN-ResNet fully exploits the interlayer structure information in 3D sMRI data and preserves most of the spatial details as well as the location information when converting the extracted features into vectors. Furthermore, our model solves the feature map reconstruction problem in closed form to produce a straightforward and efficient classifier and dramatically improves model performance. We evaluate our framework on a private brain sMRI dataset of MDD patients. Experimental results show that the proposed model exhibits promising performance and outperforms the typical other methods, achieving the accuracy, recall, precision, and F1 values of 0.86776, 0.84237, 0.85333, and 0.84781, respectively.
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Affiliation(s)
- Jialin Hong
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yueqi Huang
- Department of Psychiatry, Hangzhou Seventh People’s Hospital, Hangzhou, China
| | - Jianming Ye
- First Affiliated Hospital, Gannan Medical University, Ganzhou, China
| | - Jianqing Wang
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiaomei Xu
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yan Wu
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yi Li
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jialu Zhao
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Ruipeng Li
- Hangzhou Third People’s Hospital, Hangzhou, China
| | - Junlong Kang
- Zhongshan Hospital, Xiamen University, Xiamen, China
| | - Xiaobo Lai
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
- Department of Nephrology Surgery, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou, China
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16
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Varoquaux G, Cheplygina V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ Digit Med 2022; 5:48. [PMID: 35413988 PMCID: PMC9005663 DOI: 10.1038/s41746-022-00592-y] [Citation(s) in RCA: 122] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 03/09/2022] [Indexed: 12/23/2022] Open
Abstract
Research in computer analysis of medical images bears many promises to improve patients' health. However, a number of systematic challenges are slowing down the progress of the field, from limitations of the data, such as biases, to research incentives, such as optimizing for publication. In this paper we review roadblocks to developing and assessing methods. Building our analysis on evidence from the literature and data challenges, we show that at every step, potential biases can creep in. On a positive note, we also discuss on-going efforts to counteract these problems. Finally we provide recommendations on how to further address these problems in the future.
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Affiliation(s)
- Gaël Varoquaux
- INRIA, Versailles, France.
- McGill University, Montreal, Canada.
- Mila, Montreal, Canada.
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17
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Wada A, Saito Y, Fujita S, Irie R, Akashi T, Sano K, Kato S, Ikenouchi Y, Hagiwara A, Sato K, Tomizawa N, Hayakawa Y, Kikuta J, Kamagata K, Suzuki M, Hori M, Nakanishi A, Aoki S. Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning. Magn Reson Med Sci 2021; 22:57-66. [PMID: 34897147 PMCID: PMC9849414 DOI: 10.2463/mrms.mp.2021-0068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
PURPOSE Myelination-related MR signal changes in white matter are helpful for assessing normal development in infants and children. A rule-based myelination evaluation workflow regarding signal changes on T1-weighted images (T1WIs) and T2-weighted images (T2WIs) has been widely used in radiology. This study aimed to simulate a rule-based workflow using a stacked deep learning model and evaluate age estimation accuracy. METHODS The age estimation system involved two stacked neural networks: a target network-to extract five myelination-related images from the whole brain, and an age estimation network from extracted T1- and T2WIs separately. A dataset was constructed from 119 children aged below 2 years with two MRI systems. A four-fold cross-validation method was adopted. The correlation coefficient (CC), mean absolute error (MAE), and root mean squared error (RMSE) of the corrected chronological age of full-term birth, as well as the mean difference and the upper and lower limits of 95% agreement, were measured. Generalization performance was assessed using datasets acquired from different MR images. Age estimation was performed in Sturge-Weber syndrome (SWS) cases. RESULTS There was a strong correlation between estimated age and corrected chronological age (MAE: 0.98 months; RMSE: 1.27 months; and CC: 0.99). The mean difference and standard deviation (SD) were -0.15 and 1.26, respectively, and the upper and lower limits of 95% agreement were 2.33 and -2.63 months. Regarding generalization performance, the performance values on the external dataset were MAE of 1.85 months, RMSE of 2.59 months, and CC of 0.93. Among 13 SWS cases, 7 exceeded the limits of 95% agreement, and a proportional bias of age estimation based on myelination acceleration was exhibited below 12 months of age (P = 0.03). CONCLUSION Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.
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Affiliation(s)
- Akihiko Wada
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan,Corresponding author: Department of Radiology, Juntendo University School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo 113-8421, Japan. Phone: +81-3-5802-1230, Fax: +81-3-3816-0958, E-mail:
| | - Yuya Saito
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shohei Fujita
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Ryusuke Irie
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Toshiaki Akashi
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Katsuhiro Sano
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shinpei Kato
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Yutaka Ikenouchi
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Kanako Sato
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Nobuo Tomizawa
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Yayoi Hayakawa
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Junko Kikuta
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Michimasa Suzuki
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Masaaki Hori
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Atsushi Nakanishi
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
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18
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van Kempen EJ, Post M, Mannil M, Witkam RL, Ter Laan M, Patel A, Meijer FJA, Henssen D. Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis. Eur Radiol 2021; 31:9638-9653. [PMID: 34019128 PMCID: PMC8589805 DOI: 10.1007/s00330-021-08035-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 04/04/2021] [Accepted: 05/03/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVES Different machine learning algorithms (MLAs) for automated segmentation of gliomas have been reported in the literature. Automated segmentation of different tumor characteristics can be of added value for the diagnostic work-up and treatment planning. The purpose of this study was to provide an overview and meta-analysis of different MLA methods. METHODS A systematic literature review and meta-analysis was performed on the eligible studies describing the segmentation of gliomas. Meta-analysis of the performance was conducted on the reported dice similarity coefficient (DSC) score of both the aggregated results as two subgroups (i.e., high-grade and low-grade gliomas). This study was registered in PROSPERO prior to initiation (CRD42020191033). RESULTS After the literature search (n = 734), 42 studies were included in the systematic literature review. Ten studies were eligible for inclusion in the meta-analysis. Overall, the MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82-0.86). In addition, a DSC score of 0.83 (95% CI: 0.80-0.87) and 0.82 (95% CI: 0.78-0.87) was observed for the automated glioma segmentation of the high-grade and low-grade gliomas, respectively. However, heterogeneity was considerably high between included studies, and publication bias was observed. CONCLUSION MLAs facilitating automated segmentation of gliomas show good accuracy, which is promising for future implementation in neuroradiology. However, before actual implementation, a few hurdles are yet to be overcome. It is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set. KEY POINTS • MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82-0.86), indicating a good performance. • MLA performance was comparable when comparing the segmentation results of the high-grade gliomas and the low-grade gliomas. • For future studies using MLAs, it is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set.
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Affiliation(s)
- Evi J van Kempen
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 EZ, Nijmegen, The Netherlands
| | - Max Post
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 EZ, Nijmegen, The Netherlands
| | - Manoj Mannil
- Clinic of Radiology, University Hospital Münster, Münster, Germany
| | - Richard L Witkam
- Department of Anaesthesiology, Pain and Palliative Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Neurosurgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mark Ter Laan
- Department of Neurosurgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Ajay Patel
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 EZ, Nijmegen, The Netherlands
| | - Frederick J A Meijer
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 EZ, Nijmegen, The Netherlands
| | - Dylan Henssen
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 EZ, Nijmegen, The Netherlands.
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19
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Diagnosis of Schizophrenia Based on Deep Learning Using fMRI. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:8437260. [PMID: 34795793 PMCID: PMC8594998 DOI: 10.1155/2021/8437260] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 09/12/2021] [Accepted: 10/04/2021] [Indexed: 12/17/2022]
Abstract
Schizophrenia is a brain disease that frequently occurs in young people. Early diagnosis and treatment can reduce family burdens and reduce social costs. There is no objective evaluation index for schizophrenia. In order to improve the classification effect of traditional classification methods on magnetic resonance data, a method of classification of functional magnetic resonance imaging data is proposed in conjunction with the convolutional neural network algorithm. We take functional magnetic resonance imaging (fMRI) data for schizophrenia as an example, to extract effective time series from preprocessed fMRI data, and perform correlation analysis on regions of interest, using transfer learning and VGG16 net, and the functional connection between schizophrenia and healthy controls is classified. Experimental results show that the classification accuracy of fMRI based on VGG16 is up to 84.3%. On the one hand, it can improve the early diagnosis of schizophrenia, and on the other hand, it can solve the classification problem of small samples and high-dimensional data and effectively improve the generalization ability of deep learning models.
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20
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Kitamura FC, Pan I, Ferraciolli SF, Yeom KW, Abdala N. Clinical Artificial Intelligence Applications in Radiology: Neuro. Radiol Clin North Am 2021; 59:1003-1012. [PMID: 34689869 DOI: 10.1016/j.rcl.2021.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Radiologists have been at the forefront of the digitization process in medicine. Artificial intelligence (AI) is a promising area of innovation, particularly in medical imaging. The number of applications of AI in neuroradiology has also grown. This article illustrates some of these applications. This article reviews machine learning challenges related to neuroradiology. The first approval of reimbursement for an AI algorithm by the Centers for Medicare and Medicaid Services, covering a stroke software for early detection of large vessel occlusion, is also discussed.
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Affiliation(s)
- Felipe Campos Kitamura
- DasaInova, Diagnósticos da América SA (Dasa), São Paulo, São Paulo, Brazil; Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil.
| | - Ian Pan
- DasaInova, Diagnósticos da América SA (Dasa), São Paulo, São Paulo, Brazil; Brigham and Woman's Hospital, Boston, MA, USA
| | | | | | - Nitamar Abdala
- Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil
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21
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Explainable AI to improve acceptance of convolutional neural networks for automatic classification of dopamine transporter SPECT in the diagnosis of clinically uncertain parkinsonian syndromes. Eur J Nucl Med Mol Imaging 2021; 49:1176-1186. [PMID: 34651223 PMCID: PMC8921148 DOI: 10.1007/s00259-021-05569-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 09/17/2021] [Indexed: 12/31/2022]
Abstract
Purpose Deep convolutional neural networks (CNN) provide high accuracy for automatic classification of dopamine transporter (DAT) SPECT images. However, CNN are inherently black-box in nature lacking any kind of explanation for their decisions. This limits their acceptance for clinical use. This study tested layer-wise relevance propagation (LRP) to explain CNN-based classification of DAT-SPECT in patients with clinically uncertain parkinsonian syndromes. Methods The study retrospectively included 1296 clinical DAT-SPECT with visual binary interpretation as “normal” or “reduced” by two experienced readers as standard-of-truth. A custom-made CNN was trained with 1008 randomly selected DAT-SPECT. The remaining 288 DAT-SPECT were used to assess classification performance of the CNN and to test LRP for explanation of the CNN-based classification. Results Overall accuracy, sensitivity, and specificity of the CNN were 95.8%, 92.8%, and 98.7%, respectively. LRP provided relevance maps that were easy to interpret in each individual DAT-SPECT. In particular, the putamen in the hemisphere most affected by nigrostriatal degeneration was the most relevant brain region for CNN-based classification in all reduced DAT-SPECT. Some misclassified DAT-SPECT showed an “inconsistent” relevance map more typical for the true class label. Conclusion LRP is useful to provide explanation of CNN-based decisions in individual DAT-SPECT and, therefore, can be recommended to support CNN-based classification of DAT-SPECT in clinical routine. Total computation time of 3 s is compatible with busy clinical workflow. The utility of “inconsistent” relevance maps to identify misclassified cases requires further investigation. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05569-9.
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22
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Montaleão Brum Alves R, Ferreira da Silva M, Assis Schmitz É, Juarez Alencar A. Trends, Limits, and Challenges of Computer Technologies in Attention Deficit Hyperactivity Disorder Diagnosis and Treatment. CYBERPSYCHOLOGY BEHAVIOR AND SOCIAL NETWORKING 2021; 25:14-26. [PMID: 34569852 DOI: 10.1089/cyber.2020.0867] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Attention deficit hyperactivity disorder (ADHD) is a neurobiological condition that appears during an individual's childhood and may follow her/him for life. The research objective was to understand better how and which computer technologies have been applied to support ADHD diagnosis and treatment. The research used the systematic literature review method: a rigorous, verifiable, and repeatable approach that follows well-defined steps. Six well-known academic data sources have been consulted, including search engines and bibliographic databases, from technology and health care areas. After a rigorous research protocol, 1,239 articles were analyzed. For the diagnosis, the use of machine learning techniques was verified in 61 percent of the articles. Neurofeedback was ranked second with 9.3 percent participation, followed by serious games and eye tracking with 5.6 percent each. For the treatment, neurofeedback was present in 50 percent of the articles, whereas some studies combined both approaches, accounting for 31 percent of the total. Nine percent of the articles reported remote assistance technology, whereas another 9 percent have used virtual reality. By highlighting the leading computer technologies used, their applications, results, and challenges, this literature review breaks ground for further investigations. Moreover, the study highlighted the lack of consensus on ADHD biomarkers. The approaches using machine learning call attention to the probable occurrence of overfitting in several studies, thus demonstrating limitations of this technology on small-sized bases. This research also presented the convergence of evidence from different studies on the persistence of long-term effects of using neurofeedback in treating ADHD.
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23
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Zhou X, Lin Q, Gui Y, Wang Z, Liu M, Lu H. Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning. Front Neurosci 2021; 15:710133. [PMID: 34594183 PMCID: PMC8477011 DOI: 10.3389/fnins.2021.710133] [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: 05/15/2021] [Accepted: 07/30/2021] [Indexed: 11/13/2022] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is one of the most common brain diseases among children. The current criteria of ADHD diagnosis mainly depend on behavior analysis, which is subjective and inconsistent, especially for children. The development of neuroimaging technologies, such as magnetic resonance imaging (MRI), drives the discovery of brain abnormalities in structure and function by analyzing multimodal neuroimages for computer-aided diagnosis of brain diseases. This paper proposes a multimodal machine learning framework that combines the Boruta based feature selection and Multiple Kernel Learning (MKL) to integrate the multimodal features of structural and functional MRIs and Diffusion Tensor Images (DTI) for the diagnosis of early adolescent ADHD. The rich and complementary information of the macrostructural features, microstructural properties, and functional connectivities are integrated at the kernel level, followed by a support vector machine classifier for discriminating ADHD from healthy children. Our experiments were conducted on the comorbidity-free ADHD subjects and covariable-matched healthy children aged 9-10 chosen from the Adolescent Brain and Cognitive Development (ABCD) study. This paper is the first work to combine structural and functional MRIs with DTI for early adolescents of the ABCD study. The results indicate that the kernel-level fusion of multimodal features achieves 0.698 of AUC (area under the receiver operating characteristic curves) and 64.3% of classification accuracy for ADHD diagnosis, showing a significant improvement over the early feature fusion and unimodal features. The abnormal functional connectivity predictors, involving default mode network, attention network, auditory network, and sensorimotor mouth network, thalamus, and cerebellum, as well as the anatomical regions in basal ganglia, are found to encode the most discriminative information, which collaborates with macrostructure and diffusion alterations to boost the performances of disorder diagnosis.
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Affiliation(s)
- Xiaocheng Zhou
- Shanghai Jiao Tong University-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
- Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Qingmin Lin
- Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yuanyuan Gui
- Shanghai Jiao Tong University-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
- Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Zixin Wang
- Shanghai Jiao Tong University-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
- Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Manhua Liu
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China
- Department of Instrument Science and Engineering, School of EIEE, Shanghai Jiao Tong University, Shanghai, China
| | - Hui Lu
- Shanghai Jiao Tong University-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
- Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- Center for Biomedical Informatics, Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Shanghai Children's Hospital, Shanghai, China
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Peixoto-Santos JE, Blumcke I. Neuropathology of the 21st century for the Latin American epilepsy community. Seizure 2021; 90:51-59. [DOI: 10.1016/j.seizure.2021.02.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 01/18/2021] [Accepted: 02/01/2021] [Indexed: 12/13/2022] Open
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25
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van Kempen EJ, Post M, Mannil M, Kusters B, ter Laan M, Meijer FJA, Henssen DJHA. Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis. Cancers (Basel) 2021; 13:cancers13112606. [PMID: 34073309 PMCID: PMC8198025 DOI: 10.3390/cancers13112606] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Glioma prognosis and treatment are based on histopathological characteristics and molecular profile. Following the World Health Organization (WHO) guidelines (2016), the most important molecular diagnostic markers include IDH1/2-genotype and 1p/19q codeletion status, although more recent publications also include ARTX genotype and TERT- and MGMT promoter methylation. Machine learning algorithms (MLAs), however, were described to successfully determine these molecular characteristics non-invasively by using magnetic resonance imaging (MRI) data. The aim of this review and meta-analysis was to define the diagnostic accuracy of MLAs with regard to these different molecular markers. We found high accuracies of MLAs to predict each individual molecular marker, with IDH1/2-genotype being the most investigated and the most accurate. Radiogenomics could therefore be a promising tool for discriminating genetically determined gliomas in a non-invasive fashion. Although encouraging results are presented here, large-scale, prospective trials with external validation groups are warranted. Abstract Treatment planning and prognosis in glioma treatment are based on the classification into low- and high-grade oligodendroglioma or astrocytoma, which is mainly based on molecular characteristics (IDH1/2- and 1p/19q codeletion status). It would be of great value if this classification could be made reliably before surgery, without biopsy. Machine learning algorithms (MLAs) could play a role in achieving this by enabling glioma characterization on magnetic resonance imaging (MRI) data without invasive tissue sampling. The aim of this study is to provide a performance evaluation and meta-analysis of various MLAs for glioma characterization. Systematic literature search and meta-analysis were performed on the aggregated data, after which subgroup analyses for several target conditions were conducted. This study is registered with PROSPERO, CRD42020191033. We identified 724 studies; 60 and 17 studies were eligible to be included in the systematic review and meta-analysis, respectively. Meta-analysis showed excellent accuracy for all subgroups, with the classification of 1p/19q codeletion status scoring significantly poorer than other subgroups (AUC: 0.748, p = 0.132). There was considerable heterogeneity among some of the included studies. Although promising results were found with regard to the ability of MLA-tools to be used for the non-invasive classification of gliomas, large-scale, prospective trials with external validation are warranted in the future.
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Affiliation(s)
- Evi J. van Kempen
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Max Post
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Manoj Mannil
- Clinic of Radiology, University Hospital Münster, WWU University of Münster, 48149 Münster, Germany;
| | - Benno Kusters
- Department of Pathology, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands;
| | - Mark ter Laan
- Department of Neurosurgery, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands;
| | - Frederick J. A. Meijer
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Dylan J. H. A. Henssen
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
- Correspondence:
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Ganau M, Magdum SA, Calisto A. Pre-operative imaging and post-operative appearance of standard paediatric neurosurgical approaches: a training guide for neuroradiologists. Transl Pediatr 2021; 10:1231-1243. [PMID: 34012863 PMCID: PMC8107881 DOI: 10.21037/tp-20-484] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
A short-cut narrative review was conducted according to the SANRA guidelines to identify studies describing normal and abnormal postoperative radiological features of the most common paediatric neurosurgical procedures. Rather than focusing on the original pathology addressed by neurosurgical means, this review explored three main areas of operative neurosurgery: ventricular access, supratentorial & infratentorial craniotomies, and posterior fossa/craniocervical junction decompression. A total of twenty-three landmark papers were included for review based on their relevance to address the research question and serve as a practical guide for paediatric neuroradiology trainees and fellows. Accurate in text referencing of the ClinicalTrials.gov identifier, and weblink, has also been provided for all trials discussed in the results section. All the above is complemented by relevant iconography meant to describe a wide range of postoperative changes and early complications. Finally, the review is enriched by a discussion touching upon haemostatic agents, intentionally retained foreign bodies and the future of machine learning for neuroradiology reporting. Overall, the information presented in a systematic fashion will not only help trainees and fellows to deepen these topics and expand their knowledge in preparation for written and oral boards, but will also represent a useful resource for everyone including trained neuroradiologists and neurosurgeons themselves.
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Affiliation(s)
- Mario Ganau
- Department of Neurosurgery, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Shailendra A Magdum
- Department of Neurosurgery, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Amedeo Calisto
- Department of Neurosurgery, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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27
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Wang P, Zhao X, Zhong J, Zhou Y. Localization and Diagnosis of Attention-Deficit/Hyperactivity Disorder. Healthcare (Basel) 2021; 9:372. [PMID: 33801750 PMCID: PMC8066369 DOI: 10.3390/healthcare9040372] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Revised: 03/21/2021] [Accepted: 03/23/2021] [Indexed: 11/17/2022] Open
Abstract
In this paper, a random-forest-based method was proposed for the classification and localization of Attention-Deficit/Hyperactivity Disorder (ADHD), a common neurodevelopmental disorder among children. Experimental data were magnetic resonance imaging (MRI) from the public case-control dataset of 3D images for ADHD-200. Each MRI image was a 3D-tensor of 121×145×121 size. All 3D matrices (MRI) were segmented into the slices from each of three orthogonal directions. Each slice from the same position of the same direction in the training set was converted into a vector, and all these vectors were composed into a designed matrix to train the random forest classification algorithm; then, the well-trained RF classifier was exploited to give a prediction label in correspondence direction and position. Diagnosis and location results can be obtained upon the intersection of these three prediction matrices. The performance of our proposed method was illustrated on the dataset from New York University (NYU), Kennedy Krieger Institute (KKI) and full datasets; the results show that the proposed methods can archive more accuracy identification in discrimination of ADHD, and can be extended to the other practices of diagnosis. Moreover, another suspected region was found at the first time.
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Affiliation(s)
- Peng Wang
- School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China; (P.W.); (J.Z.); (Y.Z.)
- School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xuejing Zhao
- School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China; (P.W.); (J.Z.); (Y.Z.)
| | - Jitao Zhong
- School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China; (P.W.); (J.Z.); (Y.Z.)
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Ying Zhou
- School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China; (P.W.); (J.Z.); (Y.Z.)
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28
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Seccia R, Romano S, Salvetti M, Crisanti A, Palagi L, Grassi F. Machine Learning Use for Prognostic Purposes in Multiple Sclerosis. Life (Basel) 2021; 11:life11020122. [PMID: 33562572 PMCID: PMC7914671 DOI: 10.3390/life11020122] [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: 01/11/2021] [Revised: 01/29/2021] [Accepted: 01/30/2021] [Indexed: 12/28/2022] Open
Abstract
The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.
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Affiliation(s)
- Ruggiero Seccia
- Department of Computer, Control and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, 00185 Rome, Italy; (R.S.); (L.P.)
| | - Silvia Romano
- Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, 00189 Rome, Italy; (S.R.); (M.S.)
| | - Marco Salvetti
- Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, 00189 Rome, Italy; (S.R.); (M.S.)
- Mediterranean Neurological Institute Neuromed, 86077 Pozzilli, Italy
| | - Andrea Crisanti
- Department of Physics, Sapienza University of Rome, 00185 Rome, Italy;
| | - Laura Palagi
- Department of Computer, Control and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, 00185 Rome, Italy; (R.S.); (L.P.)
| | - Francesca Grassi
- Department of Physiology and Pharmacology, Sapienza University of Rome, 00185 Rome, Italy
- Correspondence:
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Vázquez-Marrufo M, Sarrias-Arrabal E, García-Torres M, Martín-Clemente R, Izquierdo G. A systematic review of the application of machine-learning algorithms in multiple sclerosis. Neurologia 2021; 38:S0213-4853(20)30431-X. [PMID: 33549371 DOI: 10.1016/j.nrl.2020.10.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 08/20/2020] [Accepted: 10/11/2020] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years. OBJECTIVE We present a systematic review of the application of ML algorithms in MS. MATERIALS AND METHODS We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords "machine learning" and "multiple sclerosis." We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected. CONCLUSIONS After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.
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Affiliation(s)
- M Vázquez-Marrufo
- Departamento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Sevilla, España.
| | - E Sarrias-Arrabal
- Departamento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Sevilla, España
| | - M García-Torres
- Escuela Politécnica Superior, Universidad Pablo de Olavide, Sevilla, España
| | - R Martín-Clemente
- Departamento de Teoría de la Señal y Comunicaciones, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Sevilla, España
| | - G Izquierdo
- Unidad de Esclerosis Múltiple, Hospital VITHAS, Sevilla, España
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Radabaugh H, Bonnell J, Schwartz O, Sarkar D, Dietrich WD, Bramlett HM. Use of Machine Learning to Re-Assess Patterns of Multivariate Functional Recovery after Fluid Percussion Injury: Operation Brain Trauma Therapy. J Neurotrauma 2021; 38:1670-1678. [PMID: 33107380 DOI: 10.1089/neu.2020.7357] [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: 11/12/2022] Open
Abstract
Traumatic brain injury (TBI) is a leading cause of death and disability. Yet, despite immense research efforts, treatment options remain elusive. Translational failures in TBI are often attributed to the heterogeneity of the TBI population and limited methods to capture these individual variabilities. Advances in machine learning (ML) have the potential to further personalized treatment strategies and better inform translational research. However, the use of ML has yet to be widely assessed in pre-clinical neurotrauma research, where data are strictly limited in subject number. To better establish ML's feasibility, we utilized the fluid percussion injury (FPI) portion of the rich, rat data set collected by Operation Brain Trauma Therapy (OBTT), which tested multiple pharmacological treatments. Previous work has provided confidence that both unsupervised and supervised ML techniques can uncover useful insights from this OBTT pre-clinical research data set. As a proof-of-concept, we aimed to better evaluate the multi-variate recovery profiles afforded by the administration of nine different experimental therapies. We assessed supervised pairwise classifiers trained on a pre-processed data set that incorporated metrics from four feature groups to determine their ability to correctly identify specific drug treatments. In all but one of the possible pairwise combinations of minocycline, levetiracetam, erythropoietin, nicotinamide, and amantadine, the baseline was outperformed by one or more supervised classifiers, the exception being nicotinamide versus amantadine. Further, when the same methods were employed to assess different doses of the same treatment, the ML classifiers had greater difficulty in understanding which treatment each sample received. Our data serve as a critical first step toward identifying optimal treatments for specific subgroups of samples that are dependent on factors such as types and severity of traumatic injuries, as well as informing the prediction of therapeutic combinations that may lead to greater treatment effects than individual therapies.
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Affiliation(s)
- Hannah Radabaugh
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Jerry Bonnell
- Department of Computer Science, University of Miami College of Arts and Sciences, Miami, Florida, USA
| | - Odelia Schwartz
- Department of Computer Science, University of Miami College of Arts and Sciences, Miami, Florida, USA
| | - Dilip Sarkar
- Department of Computer Science, University of Miami College of Arts and Sciences, Miami, Florida, USA
| | - W Dalton Dietrich
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Helen M Bramlett
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA.,Bruce W. Carter Department of Veterans Affairs Medical Center, Miami, Florida, USA
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Taoka T, Naganawa S. Imaging for central nervous system (CNS) interstitial fluidopathy: disorders with impaired interstitial fluid dynamics. Jpn J Radiol 2021; 39:1-14. [PMID: 32653987 PMCID: PMC7813706 DOI: 10.1007/s11604-020-01017-0] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 07/02/2020] [Indexed: 02/07/2023]
Abstract
After the introduction of the glymphatic system hypothesis, an increasing number of studies on cerebrospinal fluid and interstitial fluid dynamics within the brain have been investigated and reported. A series of diseases are known which develop due to abnormality of the glymphatic system including Alzheimer's disease, traumatic brain injury, stroke, or other disorders. These diseases or disorders share the characteristics of the glymphatic system dysfunction or other mechanisms related to the interstitial fluid dynamics. In this review article, we propose "Central Nervous System (CNS) Interstitial Fluidopathy" as a new concept encompassing diseases whose pathologies are majorly associated with abnormal interstitial fluid dynamics. Categorizing these diseases or disorders as "CNS interstitial fluidopathies," will promote the understanding of their mechanisms and the development of potential imaging methods for the evaluation of the disease as well as clinical methods for disease treatment or prevention. In other words, having a viewpoint of the dynamics of interstitial fluid appears relevant for understanding CNS diseases or disorders, and it would be possible to develop novel common treatment methods or medications for "CNS interstitial fluidopathies."
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Affiliation(s)
- Toshiaki Taoka
- Department of Innovative Biomedical Visualization (iBMV), Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan. .,Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Japan.
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
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Yang Y, Wei L, Hu Y, Wu Y, Hu L, Nie S. Classification of Parkinson's disease based on multi-modal features and stacking ensemble learning. J Neurosci Methods 2020; 350:109019. [PMID: 33321153 DOI: 10.1016/j.jneumeth.2020.109019] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 11/27/2020] [Accepted: 11/28/2020] [Indexed: 11/18/2022]
Abstract
BACKGROUND Early diagnosis of Parkinson's disease (PD) enables timely treatment of patients and helps control the course of the disease. An efficient and reliable approach is therefore needed to develop for improving the clinical ability to diagnose this disease. NEW METHOD We proposed a two-layer stacking ensemble learning framework with fusing multi-modal features in this study, for accurately identifying early PD with healthy control (HC). To begin with, we investigated relative importance of multi-modal neuroimaging (T1 weighted image (T1WI), diffusion tensor imaging (DTI)) and early clinical assessment to classify PD and HC. Next, a two-layer stacking ensemble framework was proposed: at the first layer, we evaluated advantages of these four base classifiers: support vector machine (SVM), random forests (RF), K-nearest neighbor (KNN) and artificial neural network (ANN); at the second layer, a logistic regression (LR) classifier was applied to classify PD. The performance of the proposed model was evaluated by comparing with traditional ensemble models. RESULTS The proposed method performed an accuracy of 96.88 %, a precision of 100 %, a recall of 95 % and a F1 score of 97.44 % respectively for identifying PD and HC. COMPARISON WITH EXISTING METHOD The classification results showed that the proposed model achieved a superior performance in comparison with traditional ensemble models. CONCLUSION The stacking ensemble model with efficiently and effectively integrate multiple base classifiers performed higher accuracy than each single traditional model. The method developed in this study provided a novel strategy to enhance the accuracy of diagnosis and early detection of PD.
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Affiliation(s)
- Yifeng Yang
- School of Medical Instrument & Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Long Wei
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, Shandong, 250101, China
| | - Ying Hu
- School of Medical Instrument & Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Yan Wu
- School of Medical Instrument & Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Liangyun Hu
- Center for Functional Neurosurgery, RuiJin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Shengdong Nie
- School of Medical Instrument & Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
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Guan Y, Cheng CH, Chen W, Zhang Y, Koo S, Krengel M, Janulewicz P, Toomey R, Yang E, Bhadelia R, Steele L, Kim JH, Sullivan K, Koo BB. Neuroimaging Markers for Studying Gulf-War Illness: Single-Subject Level Analytical Method Based on Machine Learning. Brain Sci 2020; 10:brainsci10110884. [PMID: 33233672 PMCID: PMC7699718 DOI: 10.3390/brainsci10110884] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 11/11/2020] [Accepted: 11/17/2020] [Indexed: 12/17/2022] Open
Abstract
Gulf War illness (GWI) refers to the multitude of chronic health symptoms, spanning from fatigue, musculoskeletal pain, and neurological complaints to respiratory, gastrointestinal, and dermatologic symptoms experienced by about 250,000 GW veterans who served in the 1991 Gulf War (GW). Longitudinal studies showed that the severity of these symptoms often remain unchanged even years after the GW, and these veterans with GWI continue to have poorer general health and increased chronic medical conditions than their non-deployed counterparts. For better management and treatment of this condition, there is an urgent need for developing objective biomarkers that can help with simple and accurate diagnosis of GWI. In this study, we applied multiple neuroimaging techniques, including T1-weighted magnetic resonance imaging (T1W-MRI), diffusion tensor imaging (DTI), and novel neurite density imaging (NDI) to perform both a group-level statistical comparison and a single-subject level machine learning (ML) analysis to identify diagnostic imaging features of GWI. Our results supported NDI as the most sensitive in defining GWI characteristics. In particular, our classifier trained with white matter NDI features achieved an accuracy of 90% and F-score of 0.941 for classifying GWI cases from controls after the cross-validation. These results are consistent with our previous study which suggests that NDI measures are sensitive to the microstructural and macrostructural changes in the brain of veterans with GWI, which can be valuable for designing better diagnosis method and treatment efficacy studies.
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Affiliation(s)
- Yi Guan
- School of Medicine, Boston University, Boston, MA 02118, USA; (Y.G.); (C.-H.C.); (W.C.); (Y.Z.); (S.K.); (M.K.); (R.T.)
| | - Chia-Hsin Cheng
- School of Medicine, Boston University, Boston, MA 02118, USA; (Y.G.); (C.-H.C.); (W.C.); (Y.Z.); (S.K.); (M.K.); (R.T.)
| | - Weifan Chen
- School of Medicine, Boston University, Boston, MA 02118, USA; (Y.G.); (C.-H.C.); (W.C.); (Y.Z.); (S.K.); (M.K.); (R.T.)
| | - Yingqi Zhang
- School of Medicine, Boston University, Boston, MA 02118, USA; (Y.G.); (C.-H.C.); (W.C.); (Y.Z.); (S.K.); (M.K.); (R.T.)
| | - Sophia Koo
- School of Medicine, Boston University, Boston, MA 02118, USA; (Y.G.); (C.-H.C.); (W.C.); (Y.Z.); (S.K.); (M.K.); (R.T.)
| | - Maxine Krengel
- School of Medicine, Boston University, Boston, MA 02118, USA; (Y.G.); (C.-H.C.); (W.C.); (Y.Z.); (S.K.); (M.K.); (R.T.)
| | | | - Rosemary Toomey
- School of Medicine, Boston University, Boston, MA 02118, USA; (Y.G.); (C.-H.C.); (W.C.); (Y.Z.); (S.K.); (M.K.); (R.T.)
| | - Ehwa Yang
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; (E.Y.); (J.-H.K.)
| | - Rafeeque Bhadelia
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, USA;
| | - Lea Steele
- Neuropsychiatry Division, Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX 77030, USA;
| | - Jae-Hun Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; (E.Y.); (J.-H.K.)
| | - Kimberly Sullivan
- School of Public Health, Boston University, Boston, MA 02118, USA;
- Correspondence: (K.S.); (B.-B.K.)
| | - Bang-Bon Koo
- School of Medicine, Boston University, Boston, MA 02118, USA; (Y.G.); (C.-H.C.); (W.C.); (Y.Z.); (S.K.); (M.K.); (R.T.)
- Correspondence: (K.S.); (B.-B.K.)
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Zhu G, Jiang B, Chen H, Tong E, Xie Y, Faizy TD, Heit JJ, Zaharchuk G, Wintermark M. Artificial Intelligence and Stroke Imaging. Neuroimaging Clin N Am 2020; 30:479-492. [DOI: 10.1016/j.nic.2020.07.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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35
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Park CH, Lee PH, Lee SK, Chung SJ, Shin NY. The diagnostic potential of multimodal neuroimaging measures in Parkinson's disease and atypical parkinsonism. Brain Behav 2020; 10:e01808. [PMID: 33029883 PMCID: PMC7667347 DOI: 10.1002/brb3.1808] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 08/03/2020] [Accepted: 08/04/2020] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION For the diagnosis of Parkinson's disease (PD) and atypical parkinsonism (AP) using neuroimaging, structural measures have been largely employed since structural abnormalities are most noticeable in the diseases. Functional abnormalities have been known as well, though less clearly seen, and thus, the addition of functional measures to structural measures is expected to be more informative for the diagnosis. Here, we aimed to assess whether multimodal neuroimaging measures of structural and functional alterations could have potential for enhancing performance in diverse diagnostic classification problems. METHODS For 77 patients with PD, 86 patients with AP comprising multiple system atrophy and progressive supranuclear palsy, and 53 healthy controls (HC), structural and functional MRI data were collected. Gray matter (GM) volume was acquired as a structural measure, and GM regional homogeneity and degree centrality were acquired as functional measures. The measures were used as predictors individually or in combination in support vector machine classifiers for different problems of distinguishing between HC and each diagnostic type and between different diagnostic types. RESULTS In statistical comparisons of the measures, structural alterations were extensively seen in all diagnostic types, whereas functional alterations were limited to specific diagnostic types. The addition of functional measures to the structural measure generally yielded statistically significant improvements to classification accuracy, compared to the use of the structural measure alone. CONCLUSION We suggest the fusion of multimodal neuroimaging measures as an effective strategy that could generally cope with diverse prediction problems of clinical concerns.
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Affiliation(s)
- Chang-Hyun Park
- Department of Radiology, College of Medicine, The Catholic University of Korea, Seoul, Korea.,Center for Neuroprosthetics and Brain Mind Institute, Swiss Federal Institute of Technology (EPFL), Geneva, Switzerland
| | - Phil Hyu Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology, Yonsei University College of Medicine, Seoul, Korea
| | - Seok Jong Chung
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea.,Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, Korea
| | - Na-Young Shin
- Department of Radiology, College of Medicine, The Catholic University of Korea, Seoul, Korea
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Nakaura T, Higaki T, Awai K, Ikeda O, Yamashita Y. A primer for understanding radiology articles about machine learning and deep learning. Diagn Interv Imaging 2020; 101:765-770. [PMID: 33121910 DOI: 10.1016/j.diii.2020.10.001] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 10/06/2020] [Accepted: 10/06/2020] [Indexed: 12/12/2022]
Abstract
The application of machine learning and deep learning in the field of imaging is rapidly growing. Although the principles of machine and deep learning are unfamiliar to the majority of clinicians, the basics are not so complicated. One of the major issues is that commentaries written by experts are difficult to understand, and are not primarily written for clinicians. The purpose of this article was to describe the different concepts behind machine learning, radiomics, and deep learning to make clinicians more familiar with these techniques.
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Affiliation(s)
- Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate school of medical sciences, Japan Kumamoto university, 1-1-1 Honjo, Chuo-ku, 860-8556 Kumamoto City, Japan.
| | - Toru Higaki
- Departments of Diagnostic Radiology and Radiology, Hiroshima university, 1-2-3 Kasumi, Minami-ku, 734-8551 Hiroshima City, Japan
| | - Kazuo Awai
- Departments of Diagnostic Radiology and Radiology, Hiroshima university, 1-2-3 Kasumi, Minami-ku, 734-8551 Hiroshima City, Japan
| | - Osamu Ikeda
- Department of Diagnostic Radiology, Graduate school of medical sciences, Japan Kumamoto university, 1-1-1 Honjo, Chuo-ku, 860-8556 Kumamoto City, Japan
| | - Yasuyuki Yamashita
- Department of Diagnostic Radiology, Graduate school of medical sciences, Japan Kumamoto university, 1-1-1 Honjo, Chuo-ku, 860-8556 Kumamoto City, Japan
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Radabaugh HL, Bonnell J, Dietrich WD, Bramlett HM, Schwartz O, Sarkar D. Development and Evaluation of Machine Learning Models for Recovery Prediction after Treatment for Traumatic Brain Injury. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2416-2420. [PMID: 33018494 DOI: 10.1109/embc44109.2020.9175658] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Traumatic brain injury (TBI) is a leading cause of death and disability yet treatment strategies remain elusive. Advances in machine learning present exciting opportunities for developing personalized medicine and informing laboratory research. However, their feasibility has yet to be widely assessed in animal research where data are typically limited or in the TBI field where each patient presents with a unique injury. The Operation Brain Trauma Therapy (OBTT) has amassed an animal dataset that spans multiple types of injury, treatment strategies, behavioral assessments, histological measures, and biomarker screenings. This paper aims to analyze these data using supervised learning techniques for the first time by partitioning the dataset into acute input metrics (i.e. 7 days post-injury) and a defined recovery outcome (i.e. memory retention). Preprocessing is then applied to transform the raw OBTT dataset, e.g. developing a class attribute by histogram binning, eliminating borderline cases, and applying principal component analysis (PCA). We find that these steps are also useful in establishing a treatment ranking; Minocycline, a therapy with no significant findings in the OBTT analyses, yields the highest percentage recovery in our ranking. Furthermore, of the seven classifiers we have evaluated, Naïve Bayes achieves the best performance (67%) and yields significant improvement over our baseline model on the preprocessed dataset with borderline elimination. We also investigate the effect of testing on individual treatment groups to evaluate which groups are difficult to classify, and note the interpretive qualities of our model that can be clinically relevant.Clinical Relevance- These studies establish methods for better analyzing multivariate functional recovery and understanding which measures affect prognosis following traumatic brain injury.
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Segato A, Marzullo A, Calimeri F, De Momi E. Artificial intelligence for brain diseases: A systematic review. APL Bioeng 2020; 4:041503. [PMID: 33094213 PMCID: PMC7556883 DOI: 10.1063/5.0011697] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 09/09/2020] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.
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Affiliation(s)
- Alice Segato
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
| | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
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Olthof AW, van Ooijen PMA, Rezazade Mehrizi MH. Promises of artificial intelligence in neuroradiology: a systematic technographic review. Neuroradiology 2020; 62:1265-1278. [PMID: 32318774 PMCID: PMC7479016 DOI: 10.1007/s00234-020-02424-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 03/27/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE To conduct a systematic review of the possibilities of artificial intelligence (AI) in neuroradiology by performing an objective, systematic assessment of available applications. To analyse the potential impacts of AI applications on the work of neuroradiologists. METHODS We identified AI applications offered on the market during the period 2017-2019. We systematically collected and structured information in a relational database and coded for the characteristics of the applications, their functionalities for the radiology workflow and their potential impacts in terms of 'supporting', 'extending' and 'replacing' radiology tasks. RESULTS We identified 37 AI applications in the domain of neuroradiology from 27 vendors, together offering 111 functionalities. The majority of functionalities 'support' radiologists, especially for the detection and interpretation of image findings. The second-largest group of functionalities 'extends' the possibilities of radiologists by providing quantitative information about pathological findings. A small but noticeable portion of functionalities seek to 'replace' certain radiology tasks. CONCLUSION Artificial intelligence in neuroradiology is not only in the stage of development and testing but also available for clinical practice. The majority of functionalities support radiologists or extend their tasks. None of the applications can replace the entire radiology profession, but a few applications can do so for a limited set of tasks. Scientific validation of the AI products is more limited than the regulatory approval.
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Affiliation(s)
- Allard W Olthof
- Department of Radiology, Treant Health Care Group, Dr. G.H. Amshoffweg 1, Hoogeveen, The Netherlands.
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, The Netherlands.
| | - Peter M A van Ooijen
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, The Netherlands
- Data Science Center in Health (DASH), Machine Learning Lab, University of Groningen, University Medical Center Groningen, Zielstraweg 2, Groningen, The Netherlands
| | - Mohammad H Rezazade Mehrizi
- School of Business and Economics, Knowledge, Information and Innovation, KIN Center for Digital Innovation, Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam, The Netherlands
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Alsenan S, Al-Turaiki I, Hafez A. A Recurrent Neural Network model to predict blood-brain barrier permeability. Comput Biol Chem 2020; 89:107377. [PMID: 33010784 DOI: 10.1016/j.compbiolchem.2020.107377] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 09/09/2020] [Accepted: 09/12/2020] [Indexed: 12/14/2022]
Abstract
The rapid development of computational methods and the increasing volume of chemical and biological data have contributed to an immense growth in chemical research. This field of study is known as "chemoinformatics," which is a discipline that uses machine-learning techniques to extract, process, and extrapolate data from chemical structures. One of the significant lines of research in chemoinformatics is the study of blood-brain barrier (BBB) permeability, which aims to identify drug penetration into the central nervous system (CNS). In this research, we attempt to solve the problem of BBB permeability by predicting compounds penetration to the CNS. To accomplish this goal: (i) First, an overview is provided to the field of chemoinformatics, its definition, applications, and challenges, (ii) Second, a broad view is taken to investigate previous machine-learning and deep-learning computational models to solve BBB permeability. Based on the analysis of previous models, three main challenges that collectively affect the classifier performance are identified, which we define as "the triple constraints"; subsequently, we map each constraint to a proposed solution, (iii) Finally, we conclude this endeavor by proposing a deep learning based Recurrent Neural Network model, to predict BBB permeability (RNN-BBB model). Our model outperformed other studies from the literature by scoring an overall accuracy of 96.53%, and a specificity score of 98.08%. The obtained results confirm that addressing the triple constraints substantially improves the classification model capability specifically when predicting compounds with low penetration.
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Affiliation(s)
- Shrooq Alsenan
- Research Center, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia; Research Chair in Healthcare Innovation, Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
| | - Isra Al-Turaiki
- College of Computer and Information Sciences, Information Technology Department, King Saud University, Riyadh, Saudi Arabia.
| | - Alaaeldin Hafez
- College of Computer and Information Sciences, Information Systems Department, King Saud University, Riyadh, Saudi Arabia.
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Slobodin O, Yahav I, Berger I. A Machine-Based Prediction Model of ADHD Using CPT Data. Front Hum Neurosci 2020; 14:560021. [PMID: 33093829 PMCID: PMC7528635 DOI: 10.3389/fnhum.2020.560021] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 08/24/2020] [Indexed: 11/17/2022] Open
Abstract
Despite the popularity of the continuous performance test (CPT) in the diagnosis of attention-deficit/hyperactivity disorder (ADHD), its specificity, sensitivity, and ecological validity are still debated. To address some of the known shortcomings of traditional analysis and interpretation of CPT data, the present study applied a machine learning-based model (ML) using CPT indices for the Prediction of ADHD.Using a retrospective factorial fitting, followed by a bootstrap technique, we trained, cross-validated, and tested learning models on CPT performance data of 458 children aged 6–12 years (213 children with ADHD and 245 typically developed children). We used the MOXO-CPT version that included visual and auditory stimuli distractors. Results showed that the ML proposed model performed better and had a higher accuracy than the benchmark approach that used clinical data only. Using the CPT total score (that included all four indices: Attention, Timeliness, Hyperactivity, and Impulsiveness), as well as four control variables [age, gender, day of the week (DoW), time of day (ToD)], provided the most salient information for discriminating children with ADHD from their typically developed peers. This model had an accuracy rate of 87%, a sensitivity rate of 89%, and a specificity rate of 84%. This performance was 34% higher than the best-achieved accuracy of the benchmark model. The ML detection model could classify children with ADHD with high accuracy based on CPT performance. ML model of ADHD holds the promise of enhancing, perhaps complementing, behavioral assessment and may be used as a supportive measure in the evaluation of ADHD.
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Affiliation(s)
- Ortal Slobodin
- Department of Education, Ben-Gurion University, Beer-Sheva, Israel
- *Correspondence: Ortal Slobodin
| | - Inbal Yahav
- Coller School of Management, Tel Aviv University, Tel Aviv, Israel
| | - Itai Berger
- Pediatric Neurology, Assuta Ashdod University Hospital, Ashdod, Israel
- Faculty of Health Sciences, Ben-Gurion University, Beer-Sheva, Israel
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Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity. Neural Plast 2020; 2020:8871712. [PMID: 32908491 PMCID: PMC7463415 DOI: 10.1155/2020/8871712] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 08/02/2020] [Accepted: 08/08/2020] [Indexed: 12/13/2022] Open
Abstract
The effects of acupuncture facilitating neural plasticity for treating diseases have been identified by clinical and experimental studies. In the last two decades, the application of neuroimaging techniques in acupuncture research provided visualized evidence for acupuncture promoting neuroplasticity. Recently, the integration of machine learning (ML) and neuroimaging techniques becomes a focus in neuroscience and brings a new and promising approach to understand the facilitation of acupuncture on neuroplasticity at the individual level. This review is aimed at providing an overview of this rapidly growing field by introducing the commonly used ML algorithms in neuroimaging studies briefly and analyzing the characteristics of the acupuncture studies based on ML and neuroimaging, so as to provide references for future research.
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Liu W, Zhang C, Wang X, Xu J, Chang Y, Ristaniemi T, Cong F. Functional connectivity of major depression disorder using ongoing EEG during music perception. Clin Neurophysiol 2020; 131:2413-2422. [PMID: 32828045 DOI: 10.1016/j.clinph.2020.06.031] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 05/07/2020] [Accepted: 06/29/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVE The functional connectivity (FC) of major depression disorder (MDD) has not been well studied under naturalistic and continuous stimuli conditions. In this study, we investigated the frequency-specific FC of MDD patients exposed to conditions of music perception using ongoing electroencephalogram (EEG). METHODS First, we applied the phase lag index (PLI) method to calculate the connectivity matrices and graph theory-based methods to measure the topology of brain networks across different frequency bands. Then, classification methods were adopted to identify the most discriminate frequency band for the diagnosis of MDD. RESULTS During music perception, MDD patients exhibited a decreased connectivity pattern in the delta band but an increased connectivity pattern in the beta band. Healthy people showed a left hemisphere-dominant phenomenon, but MDD patients did not show such a lateralized effect. Support vector machine (SVM) achieved the best classification performance in the beta frequency band with an accuracy of 89.7%, sensitivity of 89.4% and specificity of 89.9%. CONCLUSIONS MDD patients exhibited an altered FC in delta and beta bands, and the beta band showed a superiority in the diagnosis of MDD. SIGNIFICANCE Our study provided a promising reference for the diagnosis of MDD, and revealed a new perspective for understanding the topology of MDD brain networks during music perception.
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Affiliation(s)
- Wenya Liu
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China; Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland
| | - Chi Zhang
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China
| | - Xiaoyu Wang
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China
| | - Jing Xu
- Department of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, 116011 Dalian, China.
| | - Yi Chang
- Department of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, 116011 Dalian, China.
| | - Tapani Ristaniemi
- Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China; Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland; School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China; Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province. Dalian University of Technology, 116024 Dalian, China.
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Meijs M, Pegge SAH, Vos MHE, Patel A, van de Leemput SC, Koschmieder K, Prokop M, Meijer FJA, Manniesing R. Cerebral Artery and Vein Segmentation in Four-dimensional CT Angiography Using Convolutional Neural Networks. Radiol Artif Intell 2020; 2:e190178. [PMID: 33937832 DOI: 10.1148/ryai.2020190178] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 04/18/2020] [Accepted: 04/23/2020] [Indexed: 12/15/2022]
Abstract
Purpose To implement and test a deep learning approach for the segmentation of the arterial and venous cerebral vasculature with four-dimensional (4D) CT angiography. Materials and Methods Patients who had undergone 4D CT angiography for the suspicion of acute ischemic stroke were retrospectively identified. A total of 390 patients evaluated in 2014 (n = 113) or 2018 (n = 277) were included in this study, with each patient having undergone one 4D CT angiographic scan. One hundred patients from 2014 were randomly selected, and the arteries and veins on their CT scans were manually annotated by five experienced observers. The weighted temporal average and weighted temporal variance from 4D CT angiography were used as input for a three-dimensional Dense-U-Net. The network was trained with the fully annotated cerebral vessel artery-vein maps from 60 patients. Forty patients were used for quantitative evaluation. The relative absolute volume difference and the Dice similarity coefficient are reported. The neural network segmentations from 277 patients who underwent scanning in 2018 were qualitatively evaluated by an experienced neuroradiologist using a five-point scale. Results The average time for processing arterial and venous cerebral vasculature with the network was less than 90 seconds. The mean Dice similarity coefficient in the test set was 0.80 ± 0.04 (standard deviation) for the arteries and 0.88 ± 0.03 for the veins. The mean relative absolute volume difference was 7.3% ± 5.7 for the arteries and 8.5% ± 4.8 for the veins. Most of the segmentations (n = 273, 99.3%) were rated as very good to perfect. Conclusion The proposed convolutional neural network enables accurate artery and vein segmentation with 4D CT angiography with a processing time of less than 90 seconds.© RSNA, 2020.
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Affiliation(s)
- Midas Meijs
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein-Zuid 10, Nijmegen 6500 HB, the Netherlands
| | - Sjoert A H Pegge
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein-Zuid 10, Nijmegen 6500 HB, the Netherlands
| | - Maria H E Vos
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein-Zuid 10, Nijmegen 6500 HB, the Netherlands
| | - Ajay Patel
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein-Zuid 10, Nijmegen 6500 HB, the Netherlands
| | - Sil C van de Leemput
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein-Zuid 10, Nijmegen 6500 HB, the Netherlands
| | - Kevin Koschmieder
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein-Zuid 10, Nijmegen 6500 HB, the Netherlands
| | - Mathias Prokop
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein-Zuid 10, Nijmegen 6500 HB, the Netherlands
| | - Frederick J A Meijer
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein-Zuid 10, Nijmegen 6500 HB, the Netherlands
| | - Rashindra Manniesing
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein-Zuid 10, Nijmegen 6500 HB, the Netherlands
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Zuo XN. A machine learning window into brain waves. Neuroscience 2020; 436:167-169. [PMID: 32205203 DOI: 10.1016/j.neuroscience.2020.03.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 03/13/2020] [Accepted: 03/18/2020] [Indexed: 11/29/2022]
Affiliation(s)
- Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Key Laboratory for Brain and Education Sciences, School of Education Sciences, Nanning Normal University, Nanning 530001, Guangxi, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China.
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Castillo-Barnes D, Su L, Ramírez J, Salas-Gonzalez D, Martinez-Murcia FJ, Illan IA, Segovia F, Ortiz A, Cruchaga C, Farlow MR, Xiong C, Graff-Radford NR, Schofield PR, Masters CL, Salloway S, Jucker M, Mori H, Levin J, Gorriz JM. Autosomal Dominantly Inherited Alzheimer Disease: Analysis of genetic subgroups by Machine Learning. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2020; 58:153-167. [PMID: 32284705 PMCID: PMC7153760 DOI: 10.1016/j.inffus.2020.01.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Despite subjects with Dominantly-Inherited Alzheimer's Disease (DIAD) represent less than 1% of all Alzheimer's Disease (AD) cases, the Dominantly Inherited Alzheimer Network (DIAN) initiative constitutes a strong impact in the understanding of AD disease course with special emphasis on the presyptomatic disease phase. Until now, the 3 genes involved in DIAD pathogenesis (PSEN1, PSEN2 and APP) have been commonly merged into one group (Mutation Carriers, MC) and studied using conventional statistical analysis. Comparisons between groups using null-hypothesis testing or longitudinal regression procedures, such as the linear-mixed-effects models, have been assessed in the extant literature. Within this context, the work presented here performs a comparison between different groups of subjects by considering the 3 genes, either jointly or separately, and using tools based on Machine Learning (ML). This involves a feature selection step which makes use of ANOVA followed by Principal Component Analysis (PCA) to determine which features would be realiable for further comparison purposes. Then, the selected predictors are classified using a Support-Vector-Machine (SVM) in a nested k-Fold cross-validation resulting in maximum classification rates of 72-74% using PiB PET features, specially when comparing asymptomatic Non-Carriers (NC) subjects with asymptomatic PSEN1 Mutation-Carriers (PSEN1-MC). Results obtained from these experiments led to the idea that PSEN1-MC might be considered as a mixture of two different subgroups including: a first group whose patterns were very close to NC subjects, and a second group much more different in terms of imaging patterns. Thus, using a k-Means clustering algorithm it was determined both subgroups and a new classification scenario was conducted to validate this process. The comparison between each subgroup vs. NC subjects resulted in classification rates around 80% underscoring the importance of considering DIAN as an heterogeneous entity.
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Affiliation(s)
- Diego Castillo-Barnes
- Department of Signal Theory, Telematics and Communications, University of Granada, Granada (Spain)
| | - Li Su
- Department of Psychiatry, University of Cambridge, Cambridge (UK)
| | - Javier Ramírez
- Department of Signal Theory, Telematics and Communications, University of Granada, Granada (Spain)
| | - Diego Salas-Gonzalez
- Department of Signal Theory, Telematics and Communications, University of Granada, Granada (Spain)
| | | | - Ignacio A. Illan
- Department of Signal Theory, Telematics and Communications, University of Granada, Granada (Spain)
| | - Fermin Segovia
- Department of Signal Theory, Telematics and Communications, University of Granada, Granada (Spain)
| | - Andres Ortiz
- Department of Communications Engineering, University of Malaga, Malaga (Spain)
| | - Carlos Cruchaga
- Department of Psychiatry and Neurology, Washington University School of Medicine, St. Louis, Missouri (USA)
| | - Martin R. Farlow
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana (USA)
| | - Chengjie Xiong
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri (USA)
| | | | - Peter R. Schofield
- Neuroscience Research Australia and School of Medical Sciences, University of New South Wales, Sydney (Australia)
| | - Colin L. Masters
- Florey Institute and University of Melbourne, Victoria (Australia)
| | | | - Mathias Jucker
- Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen (Germany)
| | - Hiroshi Mori
- Department of Clinical Neuroscience, Osaka City University Medical school, Osaka (Japan)
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-University of Munich, Munich (Germany)
| | - Juan M. Gorriz
- Department of Signal Theory, Telematics and Communications, University of Granada, Granada (Spain)
- Department of Psychiatry, University of Cambridge, Cambridge (UK)
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Shinohara Y, Takahashi N, Lee Y, Ohmura T, Umetsu A, Kinoshita F, Kuya K, Kato A, Kinoshita T. Usefulness of deep learning-assisted identification of hyperdense MCA sign in acute ischemic stroke: comparison with readers' performance. Jpn J Radiol 2020; 38:870-877. [PMID: 32399602 DOI: 10.1007/s11604-020-00986-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 04/28/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE To evaluate the usefulness of deep learning-assisted diagnosis for identifying hyperdense middle cerebral artery sign (HMCAS) on non-contrast computed tomography in comparison with the diagnostic performance of neuroradiologists. MATERIALS AND METHODS We obtained 46 HMCAS-positive and 52 HMCAS-negative test samples extracted using 50-pixel-diameter circular regions of interest. Five neuroradiologists undertook an initial diagnostic performance test by describing the HMCAS-positive prediction rate in each sample. Their diagnostic performance was compared with that of a deep convolutional neural network (DCNN) model that had been trained using another dataset in our previous study. In the second test, readers could reference the prediction rate of the DCNN model in each sample. RESULTS The diagnostic performance of the DCNN for HMCAS showed an accuracy of 81.6% and area under the receiver-operating characteristic curve (AUC) of 0.869, whereas the initial diagnostic performance of neuroradiologists showed an accuracy of 78.8% and AUC of 0.882. The second diagnostic test of neuroradiologists with reference to the results of the DCNN model showed an accuracy of 84.7% and AUC of 0.932. In all readers, AUC values were higher in the second test than the initial test. CONCLUSION The ability of DCNN to identify HMCAS is comparable with the diagnostic performance of neuroradiologists.
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Affiliation(s)
- Yuki Shinohara
- Department of Radiology and Nuclear Medicine, Research Institute for Brain and Blood Vessels-Akita, 6-10 Senshu-kubota-machi, Akita, 010-0874, Japan.
| | - Noriyuki Takahashi
- Department of Radiology and Nuclear Medicine, Research Institute for Brain and Blood Vessels-Akita, 6-10 Senshu-kubota-machi, Akita, 010-0874, Japan.,Preparing Section for New Faculty of Medical Science, Fukushima Medical University, Fukushima, Japan
| | - Yongbum Lee
- Graduate School of Health Science, Niigata University, Niigata, Japan
| | - Tomomi Ohmura
- Department of Radiology and Nuclear Medicine, Research Institute for Brain and Blood Vessels-Akita, 6-10 Senshu-kubota-machi, Akita, 010-0874, Japan
| | - Atsushi Umetsu
- Department of Diagnostic Radiology, Tohoku University School of Medicine, Sendai, Japan
| | - Fumiko Kinoshita
- Department of Radiology and Nuclear Medicine, Research Institute for Brain and Blood Vessels-Akita, 6-10 Senshu-kubota-machi, Akita, 010-0874, Japan
| | - Keita Kuya
- Department of Radiology, Secomedic Hospital, Funabashi, Japan
| | - Ayumi Kato
- Division of Radiology, Department of Pathophysiological Therapeutic Science, Faculty of Medicine, Tottori University, Yonago, Japan
| | - Toshibumi Kinoshita
- Department of Radiology and Nuclear Medicine, Research Institute for Brain and Blood Vessels-Akita, 6-10 Senshu-kubota-machi, Akita, 010-0874, Japan
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48
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Chen X, Chen J, Cheng G, Gong T. Topics and trends in artificial intelligence assisted human brain research. PLoS One 2020; 15:e0231192. [PMID: 32251489 PMCID: PMC7135272 DOI: 10.1371/journal.pone.0231192] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 03/18/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in application scope of AI-assisted human brain research. A comprehensive understanding of this field is necessary to assess research efficacy, (re)allocate research resources, and conduct collaborations. This paper combines the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale, unstructured text of AI-assisted human brain research publications in the past decade. Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics, promising research orientations, and diverse topical distributions in influential countries/regions and research institutes. These findings help better understand scientific and technological AI-assisted human brain research, provide insightful guidance for resource (re)allocation, and promote effective international collaborations.
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Affiliation(s)
- Xieling Chen
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China
| | - Juan Chen
- Center for the Study of Applied Psychology, Guangdong Key Laboratory of Mental Health and Cognitive Science and the School of Psychology, South China Normal University, Guangzhou, China
| | - Gary Cheng
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China
- * E-mail: (GC); (TG)
| | - Tao Gong
- Center for Linguistics and Applied Linguistics, Guangdong University of Foreign Studies, Guangzhou, China
- Educational Testing Service, Princeton, NJ, United States of America
- * E-mail: (GC); (TG)
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49
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Cao P, Gao J, Zhang Z. Multi-View Based Multi-Model Learning for MCI Diagnosis. Brain Sci 2020; 10:brainsci10030181. [PMID: 32244855 PMCID: PMC7139974 DOI: 10.3390/brainsci10030181] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 03/16/2020] [Indexed: 12/26/2022] Open
Abstract
Mild cognitive impairment (MCI) is the early stage of Alzheimer’s disease (AD). Automatic diagnosis of MCI by magnetic resonance imaging (MRI) images has been the focus of research in recent years. Furthermore, deep learning models based on 2D view and 3D view have been widely used in the diagnosis of MCI. The deep learning architecture can capture anatomical changes in the brain from MRI scans to extract the underlying features of brain disease. In this paper, we propose a multi-view based multi-model (MVMM) learning framework, which effectively combines the local information of 2D images with the global information of 3D images. First, we select some 2D slices from MRI images and extract the features representing 2D local information. Then, we combine them with the features representing 3D global information learned from 3D images to train the MVMM learning framework. We evaluate our model on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed model can effectively recognize MCI through MRI images (accuracy of 87.50% for MCI/HC and accuracy of 83.18% for MCI/AD).
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50
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Keep RF, Jones HC, Drewes LR. This was the year that was: brain barriers and brain fluid research in 2019. Fluids Barriers CNS 2020; 17:20. [PMID: 32138786 PMCID: PMC7059280 DOI: 10.1186/s12987-020-00181-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
This editorial highlights advances in brain barrier and brain fluid research published in 2019, as well as addressing current controversies and pressing needs. Topics include recent advances related to: the cerebral endothelium and the neurovascular unit; the choroid plexus, arachnoid membrane; cerebrospinal fluid and the glymphatic hypothesis; the impact of disease states on brain barriers and brain fluids; drug delivery to the brain; and translation of preclinical data to the clinic. This editorial also mourns the loss of two important figures in the field, Malcolm B. Segal and Edward G. Stopa.
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Affiliation(s)
- Richard F. Keep
- Department of Neurosurgery, University of Michigan, R5018 BSRB, 109 Zina Pitcher Place, Ann Arbor, MI 48109-2200 USA
| | | | - Lester R. Drewes
- Department of Biomedical Sciences, University of Minnesota Medical School Duluth, Duluth, MN 55812 USA
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