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Tu S, Vucic S, Kiernan MC. Pathological insights derived from neuroimaging in amyotrophic lateral sclerosis: emerging clinical applications. Curr Opin Neurol 2024; 37:577-584. [PMID: 38958573 DOI: 10.1097/wco.0000000000001295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
PURPOSE OF REVIEW Neuroimaging has been instrumental in shaping current understanding of the pathoanatomical signature of amyotrophic lateral sclerosis (ALS) across clinically well defined patient cohorts. The potential utility of imaging as an objective disease marker, however, remains poorly defined. RECENT FINDINGS Increasingly advanced quantitative and computational imaging studies have highlighted emerging clinical applications for neuroimaging as a complementary clinical modality for diagnosis, monitoring, and modelling disease propagation. Multimodal neuroimaging has demonstrated novel approaches for capturing primary motor disease. Extra-motor subcortical dysfunction is increasingly recognized as key modulators of disease propagation. SUMMARY The neural signature of cortical and subcortical dysfunction in ALS has been well defined at the population level. Objective metrics of focal primary motor dysfunction are increasingly sensitive and translatable to the individual patient level. Integrity of extra-motor subcortical abnormalities are recognized to represent critical pathways of the ALS disease 'connectome', predicting pathological spread. Neuroimaging plays a pivotal role in capturing upper motor neuron pathology in ALS. Their potential clinical role as objective disease markers for disease classification, longitudinal monitoring, and prognosis in ALS have become increasingly well defined.
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Affiliation(s)
- Sicong Tu
- Brain and Mind Centre
- Sydney Medical School, Faculty of Medicine and Health, The University of Sydney
- Neuroscience Research Australia
| | - Steve Vucic
- Brain and Nerve Research Center, The University of Sydney, Sydney, New South Wales, Australia
| | - Matthew C Kiernan
- Brain and Mind Centre
- Sydney Medical School, Faculty of Medicine and Health, The University of Sydney
- Neuroscience Research Australia
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Carvalho Macruz FBD, Dias ALMP, Andrade CS, Nucci MP, Rimkus CDM, Lucato LT, Rocha AJD, Kitamura FC. The new era of artificial intelligence in neuroradiology: current research and promising tools. ARQUIVOS DE NEURO-PSIQUIATRIA 2024; 82:1-12. [PMID: 38565188 PMCID: PMC10987255 DOI: 10.1055/s-0044-1779486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/13/2023] [Indexed: 04/04/2024]
Abstract
Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.
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Affiliation(s)
- Fabíola Bezerra de Carvalho Macruz
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Rede D'Or São Luiz, Departamento de Radiologia e Diagnóstico por Imagem, São Paulo SP, Brazil.
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
- Academia Nacional de Medicina, Rio de Janeiro RJ, Brazil.
| | | | | | - Mariana Penteado Nucci
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
| | - Carolina de Medeiros Rimkus
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Rede D'Or São Luiz, Departamento de Radiologia e Diagnóstico por Imagem, São Paulo SP, Brazil.
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
| | - Leandro Tavares Lucato
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Diagnósticos da América SA, São Paulo SP, Brazil.
| | | | - Felipe Campos Kitamura
- Diagnósticos da América SA, São Paulo SP, Brazil.
- Universidade Federal de São Paulo, São Paulo SP, Brazil.
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Castelli L, Vasta R, Allen SP, Waller R, Chiò A, Traynor BJ, Kirby J. From use of omics to systems biology: Identifying therapeutic targets for amyotrophic lateral sclerosis. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2024; 176:209-268. [PMID: 38802176 DOI: 10.1016/bs.irn.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Amyotrophic lateral sclerosis (ALS) is a heterogeneous progressive neurodegenerative disorder with available treatments such as riluzole and edaravone extending survival by an average of 3-6 months. The lack of highly effective, widely available therapies reflects the complexity of ALS. Omics technologies, including genomics, transcriptomic and proteomics have contributed to the identification of biological pathways dysregulated and targeted by therapeutic strategies in preclinical and clinical trials. Integrating clinical, environmental and neuroimaging information with omics data and applying a systems biology approach can further improve our understanding of the disease with the potential to stratify patients and provide more personalised medicine. This chapter will review the omics technologies that contribute to a systems biology approach and how these components have assisted in identifying therapeutic targets. Current strategies, including the use of genetic screening and biosampling in clinical trials, as well as the future application of additional technological advances, will also be discussed.
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Affiliation(s)
- Lydia Castelli
- Sheffield Institute for Translational Neuroscience, School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom; Neuroscience Institute, University of Sheffield, Sheffield, United Kingdom
| | - Rosario Vasta
- ALS Expert Center,'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Turin, Italy; Neuromuscular Diseases Research Section, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States
| | - Scott P Allen
- Sheffield Institute for Translational Neuroscience, School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom; Neuroscience Institute, University of Sheffield, Sheffield, United Kingdom
| | - Rachel Waller
- Sheffield Institute for Translational Neuroscience, School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom; Neuroscience Institute, University of Sheffield, Sheffield, United Kingdom
| | - Adriano Chiò
- ALS Expert Center,'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Turin, Italy; Neurology 1, Azienda Ospedaliero-Universitaria Città della Salute e della Scienza of Turin, Turin, Italy
| | - Bryan J Traynor
- Neuromuscular Diseases Research Section, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States; RNA Therapeutics Laboratory, National Center for Advancing Translational Sciences, NIH, Rockville, MD, United States; National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States; Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, United States; Reta Lila Weston Institute, UCL Queen Square Institute of Neurology,University College London, London, United Kingdom
| | - Janine Kirby
- Sheffield Institute for Translational Neuroscience, School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom; Neuroscience Institute, University of Sheffield, Sheffield, United Kingdom.
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Papaiz F, Dourado MET, de Medeiros Valentim RA, Pinto R, de Morais AHF, Arrais JP. Ensemble-imbalance-based classification for amyotrophic lateral sclerosis prognostic prediction: identifying short-survival patients at diagnosis. BMC Med Inform Decis Mak 2024; 24:80. [PMID: 38504285 PMCID: PMC10949816 DOI: 10.1186/s12911-024-02484-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 03/14/2024] [Indexed: 03/21/2024] Open
Abstract
Prognosticating Amyotrophic Lateral Sclerosis (ALS) presents a formidable challenge due to patients exhibiting different onset sites, progression rates, and survival times. In this study, we have developed and evaluated Machine Learning (ML) algorithms that integrate Ensemble and Imbalance Learning techniques to classify patients into Short and Non-Short survival groups based on data collected during diagnosis. We aimed to identify individuals at high risk of mortality within 24 months of symptom onset through analysis of patient data commonly encountered in daily clinical practice. Our Ensemble-Imbalance approach underwent evaluation employing six ML algorithms as base classifiers. Remarkably, our results outperformed those of individual algorithms, achieving a Balanced Accuracy of 88% and a Sensitivity of 96%. Additionally, we used the Shapley Additive Explanations framework to elucidate the decision-making process of the top-performing model, pinpointing the most important features and their correlations with the target prediction. Furthermore, we presented helpful tools to visualize and compare patient similarities, offering valuable insights. Confirming the obtained results, our approach could aid physicians in devising personalized treatment plans at the time of diagnosis or serve as an inclusion/exclusion criterion in clinical trials.
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Affiliation(s)
- Fabiano Papaiz
- Federal University of Rio Grande Do Norte, Natal, Brazil.
- University of Coimbra, Coimbra, Portugal.
- Federal Institute of Rio Grande Do Norte, Natal, Brazil.
| | | | | | - Rafael Pinto
- Federal University of Rio Grande Do Norte, Natal, Brazil
- Federal Institute of Rio Grande Do Norte, Natal, Brazil
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Rabadi MH, Russell KC, Xu C. Predictors of Mortality in Veterans with Amyotrophic Lateral Sclerosis: Respiratory Status and Speech Disorder at Presentation. Med Sci Monit 2024; 30:e943288. [PMID: 38409777 PMCID: PMC10908188 DOI: 10.12659/msm.943288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 12/18/2023] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND There is a lack of accurate models to predict amyotrophic lateral sclerosis (ALS) disease course and outcomes. As a result, risk assessment and counseling, the timing of interventions, and their stratification in clinical trials are difficult. This study aimed to evaluate the association between symptoms at presentation and mortality. MATERIAL AND METHODS A single veterans hospital reviewed the electronic records of 105 veterans with ALS who were periodically followed in our ALS clinic between 2010 and 2021. A survival decision tree (≤3 or >3 years) was generated based on the statistical median survival of our data. The variables known to influence survival when alive were compared to patients who died. RESULTS The (mean±SD) age at onset was 62±11 years, M/F ratio 101: 4, and 90% were non-Hispanic whites. The initial score for the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) was 31±8.3. Dysarthria and shortness of breath (SOB) were present on initial presentation in 52 (49.5%) and 32 (30.5%) patients, respectively. Deaths occurred in 80 (76.2%) patients during the study period. The main cause of death was respiratory disease (failure and pneumonia, n=43 53.75%). Patients survived for >3 years on initial presentation with normal respiration and speech, compared to ≤3 years of survival in patients with dysarthria and SOB, irrespective of age. CONCLUSIONS This study suggests that for veterans with ALS, the main predictors of shorter survival were respiratory status and speech disorder on initial presentation to the clinic.
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Affiliation(s)
- Meheroz H. Rabadi
- Department of Neurology, Oklahoma City VA Medical Center, Edmond, OK, USA
| | | | - Chao Xu
- Department of Biostatistics, University of Oklahoma, Oklahoma City, OK, USA
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Kuan LH, Parnianpour P, Kushol R, Kumar N, Anand T, Kalra S, Greiner R. Accurate personalized survival prediction for amyotrophic lateral sclerosis patients. Sci Rep 2023; 13:20713. [PMID: 38001260 PMCID: PMC10673879 DOI: 10.1038/s41598-023-47935-7] [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: 05/19/2023] [Accepted: 11/20/2023] [Indexed: 11/26/2023] Open
Abstract
Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease. Accurately predicting the survival time for ALS patients can help patients and clinicians to plan for future treatment and care. We describe the application of a machine-learned tool that incorporates clinical features and cortical thickness from brain magnetic resonance (MR) images to estimate the time until a composite respiratory failure event for ALS patients, and presents the prediction as individual survival distributions (ISDs). These ISDs provide the probability of survival (none of the respiratory failures) at multiple future time points, for each individual patient. Our learner considers several survival prediction models, and selects the best model to provide predictions. We evaluate our learned model using the mean absolute error margin (MAE-margin), a modified version of mean absolute error that handles data with censored outcomes. We show that our tool can provide helpful information for patients and clinicians in planning future treatment.
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Affiliation(s)
- Li-Hao Kuan
- Department of Computing Science, University of Alberta, Edmonton, Canada.
| | - Pedram Parnianpour
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
| | - Rafsanjany Kushol
- Department of Computing Science, University of Alberta, Edmonton, Canada
| | - Neeraj Kumar
- Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada
| | - Tanushka Anand
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Canada
| | - Sanjay Kalra
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Canada
| | - Russell Greiner
- Department of Computing Science, University of Alberta, Edmonton, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
- Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada
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Wang H, Xu S, Fang KB, Dai ZS, Wei GZ, Chen LF. Contrast-enhanced magnetic resonance image segmentation based on improved U-Net and Inception-ResNet in the diagnosis of spinal metastases. J Bone Oncol 2023; 42:100498. [PMID: 37670740 PMCID: PMC10475503 DOI: 10.1016/j.jbo.2023.100498] [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: 02/17/2023] [Revised: 07/18/2023] [Accepted: 07/26/2023] [Indexed: 09/07/2023] Open
Abstract
Objective The objective of this study was to investigate the use of contrast-enhanced magnetic resonance imaging (CE-MRI) combined with radiomics and deep learning technology for the identification of spinal metastases and primary malignant spinal bone tumor. Methods The region growing algorithm was utilized to segment the lesions, and two parameters were defined based on the region of interest (ROI). Deep learning algorithms were employed: improved U-Net, which utilized CE-MRI parameter maps as input, and used 10 layers of CE images as input. Inception-ResNet model was used to extract relevant features for disease identification and construct a diagnosis classifier. Results The diagnostic accuracy of radiomics was 0.74, while the average diagnostic accuracy of improved U-Net was 0.98, respectively. the PA of our model is as high as 98.001%. The findings indicate that CE-MRI based radiomics and deep learning have the potential to assist in the differential diagnosis of spinal metastases and primary malignant spinal bone tumor. Conclusion CE-MRI combined with radiomics and deep learning technology can potentially assist in the differential diagnosis of spinal metastases and primary malignant spinal bone tumor, providing a promising approach for clinical diagnosis.
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Affiliation(s)
- Hai Wang
- Department of Orthopedics, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Department of Orthopedics, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212,China
| | - Shaohua Xu
- Department of Hepatobiliary and Pancreatic Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China
| | - Kai-bin Fang
- Department of Orthopedics, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Zhang-Sheng Dai
- Department of Orthopedics, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Guo-Zhen Wei
- Department of Orthopedics, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Department of Orthopedics, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212,China
| | - Lu-Feng Chen
- Department of Thoracic and Cardiovascular Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, China
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Bede P, Lulé D, Müller HP, Tan EL, Dorst J, Ludolph AC, Kassubek J. Presymptomatic grey matter alterations in ALS kindreds: a computational neuroimaging study of asymptomatic C9orf72 and SOD1 mutation carriers. J Neurol 2023; 270:4235-4247. [PMID: 37178170 PMCID: PMC10421803 DOI: 10.1007/s00415-023-11764-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/02/2023] [Accepted: 05/03/2023] [Indexed: 05/15/2023]
Abstract
BACKGROUND The characterisation of presymptomatic disease-burden patterns in asymptomatic mutation carriers has a dual academic and clinical relevance. The understanding of disease propagation mechanisms is of considerable conceptual interests, and defining the optimal time of pharmacological intervention is essential for improved clinical trial outcomes. METHODS In a prospective, multimodal neuroimaging study, 22 asymptomatic C9orf72 GGGGCC hexanucleotide repeat carriers, 13 asymptomatic subjects with SOD1, and 54 "gene-negative" ALS kindreds were enrolled. Cortical and subcortical grey matter alterations were systematically appraised using volumetric, morphometric, vertex, and cortical thickness analyses. Using a Bayesian approach, the thalamus and amygdala were further parcellated into specific nuclei and the hippocampus was segmented into anatomically defined subfields. RESULTS Asymptomatic GGGGCC hexanucleotide repeat carriers in C9orf72 exhibited early subcortical changes with the preferential involvement of the pulvinar and mediodorsal regions of the thalamus, as well as the lateral aspect of the hippocampus. Volumetric approaches, morphometric methods, and vertex analyses were anatomically consistent in capturing focal subcortical changes in asymptomatic C9orf72 hexanucleotide repeat expansion carriers. SOD1 mutation carriers did not exhibit significant subcortical grey matter alterations. In our study, none of the two asymptomatic cohorts exhibited cortical grey matter alterations on either cortical thickness or morphometric analyses. DISCUSSION The presymptomatic radiological signature of C9orf72 is associated with selective thalamic and focal hippocampal degeneration which may be readily detectable before cortical grey matter changes ensue. Our findings confirm selective subcortical grey matter involvement early in the course of C9orf72-associated neurodegeneration.
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Affiliation(s)
- Peter Bede
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Dublin, D02 RS90, Ireland.
- Department of Neurology, St James's Hospital, Dublin, Ireland.
| | - Dorothée Lulé
- Department of Neurology, University of Ulm, Ulm, Germany
| | | | - Ee Ling Tan
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Dublin, D02 RS90, Ireland
| | - Johannes Dorst
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Albert C Ludolph
- Department of Neurology, University of Ulm, Ulm, Germany
- German Centre of Neurodegenerative Diseases (DZNE), Ulm, Germany
| | - Jan Kassubek
- Department of Neurology, University of Ulm, Ulm, Germany
- German Centre of Neurodegenerative Diseases (DZNE), Ulm, Germany
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Bede P, Pradat PF. Editorial: The gap between academic advances and therapy development in motor neuron disease. Curr Opin Neurol 2023; 36:335-337. [PMID: 37462047 DOI: 10.1097/wco.0000000000001179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Affiliation(s)
- Peter Bede
- Computational Neuroimaging Group, School of Medicine, Trinity College
- Department of Neurology, St James's Hospital, Dublin, Ireland
- Department of Neurology, Pitié-Salpêtrière University Hospital
| | - Pierre-Francois Pradat
- Department of Neurology, Pitié-Salpêtrière University Hospital
- Laboratoire d'Imagerie Biomédicale, Sorbonne University, CNRS, INSERM, Paris, France
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Tavazzi E, Longato E, Vettoretti M, Aidos H, Trescato I, Roversi C, Martins AS, Castanho EN, Branco R, Soares DF, Guazzo A, Birolo G, Pala D, Bosoni P, Chiò A, Manera U, de Carvalho M, Miranda B, Gromicho M, Alves I, Bellazzi R, Dagliati A, Fariselli P, Madeira SC, Di Camillo B. Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review. Artif Intell Med 2023; 142:102588. [PMID: 37316101 DOI: 10.1016/j.artmed.2023.102588] [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: 12/22/2022] [Revised: 04/14/2023] [Accepted: 05/16/2023] [Indexed: 06/16/2023]
Abstract
BACKGROUND Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous. OBJECTIVE This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS. METHODS We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics. RESULTS Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only. CONCLUSION This systematic review highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.
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Affiliation(s)
- Erica Tavazzi
- Department of Information Engineering, University of Padova, Via Gradenigo 6/b, Padua, 35131, Italy
| | - Enrico Longato
- Department of Information Engineering, University of Padova, Via Gradenigo 6/b, Padua, 35131, Italy
| | - Martina Vettoretti
- Department of Information Engineering, University of Padova, Via Gradenigo 6/b, Padua, 35131, Italy
| | - Helena Aidos
- LASIGE and Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisbon, 1749-016, Portugal
| | - Isotta Trescato
- Department of Information Engineering, University of Padova, Via Gradenigo 6/b, Padua, 35131, Italy
| | - Chiara Roversi
- Department of Information Engineering, University of Padova, Via Gradenigo 6/b, Padua, 35131, Italy
| | - Andreia S Martins
- LASIGE and Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisbon, 1749-016, Portugal
| | - Eduardo N Castanho
- LASIGE and Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisbon, 1749-016, Portugal
| | - Ruben Branco
- LASIGE and Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisbon, 1749-016, Portugal
| | - Diogo F Soares
- LASIGE and Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisbon, 1749-016, Portugal
| | - Alessandro Guazzo
- Department of Information Engineering, University of Padova, Via Gradenigo 6/b, Padua, 35131, Italy
| | - Giovanni Birolo
- Department of Medical Sciences, University of Torino, Corso Dogliotti 14, Turin, 10126, Italy
| | - Daniele Pala
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, Pavia, 27100, Italy
| | - Pietro Bosoni
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, Pavia, 27100, Italy
| | - Adriano Chiò
- Department of Neurosciences "Rita Levi Montalcini", University of Turin, Via Cherasco 15, Turin, 10126, Italy
| | - Umberto Manera
- Department of Neurosciences "Rita Levi Montalcini", University of Turin, Via Cherasco 15, Turin, 10126, Italy
| | - Mamede de Carvalho
- Faculdade de Medicina, Instituto de Medicina Molecular João Lobo Antunes, Universidade de Lisboa, Av. Prof. Egas Moniz, Lisbon, 1649-028, Portugal
| | - Bruno Miranda
- Faculdade de Medicina, Instituto de Medicina Molecular João Lobo Antunes, Universidade de Lisboa, Av. Prof. Egas Moniz, Lisbon, 1649-028, Portugal
| | - Marta Gromicho
- Faculdade de Medicina, Instituto de Medicina Molecular João Lobo Antunes, Universidade de Lisboa, Av. Prof. Egas Moniz, Lisbon, 1649-028, Portugal
| | - Inês Alves
- Faculdade de Medicina, Instituto de Medicina Molecular João Lobo Antunes, Universidade de Lisboa, Av. Prof. Egas Moniz, Lisbon, 1649-028, Portugal
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, Pavia, 27100, Italy
| | - Arianna Dagliati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, Pavia, 27100, Italy
| | - Piero Fariselli
- Department of Medical Sciences, University of Torino, Corso Dogliotti 14, Turin, 10126, Italy
| | - Sara C Madeira
- LASIGE and Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisbon, 1749-016, Portugal
| | - Barbara Di Camillo
- Department of Information Engineering, University of Padova, Via Gradenigo 6/b, Padua, 35131, Italy; Department of Comparative Biomedicine and Food Science, University of Padova, Agripolis, Viale dell'Università, 16, Legnaro (PD), 35020, Italy.
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Lopez-Bernal D, Balderas D, Ponce P, Rojas M, Molina A. Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases-A Review. Life (Basel) 2023; 13:life13041031. [PMID: 37109560 PMCID: PMC10146231 DOI: 10.3390/life13041031] [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: 12/22/2022] [Revised: 02/17/2023] [Accepted: 03/29/2023] [Indexed: 04/29/2023] Open
Abstract
Motor neuron diseases (MNDs) are a group of chronic neurological disorders characterized by the progressive failure of the motor system. Currently, these disorders do not have a definitive treatment; therefore, it is of huge importance to propose new and more advanced diagnoses and treatment options for MNDs. Nowadays, artificial intelligence is being applied to solve several real-life problems in different areas, including healthcare. It has shown great potential to accelerate the understanding and management of many health disorders, including neurological ones. Therefore, the main objective of this work is to offer a review of the most important research that has been done on the application of artificial intelligence models for analyzing motor disorders. This review includes a general description of the most commonly used AI algorithms and their usage in MND diagnosis, prognosis, and treatment. Finally, we highlight the main issues that must be overcome to take full advantage of what AI can offer us when dealing with MNDs.
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Affiliation(s)
- Diego Lopez-Bernal
- Tecnologico de Monterrey, National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
| | - David Balderas
- Tecnologico de Monterrey, National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
| | - Pedro Ponce
- Tecnologico de Monterrey, National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
| | - Mario Rojas
- Tecnologico de Monterrey, National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
| | - Arturo Molina
- Tecnologico de Monterrey, National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico
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12
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Soares DF, Henriques R, Gromicho M, de Carvalho M, Madeira SC. Triclustering-based classification of longitudinal data for prognostic prediction: targeting relevant clinical endpoints in amyotrophic lateral sclerosis. Sci Rep 2023; 13:6182. [PMID: 37061549 PMCID: PMC10105751 DOI: 10.1038/s41598-023-33223-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 04/10/2023] [Indexed: 04/17/2023] Open
Abstract
This work proposes a new class of explainable prognostic models for longitudinal data classification using triclusters. A new temporally constrained triclustering algorithm, termed TCtriCluster, is proposed to comprehensively find informative temporal patterns common to a subset of patients in a subset of features (triclusters), and use them as discriminative features within a state-of-the-art classifier with guarantees of interpretability. The proposed approach further enhances prediction with the potentialities of model explainability by revealing clinically relevant disease progression patterns underlying prognostics, describing features used for classification. The proposed methodology is used in the Amyotrophic Lateral Sclerosis (ALS) Portuguese cohort (N = 1321), providing the first comprehensive assessment of the prognostic limits of five notable clinical endpoints: need for non-invasive ventilation (NIV); need for an auxiliary communication device; need for percutaneous endoscopic gastrostomy (PEG); need for a caregiver; and need for a wheelchair. Triclustering-based predictors outperform state-of-the-art alternatives, being able to predict the need for auxiliary communication device (within 180 days) and the need for PEG (within 90 days) with an AUC above 90%. The approach was validated in clinical practice, supporting healthcare professionals in understanding the link between the highly heterogeneous patterns of ALS disease progression and the prognosis.
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Affiliation(s)
- Diogo F Soares
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.
| | - Rui Henriques
- INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Marta Gromicho
- Instituto de Medicina Molecular and Instituto de Fisiologia, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Mamede de Carvalho
- Instituto de Medicina Molecular and Instituto de Fisiologia, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Sara C Madeira
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
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13
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Iqbal S, N. Qureshi A, Li J, Mahmood T. On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:3173-3233. [PMID: 37260910 PMCID: PMC10071480 DOI: 10.1007/s11831-023-09899-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 02/19/2023] [Indexed: 06/02/2023]
Abstract
Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.
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Affiliation(s)
- Saeed Iqbal
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Punjab 54000 Pakistan
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 Beijing China
| | - Adnan N. Qureshi
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Punjab 54000 Pakistan
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 Beijing China
- Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing, 100124 Beijing China
| | - Tariq Mahmood
- Artificial Intelligence and Data Analytics (AIDA) Lab, College of Computer & Information Sciences (CCIS), Prince Sultan University, Riyadh, 11586 Kingdom of Saudi Arabia
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14
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Coll L, Pareto D, Carbonell-Mirabent P, Cobo-Calvo Á, Arrambide G, Vidal-Jordana Á, Comabella M, Castilló J, Rodríguez-Acevedo B, Zabalza A, Galán I, Midaglia L, Nos C, Salerno A, Auger C, Alberich M, Río J, Sastre-Garriga J, Oliver A, Montalban X, Rovira À, Tintoré M, Lladó X, Tur C. Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI. Neuroimage Clin 2023; 38:103376. [PMID: 36940621 PMCID: PMC10034138 DOI: 10.1016/j.nicl.2023.103376] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 03/09/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023]
Abstract
The application of convolutional neural networks (CNNs) to MRI data has emerged as a promising approach to achieving unprecedented levels of accuracy when predicting the course of neurological conditions, including multiple sclerosis, by means of extracting image features not detectable through conventional methods. Additionally, the study of CNN-derived attention maps, which indicate the most relevant anatomical features for CNN-based decisions, has the potential to uncover key disease mechanisms leading to disability accumulation. From a cohort of patients prospectively followed up after a first demyelinating attack, we selected those with T1-weighted and T2-FLAIR brain MRI sequences available for image analysis and a clinical assessment performed within the following six months (N = 319). Patients were divided into two groups according to expanded disability status scale (EDSS) score: ≥3.0 and < 3.0. A 3D-CNN model predicted the class using whole-brain MRI scans as input. A comparison with a logistic regression (LR) model using volumetric measurements as explanatory variables and a validation of the CNN model on an independent dataset with similar characteristics (N = 440) were also performed. The layer-wise relevance propagation method was used to obtain individual attention maps. The CNN model achieved a mean accuracy of 79% and proved to be superior to the equivalent LR-model (77%). Additionally, the model was successfully validated in the independent external cohort without any re-training (accuracy = 71%). Attention-map analyses revealed the predominant role of frontotemporal cortex and cerebellum for CNN decisions, suggesting that the mechanisms leading to disability accrual exceed the mere presence of brain lesions or atrophy and probably involve how damage is distributed in the central nervous system.
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Affiliation(s)
- Llucia Coll
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology (IDI), Vall d'Hebron University Hospital, Spain, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Pere Carbonell-Mirabent
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Álvaro Cobo-Calvo
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Georgina Arrambide
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Ángela Vidal-Jordana
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Manuel Comabella
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Joaquín Castilló
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Breogán Rodríguez-Acevedo
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Ana Zabalza
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Ingrid Galán
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Luciana Midaglia
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Carlos Nos
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Annalaura Salerno
- Section of Neuroradiology, Department of Radiology (IDI), Vall d'Hebron University Hospital, Spain, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Cristina Auger
- Section of Neuroradiology, Department of Radiology (IDI), Vall d'Hebron University Hospital, Spain, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Manel Alberich
- Section of Neuroradiology, Department of Radiology (IDI), Vall d'Hebron University Hospital, Spain, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jordi Río
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jaume Sastre-Garriga
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Arnau Oliver
- Research institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Xavier Montalban
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology (IDI), Vall d'Hebron University Hospital, Spain, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Mar Tintoré
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Xavier Lladó
- Research institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Carmen Tur
- Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
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Visibelli A, Roncaglia B, Spiga O, Santucci A. The Impact of Artificial Intelligence in the Odyssey of Rare Diseases. Biomedicines 2023; 11:887. [PMID: 36979866 PMCID: PMC10045927 DOI: 10.3390/biomedicines11030887] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/28/2023] [Accepted: 03/08/2023] [Indexed: 03/16/2023] Open
Abstract
Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1-9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.
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Affiliation(s)
- Anna Visibelli
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Bianca Roncaglia
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Ottavia Spiga
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
- Competence Center ARTES 4.0, 53100 Siena, Italy
- SienabioACTIVE—SbA, 53100 Siena, Italy
| | - Annalisa Santucci
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
- Competence Center ARTES 4.0, 53100 Siena, Italy
- SienabioACTIVE—SbA, 53100 Siena, Italy
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16
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Diffusion Tensor Imaging in Amyotrophic Lateral Sclerosis: Machine Learning for Biomarker Development. Int J Mol Sci 2023; 24:ijms24031911. [PMID: 36768231 PMCID: PMC9915541 DOI: 10.3390/ijms24031911] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/11/2023] [Accepted: 01/16/2023] [Indexed: 01/21/2023] Open
Abstract
Diffusion tensor imaging (DTI) allows the in vivo imaging of pathological white matter alterations, either with unbiased voxel-wise or hypothesis-guided tract-based analysis. Alterations of diffusion metrics are indicative of the cerebral status of patients with amyotrophic lateral sclerosis (ALS) at the individual level. Using machine learning (ML) models to analyze complex and high-dimensional neuroimaging data sets, new opportunities for DTI-based biomarkers in ALS arise. This review aims to summarize how different ML models based on DTI parameters can be used for supervised diagnostic classifications and to provide individualized patient stratification with unsupervised approaches in ALS. To capture the whole spectrum of neuropathological signatures, DTI might be combined with additional modalities, such as structural T1w 3-D MRI in ML models. To further improve the power of ML in ALS and enable the application of deep learning models, standardized DTI protocols and multi-center collaborations are needed to validate multimodal DTI biomarkers. The application of ML models to multiparametric MRI/multimodal DTI-based data sets will enable a detailed assessment of neuropathological signatures in patients with ALS and the development of novel neuroimaging biomarkers that could be used in the clinical workup.
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17
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Canosa A, Martino A, Manera U, Vasta R, Grassano M, Palumbo F, Cabras S, Di Pede F, Arena V, Moglia C, Giuliani A, Calvo A, Chiò A, Pagani M. Role of brain 2-[ 18F]fluoro-2-deoxy-D-glucose-positron-emission tomography as survival predictor in amyotrophic lateral sclerosis. Eur J Nucl Med Mol Imaging 2023; 50:784-791. [PMID: 36308536 PMCID: PMC9852209 DOI: 10.1007/s00259-022-05987-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 09/29/2022] [Indexed: 01/24/2023]
Abstract
PURPOSE The identification of prognostic tools in amyotrophic lateral sclerosis (ALS) would improve the design of clinical trials, the management of patients, and life planning. We aimed to evaluate the accuracy of brain 2-[18F]fluoro-2-deoxy-D-glucose-positron-emission tomography (2-[18F]FDG-PET) as an independent predictor of survival in ALS. METHODS A prospective cohort study enrolled 418 ALS patients, who underwent brain 2-[18F]FDG-PET at diagnosis and whose survival time was available. We discretized the survival time in a finite number of classes in a data-driven fashion by employing a k-means-like strategy. We identified "hot brain regions" with maximal power in discriminating survival classes, by evaluating the Laplacian scores in a class-aware fashion. We retained the top-m features for each class to train the classification systems (i.e., a support vector machine, SVM), using 10% of the ALS cohort as test set. RESULTS Data were discretized in three survival profiles: 0-2 years, 2-5 years, and > 5 years. SVM resulted in an error rate < 20% for two out of three classes separately. As for class one, the discriminant clusters included left caudate body and anterior cingulate cortex. The most discriminant regions were bilateral cerebellar pyramid in class two, and right cerebellar dentate nucleus, and left cerebellar nodule in class three. CONCLUSION Brain 2-[18F]FDG-PET along with artificial intelligence was able to predict with high accuracy the survival time range in our ALS cohort. Healthcare professionals can benefit from this prognostic tool for planning patients' management and follow-up. 2-[18F]FDG-PET represents a promising biomarker for individual patients' stratification in clinical trials. The lack of a multicentre external validation of the model warrants further studies to evaluate its generalization capability.
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Affiliation(s)
- Antonio Canosa
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy ,grid.432329.d0000 0004 1789 4477SC Neurologia 1U, Azienda Ospedaliero-Universitaria Città della Salute e della Scienza di Torino, Turin, Italy ,grid.428479.40000 0001 2297 9633Institute of Cognitive Sciences and Technologies, C.N.R., Rome, Italy
| | - Alessio Martino
- grid.428479.40000 0001 2297 9633Institute of Cognitive Sciences and Technologies, C.N.R., Rome, Italy ,grid.18038.320000 0001 2180 8787Department of Business and Management, LUISS University, Viale Romania 32, 00197 Rome, Italy
| | - Umberto Manera
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy ,grid.432329.d0000 0004 1789 4477SC Neurologia 1U, Azienda Ospedaliero-Universitaria Città della Salute e della Scienza di Torino, Turin, Italy
| | - Rosario Vasta
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy
| | - Maurizio Grassano
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy
| | - Francesca Palumbo
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy
| | - Sara Cabras
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy
| | - Francesca Di Pede
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy
| | - Vincenzo Arena
- Positron Emission Tomography Centre AFFIDEA-IRMET S.p.A., Turin, Italy
| | - Cristina Moglia
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy ,grid.432329.d0000 0004 1789 4477SC Neurologia 1U, Azienda Ospedaliero-Universitaria Città della Salute e della Scienza di Torino, Turin, Italy
| | - Alessandro Giuliani
- Environment and Health Department, Istituto Superiore di Sanità, Rome, Italy
| | - Andrea Calvo
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy ,grid.432329.d0000 0004 1789 4477SC Neurologia 1U, Azienda Ospedaliero-Universitaria Città della Salute e della Scienza di Torino, Turin, Italy ,grid.7605.40000 0001 2336 6580Neuroscience Institute of Turin (NIT), Turin, Italy
| | - Adriano Chiò
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy ,grid.432329.d0000 0004 1789 4477SC Neurologia 1U, Azienda Ospedaliero-Universitaria Città della Salute e della Scienza di Torino, Turin, Italy ,grid.428479.40000 0001 2297 9633Institute of Cognitive Sciences and Technologies, C.N.R., Rome, Italy ,grid.7605.40000 0001 2336 6580Neuroscience Institute of Turin (NIT), Turin, Italy
| | - Marco Pagani
- grid.428479.40000 0001 2297 9633Institute of Cognitive Sciences and Technologies, C.N.R., Rome, Italy ,grid.24381.3c0000 0000 9241 5705Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
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Ashhurst JF, Tu S, Timmins HC, Kiernan MC. Progress, development, and challenges in amyotrophic lateral sclerosis clinical trials. Expert Rev Neurother 2022; 22:905-913. [PMID: 36543326 DOI: 10.1080/14737175.2022.2161893] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Amyotrophic Lateral Sclerosis (ALS) brings unique challenges to a clinical trial setting, due in part to relatively low disease prevalence coupled with a poor prognosis, in addition to the complexities linked to disease heterogeneity. As critical understanding of the disease develops, particularly in relation to clinical phenotype and the mechanisms of disease progression, so too new concepts evolve in relation to clinical trials, including the advent of precision therapy, targeted to subgroups of ALS patients. AREAS COVERED Individualized, or precision medicine in ALS recognizes the heterogeneous nature of the disease and utilizes information such as the clinical phenotype of the disease, clinical biomarkers, and genotyping to promote a tailored approach to treatment. Separate to these considerations, the present review will discuss clinical trial design and how this can be improved to better match patient and investigator needs in ALS clinical trials. EXPERT OPINION Precision therapy will promote a more focused treatment approach, with the goal of improving clinical outcomes for ALS patients. An increased community awareness of ALS, coupled with significant industry and philanthropic funding for ALS research, is accelerating this process.
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Affiliation(s)
| | - Sicong Tu
- Brain and Mind Centre, University of Sydney, Camperdown, Australia
| | - Hannah C Timmins
- Brain and Mind Centre, University of Sydney, Camperdown, Australia
| | - Matthew C Kiernan
- Brain and Mind Centre, University of Sydney, Camperdown, Australia.,Department of Neurology, Royal Prince Alfred Hospital, Sydney, Australia
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19
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Automatic detection of crohn disease in wireless capsule endoscopic images using a deep convolutional neural network. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04146-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
AbstractThe diagnosis of Crohn’s disease (CD) in the small bowel is generally performed by observing a very large number of images captured by capsule endoscopy (CE). This diagnostic technique entails a heavy workload for the specialists in terms of time spent reviewing the images. This paper presents a convolutional neural network capable of classifying the CE images to identify those ones affected by lesions indicative of the disease. The architecture of the proposed network was custom designed to solve this image classification problem. This allowed different design decisions to be made with the aim of improving its performance in terms of accuracy and processing speed compared to other state-of-the-art deep-learning-based reference architectures. The experimentation was carried out on a set of 15,972 images extracted from 31 CE videos of patients affected by CD, 7,986 of which showed lesions associated with the disease. The training, validation/selection and evaluation of the network was performed on 70%, 10% and 20% of the total images, respectively. The ROC curve obtained on the test image set has an area greater than 0.997, with points in a 95-99% sensitivity range associated with specificities of 99-96%. These figures are higher than those achieved by EfficientNet-B5, VGG-16, Xception or ResNet networks which also require an average processing time per image significantly higher than the one needed in the proposed architecture. Therefore, the network outlined in this paper is proving to be sufficiently promising to be considered for integration into tools used by specialists in their diagnosis of CD. In the sample of images analysed, the network was able to detect 99% of the images with lesions, filtering out for specialist review 96% of those with no signs of disease.
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20
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Gao W, Wang C, Li Q, Zhang X, Yuan J, Li D, Sun Y, Chen Z, Gu Z. Application of medical imaging methods and artificial intelligence in tissue engineering and organ-on-a-chip. Front Bioeng Biotechnol 2022; 10:985692. [PMID: 36172022 PMCID: PMC9511994 DOI: 10.3389/fbioe.2022.985692] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 08/08/2022] [Indexed: 12/02/2022] Open
Abstract
Organ-on-a-chip (OOC) is a new type of biochip technology. Various types of OOC systems have been developed rapidly in the past decade and found important applications in drug screening and precision medicine. However, due to the complexity in the structure of both the chip-body itself and the engineered-tissue inside, the imaging and analysis of OOC have still been a big challenge for biomedical researchers. Considering that medical imaging is moving towards higher spatial and temporal resolution and has more applications in tissue engineering, this paper aims to review medical imaging methods, including CT, micro-CT, MRI, small animal MRI, and OCT, and introduces the application of 3D printing in tissue engineering and OOC in which medical imaging plays an important role. The achievements of medical imaging assisted tissue engineering are reviewed, and the potential applications of medical imaging in organoids and OOC are discussed. Moreover, artificial intelligence - especially deep learning - has demonstrated its excellence in the analysis of medical imaging; we will also present the application of artificial intelligence in the image analysis of 3D tissues, especially for organoids developed in novel OOC systems.
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Affiliation(s)
- Wanying Gao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Chunyan Wang
- State Key Laboratory of Space Medicine Fundamentals and Application, Chinese Astronaut Science Researching and Training Center, Beijing, China
| | - Qiwei Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Xijing Zhang
- Central Research Institute, United Imaging Group, Shanghai, China
| | - Jianmin Yuan
- Central Research Institute, United Imaging Group, Shanghai, China
| | - Dianfu Li
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Sun
- International Children’s Medical Imaging Research Laboratory, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Zaozao Chen
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Zhongze Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
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21
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Region Convolutional Neural Network for Brain Tumor Segmentation. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8335255. [PMID: 36124122 PMCID: PMC9482475 DOI: 10.1155/2022/8335255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/20/2022] [Accepted: 07/22/2022] [Indexed: 11/17/2022]
Abstract
Gliomas are often difficult to find and distinguish using typical manual segmentation approaches because of their vast range of changes in size, shape, and appearance. Furthermore, the manual annotation of cancer tissue segmentation under the close supervision of a human professional is both time-consuming and exhausting to perform. It will be easier and faster in the future to get accurate and quick diagnoses and treatments thanks to automated segmentation and survival rate prediction models that can be used now. In this article, a segmentation model is designed using RCNN that enables automatic prognosis on brain tumors using MRI. The study adopts a U-Net encoder for capturing the features during the training of the model. The feature extraction extracts geometric features for the estimation of tumor size. It is seen that the shape, location, and size of a tumor are significant factors in the estimation of prognosis. The experimental methods are conducted to test the efficacy of the model, and the results of the simulation show that the proposed method achieves a reduced error rate with increased accuracy than other methods.
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22
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Soares DF, Henriques R, Gromicho M, de Carvalho M, Madeira SC. Learning prognostic models using a mixture of biclustering and triclustering: Predicting the need for non-invasive ventilation in Amyotrophic Lateral Sclerosis. J Biomed Inform 2022; 134:104172. [PMID: 36055638 DOI: 10.1016/j.jbi.2022.104172] [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: 10/22/2021] [Revised: 03/31/2022] [Accepted: 08/15/2022] [Indexed: 11/26/2022]
Abstract
Longitudinal cohort studies to study disease progression generally combine temporal features produced under periodic assessments (clinical follow-up) with static features associated with single-time assessments, genetic, psychophysiological, and demographic profiles. Subspace clustering, including biclustering and triclustering stances, enables the discovery of local and discriminative patterns from such multidimensional cohort data. These patterns, highly interpretable, are relevant to identifying groups of patients with similar traits or progression patterns. Despite their potential, their use for improving predictive tasks in clinical domains remains unexplored. In this work, we propose to learn predictive models from static and temporal data using discriminative patterns, obtained via biclustering and triclustering, as features within a state-of-the-art classifier, thus enhancing model interpretation. triCluster is extended to find time-contiguous triclusters in temporal data (temporal patterns) and a biclustering algorithm to discover coherent patterns in static data. The transformed data space, composed of bicluster and tricluster features, capture local and cross-variable associations with discriminative power, yielding unique statistical properties of interest. As a case study, we applied our methodology to follow-up data from Portuguese patients with Amyotrophic Lateral Sclerosis (ALS) to predict the need for non-invasive ventilation (NIV) since the last appointment. The results showed that, in general, our methodology outperformed baseline results using the original features. Furthermore, the bicluster/tricluster-based patterns used by the classifier can be used by clinicians to understand the models by highlighting relevant prognostic patterns.
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Affiliation(s)
- Diogo F Soares
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.
| | - Rui Henriques
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Marta Gromicho
- Instituto de Medicina Molecular, Instituto de Fisiologia, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Mamede de Carvalho
- Instituto de Medicina Molecular, Instituto de Fisiologia, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Sara C Madeira
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.
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23
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Pancotti C, Birolo G, Rollo C, Sanavia T, Di Camillo B, Manera U, Chiò A, Fariselli P. Deep learning methods to predict amyotrophic lateral sclerosis disease progression. Sci Rep 2022; 12:13738. [PMID: 35962027 PMCID: PMC9374680 DOI: 10.1038/s41598-022-17805-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 08/01/2022] [Indexed: 11/10/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a highly complex and heterogeneous neurodegenerative disease that affects motor neurons. Since life expectancy is relatively low, it is essential to promptly understand the course of the disease to better target the patient's treatment. Predictive models for disease progression are thus of great interest. One of the most extensive and well-studied open-access data resources for ALS is the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) repository. In 2015, the DREAM-Phil Bowen ALS Prediction Prize4Life Challenge was held on PRO-ACT data, where competitors were asked to develop machine learning algorithms to predict disease progression measured through the slope of the ALSFRS score between 3 and 12 months. However, although it has already been successfully applied in several studies on ALS patients, to the best of our knowledge deep learning approaches still remain unexplored on the ALSFRS slope prediction in PRO-ACT cohort. Here, we investigate how deep learning models perform in predicting ALS progression using the PRO-ACT data. We developed three models based on different architectures that showed comparable or better performance with respect to the state-of-the-art models, thus representing a valid alternative to predict ALS disease progression.
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Affiliation(s)
- Corrado Pancotti
- Department of Medical Sciences, University of Turin, 10126, Turin, Italy
| | - Giovanni Birolo
- Department of Medical Sciences, University of Turin, 10126, Turin, Italy.
| | - Cesare Rollo
- Department of Medical Sciences, University of Turin, 10126, Turin, Italy
| | - Tiziana Sanavia
- Department of Medical Sciences, University of Turin, 10126, Turin, Italy
| | - Barbara Di Camillo
- Department of Information Engineering, University of Padua, 35131, Padua, Italy
| | - Umberto Manera
- ALS Center, "Rita Levi Montalcini" Department of Neuroscience, University of Turin, 10126, Turin, Italy
| | - Adriano Chiò
- ALS Center, "Rita Levi Montalcini" Department of Neuroscience, University of Turin, 10126, Turin, Italy
| | - Piero Fariselli
- Department of Medical Sciences, University of Turin, 10126, Turin, Italy
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Abstract
AbstractBrain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions. In recent years, deep learning methods have shown promising performance in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results. Considering the remarkable breakthroughs made by state-of-the-art technologies, we provide this survey with a comprehensive study of recently developed deep learning based brain tumor segmentation techniques. More than 150 scientific papers are selected and discussed in this survey, extensively covering technical aspects such as network architecture design, segmentation under imbalanced conditions, and multi-modality processes. We also provide insightful discussions for future development directions.
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25
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Liu M, Amey RC, Backer RA, Simon JP, Forbes CE. Behavioral Studies Using Large-Scale Brain Networks – Methods and Validations. Front Hum Neurosci 2022; 16:875201. [PMID: 35782044 PMCID: PMC9244405 DOI: 10.3389/fnhum.2022.875201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 05/17/2022] [Indexed: 11/13/2022] Open
Abstract
Mapping human behaviors to brain activity has become a key focus in modern cognitive neuroscience. As methods such as functional MRI (fMRI) advance cognitive scientists show an increasing interest in investigating neural activity in terms of functional connectivity and brain networks, rather than activation in a single brain region. Due to the noisy nature of neural activity, determining how behaviors are associated with specific neural signals is not well-established. Previous research has suggested graph theory techniques as a solution. Graph theory provides an opportunity to interpret human behaviors in terms of the topological organization of brain network architecture. Graph theory-based approaches, however, only scratch the surface of what neural connections relate to human behavior. Recently, the development of data-driven methods, e.g., machine learning and deep learning approaches, provide a new perspective to study the relationship between brain networks and human behaviors across the whole brain, expanding upon past literatures. In this review, we sought to revisit these data-driven approaches to facilitate our understanding of neural mechanisms and build models of human behaviors. We start with the popular graph theory approach and then discuss other data-driven approaches such as connectome-based predictive modeling, multivariate pattern analysis, network dynamic modeling, and deep learning techniques that quantify meaningful networks and connectivity related to cognition and behaviors. Importantly, for each topic, we discuss the pros and cons of the methods in addition to providing examples using our own data for each technique to describe how these methods can be applied to real-world neuroimaging data.
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Affiliation(s)
- Mengting Liu
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
- Mengting Liu,
| | - Rachel C. Amey
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
- *Correspondence: Rachel C. Amey,
| | - Robert A. Backer
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
| | - Julia P. Simon
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Chad E. Forbes
- Department of Psychology, Florida Atlantic University, Boca Raton, FL, United States
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Al Zoubi O, Misaki M, Tsuchiyagaito A, Zotev V, White E, Paulus M, Bodurka J. Machine Learning Evidence for Sex Differences Consistently Influences Resting-State Functional Magnetic Resonance Imaging Fluctuations Across Multiple Independently Acquired Data Sets. Brain Connect 2022; 12:348-361. [PMID: 34269609 PMCID: PMC9131354 DOI: 10.1089/brain.2020.0878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background/Introduction: Sex classification using functional connectivity from resting-state functional magnetic resonance imaging (rs-fMRI) has shown promising results. This suggested that sex difference might also be embedded in the blood-oxygen-level-dependent properties such as the amplitude of low-frequency fluctuation (ALFF) and the fraction of ALFF (fALFF). This study comprehensively investigates sex differences using a reliable and explainable machine learning (ML) pipeline. Five independent cohorts of rs-fMRI with over than 5500 samples were used to assess sex classification performance and map the spatial distribution of the important brain regions. Methods: Five rs-fMRI samples were used to extract ALFF and fALFF features from predefined brain parcellations and then were fed into an unbiased and explainable ML pipeline with a wide range of methods. The pipeline comprehensively assessed unbiased performance for within-sample and across-sample validation. In addition, the parcellation effect, classifier selection, scanning length, spatial distribution, reproducibility, and feature importance were analyzed and evaluated thoroughly in the study. Results: The results demonstrated high sex classification accuracies from healthy adults (area under the curve >0.89), while degrading for nonhealthy subjects. Sex classification showed moderate to good intraclass correlation coefficient based on parcellation. Linear classifiers outperform nonlinear classifiers. Sex differences could be detected even with a short rs-fMRI scan (e.g., 2 min). The spatial distribution of important features overlaps with previous results from studies. Discussion: Sex differences are consistent in rs-fMRI and should be considered seriously in any study design, analysis, or interpretation. Features that discriminate males and females were found to be distributed across several different brain regions, suggesting a complex mosaic for sex differences in rs-fMRI. Impact statement The presented study unraveled that sex differences are embedded in the blood-oxygen-level dependent (BOLD) and can be predicted using unbiased and explainable machine learning pipeline. The study revealed that psychiatric disorders and demographics might influence the BOLD signal and interact with the classification of sex. The spatial distribution of the important features presented here supports the notion that the brain is a mosaic of male and female features. The findings emphasize the importance of controlling for sex when conducting brain imaging analysis. In addition, the presented framework can be adapted to classify other variables from resting-state BOLD signals.
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Affiliation(s)
- Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
- Department of Psychiatry, Harvard Medical School/McLean Hospital, Boston, Massachusetts, USA
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | | | - Vadim Zotev
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Evan White
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, USA
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27
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Papaiz F, Dourado MET, Valentim RADM, de Morais AHF, Arrais JP. Machine Learning Solutions Applied to Amyotrophic Lateral Sclerosis Prognosis: A Review. FRONTIERS IN COMPUTER SCIENCE 2022. [DOI: 10.3389/fcomp.2022.869140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The prognosis of Amyotrophic Lateral Sclerosis (ALS), a complex and rare disease, represents a challenging and essential task to better comprehend its progression and improve patients' quality of life. The use of Machine Learning (ML) techniques in healthcare has produced valuable contributions to the prognosis field. This article presents a systematic and critical review of primary studies that used ML applied to the ALS prognosis, searching for databases, relevant predictor biomarkers, the ML algorithms and techniques, and their outcomes. We focused on studies that analyzed biomarkers commonly present in the ALS disease clinical practice, such as demographic, clinical, laboratory, and imaging data. Hence, we investigate studies to provide an overview of solutions that can be applied to develop decision support systems and be used by a higher number of ALS clinical settings. The studies were retrieved from PubMed, Science Direct, IEEEXplore, and Web of Science databases. After completing the searching and screening process, 10 articles were selected to be analyzed and summarized. The studies evaluated and used different ML algorithms, techniques, datasets, sample sizes, biomarkers, and performance metrics. Based on the results, three distinct types of prediction were identified: Disease Progression, Survival Time, and Need for Support. The biomarkers identified as relevant in more than one study were the ALSFRS/ALSFRS-R, disease duration, Forced Vital Capacity, Body Mass Index, age at onset, and Creatinine. In general, the studies presented promissory results that can be applied in developing decision support systems. Besides, we discussed the open challenges, the limitations identified, and future research opportunities.
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28
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Maliske L, Kanske P. The Social Connectome - Moving Toward Complexity in the Study of Brain Networks and Their Interactions in Social Cognitive and Affective Neuroscience. Front Psychiatry 2022; 13:845492. [PMID: 35449570 PMCID: PMC9016142 DOI: 10.3389/fpsyt.2022.845492] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 03/11/2022] [Indexed: 11/13/2022] Open
Abstract
Over the past 150 years of neuroscientific research, the field has undergone a tremendous evolution. Starting out with lesion-based inference of brain function, functional neuroimaging, introduced in the late 1980s, and increasingly fine-grained and sophisticated methods and analyses now allow us to study the live neural correlates of complex behaviors in individuals and multiple agents simultaneously. Classically, brain-behavior coupling has been studied as an association of a specific area in the brain and a certain behavioral outcome. This has been a crucial first step in understanding brain organization. Social cognitive processes, as well as their neural correlates, have typically been regarded and studied as isolated functions and blobs of neural activation. However, as our understanding of the social brain as an inherently dynamic organ grows, research in the field of social neuroscience is slowly undergoing the necessary evolution from studying individual elements to how these elements interact and their embedding within the overall brain architecture. In this article, we review recent studies that investigate the neural representation of social cognition as interacting, complex, and flexible networks. We discuss studies that identify individual brain networks associated with social affect and cognition, interaction of these networks, and their relevance for disorders of social affect and cognition. This perspective on social cognitive neuroscience can highlight how a more fine-grained understanding of complex network (re-)configurations could improve our understanding of social cognitive deficits in mental disorders such as autism spectrum disorder and schizophrenia, thereby providing new impulses for methods of interventions.
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Affiliation(s)
- Lara Maliske
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
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29
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Introduction to Machine Learning in Obstetrics and Gynecology. Obstet Gynecol 2022; 139:669-679. [PMID: 35272300 DOI: 10.1097/aog.0000000000004706] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 11/05/2021] [Indexed: 12/12/2022]
Abstract
In the digital age of the 21st century, we have witnessed an explosion in data matched by remarkable progress in the field of computer science and engineering, with the development of powerful and portable artificial intelligence-powered technologies. At the same time, global connectivity powered by mobile technology has led to an increasing number of connected users and connected devices. In just the past 5 years, the convergence of these technologies in obstetrics and gynecology has resulted in the development of innovative artificial intelligence-powered digital health devices that allow easy and accurate patient risk stratification for an array of conditions spanning early pregnancy, labor and delivery, and care of the newborn. Yet, breakthroughs in artificial intelligence and other new and emerging technologies currently have a slow adoption rate in medicine, despite the availability of large data sets that include individual electronic health records spanning years of care, genomics, and the microbiome. As a result, patient interactions with health care remain burdened by antiquated processes that are inefficient and inconvenient. A few health care institutions have recognized these gaps and, with an influx of venture capital investments, are now making in-roads in medical practice with digital products driven by artificial intelligence algorithms. In this article, we trace the history, applications, and ethical challenges of the artificial intelligence that will be at the forefront of digitally transforming obstetrics and gynecology and medical practice in general.
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30
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Recognition of Human Body Feature Changes in Sports Health Based on Deep Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1736350. [PMID: 35371282 PMCID: PMC8970933 DOI: 10.1155/2022/1736350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/19/2022] [Accepted: 03/05/2022] [Indexed: 11/18/2022]
Abstract
With the rapid development of social economy and the extensive and in-depth development of national fitness activities, national physical fitness monitoring and research work has achieved rapid development. In recent years, the application of deep learning technology has also achieved research breakthroughs in the field of computer vision. How deep learning technology can effectively capture motion information in sample data and use it to realize the recognition and classification of human actions is currently a research hot spot. Today’s popularization of various shooting devices such as mobile phones and portable action cameras has contributed to the vigorous growth of image data. Therefore, through computer vision technology, image data is widely used in practical application scenarios of human feature recognition. This paper proposes a deep learning network based on the recognition of human body feature changes in sports, improves the recognition method, and compares the recognition accuracy with the original method. The experimental results of this paper show that the result of this paper is 1.68% higher than the original recognition method, the accuracy rate of the improved motion history image is increased by 14.8%, and the overall recognition rate is higher. It can be seen from the above experimental results that this method has achieved good results in human body action recognition.
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31
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Thome J, Steinbach R, Grosskreutz J, Durstewitz D, Koppe G. Classification of amyotrophic lateral sclerosis by brain volume, connectivity, and network dynamics. Hum Brain Mapp 2022; 43:681-699. [PMID: 34655259 PMCID: PMC8720197 DOI: 10.1002/hbm.25679] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 09/27/2021] [Indexed: 12/19/2022] Open
Abstract
Emerging studies corroborate the importance of neuroimaging biomarkers and machine learning to improve diagnostic classification of amyotrophic lateral sclerosis (ALS). While most studies focus on structural data, recent studies assessing functional connectivity between brain regions by linear methods highlight the role of brain function. These studies have yet to be combined with brain structure and nonlinear functional features. We investigate the role of linear and nonlinear functional brain features, and the benefit of combining brain structure and function for ALS classification. ALS patients (N = 97) and healthy controls (N = 59) underwent structural and functional resting state magnetic resonance imaging. Based on key hubs of resting state networks, we defined three feature sets comprising brain volume, resting state functional connectivity (rsFC), as well as (nonlinear) resting state dynamics assessed via recurrent neural networks. Unimodal and multimodal random forest classifiers were built to classify ALS. Out-of-sample prediction errors were assessed via five-fold cross-validation. Unimodal classifiers achieved a classification accuracy of 56.35-61.66%. Multimodal classifiers outperformed unimodal classifiers achieving accuracies of 62.85-66.82%. Evaluating the ranking of individual features' importance scores across all classifiers revealed that rsFC features were most dominant in classification. While univariate analyses revealed reduced rsFC in ALS patients, functional features more generally indicated deficits in information integration across resting state brain networks in ALS. The present work undermines that combining brain structure and function provides an additional benefit to diagnostic classification, as indicated by multimodal classifiers, while emphasizing the importance of capturing both linear and nonlinear functional brain properties to identify discriminative biomarkers of ALS.
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Affiliation(s)
- Janine Thome
- Department of Theoretical Neuroscience, Central Institute of Mental Health Mannheim, Medical Faculty MannheimHeidelberg UniversityGermany
- Clinic for Psychiatry and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty MannheimHeidelberg UniversityGermany
| | - Robert Steinbach
- Hans Berger Department of NeurologyJena University HospitalJenaGermany
| | - Julian Grosskreutz
- Precision Neurology, Department of NeurologyUniversity of LuebeckLuebeckGermany
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health Mannheim, Medical Faculty MannheimHeidelberg UniversityGermany
| | - Georgia Koppe
- Department of Theoretical Neuroscience, Central Institute of Mental Health Mannheim, Medical Faculty MannheimHeidelberg UniversityGermany
- Clinic for Psychiatry and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty MannheimHeidelberg UniversityGermany
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32
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Mehrotra R, Agrawal R, Ansari MA. Diagnosis of hypercritical chronic pulmonary disorders using dense convolutional network through chest radiography. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:7625-7649. [PMID: 35125924 PMCID: PMC8798313 DOI: 10.1007/s11042-021-11748-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 08/30/2021] [Accepted: 11/22/2021] [Indexed: 06/14/2023]
Abstract
Lung-related ailments are prevalent all over the world which majorly includes asthma, chronic obstructive pulmonary disease (COPD), tuberculosis, pneumonia, fibrosis, etc. and now COVID-19 is added to this list. Infection of COVID-19 poses respirational complications with other indications like cough, high fever, and pneumonia. WHO had identified cancer in the lungs as a fatal cancer type amongst others and thus, the timely detection of such cancer is pivotal for an individual's health. Since the elementary convolutional neural networks have not performed fairly well in identifying atypical image types hence, we recommend a novel and completely automated framework with a deep learning approach for the recognition and classification of chronic pulmonary disorders (CPD) and COVID-pneumonia using Thoracic or Chest X-Ray (CXR) images. A novel three-step, completely automated, approach is presented that first extracts the region of interest from CXR images for preprocessing, and they are then used to detects infected lungs X-rays from the Normal ones. Thereafter, the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD), which might be utilized in the current scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases. And finally, highlight the regions in the CXR which are indicative of severe chronic pulmonary disorders like COVID-19 and pneumonia. A detailed investigation of various pivotal parameters based on several experimental outcomes are made here. This paper presents an approach that detects the Normal lung X-rays from infected ones and the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders with an utmost accuracy of 96.8%. Several other collective performance measurements validate the superiority of the presented model. The proposed framework shows effective results in classifying lung images into Normal, COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD). This framework can be effectively utilized in this current pandemic scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases.
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Affiliation(s)
- Rajat Mehrotra
- Department of Electrical & Electronics Engineering, GL Bajaj Institute of Technology & Management, Gr. Noida, India
| | - Rajeev Agrawal
- Department of Electronics & Communication Engineering, GL Bajaj Institute of Technology & Management, Gr. Noida, India
| | - M. A. Ansari
- Department of Electrical Engineering, School of Engineering, Gautam Buddha University, Gr. Noida, India
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Bede P, Murad A, Lope J, Li Hi Shing S, Finegan E, Chipika RH, Hardiman O, Chang KM. Phenotypic categorisation of individual subjects with motor neuron disease based on radiological disease burden patterns: A machine-learning approach. J Neurol Sci 2022; 432:120079. [PMID: 34875472 DOI: 10.1016/j.jns.2021.120079] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 11/25/2021] [Accepted: 11/29/2021] [Indexed: 12/20/2022]
Abstract
Motor neuron disease is an umbrella term encompassing a multitude of clinically heterogeneous phenotypes. The early and accurate categorisation of patients is hugely important, as MND phenotypes are associated with markedly different prognoses, progression rates, care needs and benefit from divergent management strategies. The categorisation of patients shortly after symptom onset is challenging, and often lengthy clinical monitoring is needed to assign patients to the appropriate phenotypic subgroup. In this study, a multi-class machine-learning strategy was implemented to classify 300 patients based on their radiological profile into diagnostic labels along the UMN-LMN spectrum. A comprehensive panel of cortical thickness measures, subcortical grey matter variables, and white matter integrity metrics were evaluated in a multilayer perceptron (MLP) model. Additional exploratory analyses were also carried out using discriminant function analyses (DFA). Excellent classification accuracy was achieved for amyotrophic lateral sclerosis in the testing cohort (93.7%) using the MLP model, but poor diagnostic accuracy was detected for primary lateral sclerosis (43.8%) and poliomyelitis survivors (60%). Feature importance analyses highlighted the relevance of white matter diffusivity metrics and the evaluation of cerebellar indices, cingulate measures and thalamic radiation variables to discriminate MND phenotypes. Our data suggest that radiological data from single patients may be meaningfully interpreted if large training data sets are available and the provision of diagnostic probability outcomes may be clinically useful in patients with short symptom duration. The computational interpretation of multimodal radiology datasets herald viable diagnostic, prognostic and clinical trial applications.
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Affiliation(s)
- Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland; Pitié-Salpêtrière University Hospital, Sorbonne University, Paris, France.
| | - Aizuri Murad
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Jasmin Lope
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Eoin Finegan
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Rangariroyashe H Chipika
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Kai Ming Chang
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland; Department of Electronics and Computer Science, University of Southampton, UK
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34
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AIM in Amyotrophic Lateral Sclerosis. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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35
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Iadanza E, Fabbri R, Goretti F, Nardo G, Niccolai E, Bendotti C, Amedei A. Machine learning for analysis of gene expression data in fast- and slow-progressing amyotrophic lateral sclerosis murine models. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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36
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Luca AR, Ursuleanu TF, Gheorghe L, Grigorovici R, Iancu S, Hlusneac M, Grigorovici A. Impact of quality, type and volume of data used by deep learning models in the analysis of medical images. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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Kocar TD, Behler A, Ludolph AC, Müller HP, Kassubek J. Multiparametric Microstructural MRI and Machine Learning Classification Yields High Diagnostic Accuracy in Amyotrophic Lateral Sclerosis: Proof of Concept. Front Neurol 2021; 12:745475. [PMID: 34867726 PMCID: PMC8637840 DOI: 10.3389/fneur.2021.745475] [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: 07/22/2021] [Accepted: 10/21/2021] [Indexed: 01/20/2023] Open
Abstract
The potential of multiparametric quantitative neuroimaging has been extensively discussed as a diagnostic tool in amyotrophic lateral sclerosis (ALS). In the past, the integration of multimodal, quantitative data into a useful diagnostic classifier was a major challenge. With recent advances in the field, machine learning in a data driven approach is a potential solution: neuroimaging biomarkers in ALS are mainly observed in the cerebral microstructure, with diffusion tensor imaging (DTI) and texture analysis as promising approaches. We set out to combine these neuroimaging markers as age-corrected features in a machine learning model with a cohort of 502 subjects, divided into 404 patients with ALS and 98 healthy controls. We calculated a linear support vector classifier (SVC) which is a very robust model and then verified the results with a multilayer perceptron (MLP)/neural network. Both classifiers were able to separate ALS patients from controls with receiver operating characteristic (ROC) curves showing an area under the curve (AUC) of 0.87-0.88 ("good") for the SVC and 0.88-0.91 ("good" to "excellent") for the MLP. Among the coefficients of the SVC, texture data contributed the most to a correct classification. We consider these results as a proof of concept that demonstrated the power of machine learning in the application of multiparametric quantitative neuroimaging data to ALS.
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Affiliation(s)
- Thomas D Kocar
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Anna Behler
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Albert C Ludolph
- Department of Neurology, University of Ulm, Ulm, Germany.,German Center for Neurodegenerative Diseases (DZNE), Ulm, Germany
| | | | - Jan Kassubek
- Department of Neurology, University of Ulm, Ulm, Germany.,German Center for Neurodegenerative Diseases (DZNE), Ulm, Germany
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Tomaz Da Silva L, Esper NB, Ruiz DD, Meneguzzi F, Buchweitz A. Visual Explanation for Identification of the Brain Bases for Developmental Dyslexia on fMRI Data. Front Comput Neurosci 2021; 15:594659. [PMID: 34566613 PMCID: PMC8458961 DOI: 10.3389/fncom.2021.594659] [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: 08/14/2020] [Accepted: 06/08/2021] [Indexed: 11/16/2022] Open
Abstract
Problem: Brain imaging studies of mental health and neurodevelopmental disorders have recently included machine learning approaches to identify patients based solely on their brain activation. The goal is to identify brain-related features that generalize from smaller samples of data to larger ones; in the case of neurodevelopmental disorders, finding these patterns can help understand differences in brain function and development that underpin early signs of risk for developmental dyslexia. The success of machine learning classification algorithms on neurofunctional data has been limited to typically homogeneous data sets of few dozens of participants. More recently, larger brain imaging data sets have allowed for deep learning techniques to classify brain states and clinical groups solely from neurofunctional features. Indeed, deep learning techniques can provide helpful tools for classification in healthcare applications, including classification of structural 3D brain images. The adoption of deep learning approaches allows for incremental improvements in classification performance of larger functional brain imaging data sets, but still lacks diagnostic insights about the underlying brain mechanisms associated with disorders; moreover, a related challenge involves providing more clinically-relevant explanations from the neural features that inform classification. Methods: We target this challenge by leveraging two network visualization techniques in convolutional neural network layers responsible for learning high-level features. Using such techniques, we are able to provide meaningful images for expert-backed insights into the condition being classified. We address this challenge using a dataset that includes children diagnosed with developmental dyslexia, and typical reader children. Results: Our results show accurate classification of developmental dyslexia (94.8%) from the brain imaging alone, while providing automatic visualizations of the features involved that match contemporary neuroscientific knowledge (brain regions involved in the reading process for the dyslexic reader group and brain regions associated with strategic control and attention processes for the typical reader group). Conclusions: Our visual explanations of deep learning models turn the accurate yet opaque conclusions from the models into evidence to the condition being studied.
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Affiliation(s)
- Laura Tomaz Da Silva
- School of Technology, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Nathalia Bianchini Esper
- Graduate School of Medicine, Neurosciences, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil.,BraIns, Brain Institute of Rio Grande do Sul, Porto Alegre, Brazil
| | - Duncan D Ruiz
- School of Technology, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Felipe Meneguzzi
- School of Technology, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Augusto Buchweitz
- BraIns, Brain Institute of Rio Grande do Sul, Porto Alegre, Brazil.,School of Health and Life Sciences, Psychology, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil
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Lin Y, He M. Deep Learning-Based Three-Dimensional Oral Conical Beam Computed Tomography for Diagnosis. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:4676316. [PMID: 34594483 PMCID: PMC8478532 DOI: 10.1155/2021/4676316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 08/31/2021] [Indexed: 11/18/2022]
Abstract
In order to deeply study oral three-dimensional cone beam computed tomography (CBCT), the diagnosis of oral and facial surgical diseases based on deep learning was studied. The utility model related to a deep learning-based classification algorithm for oral neck and facial surgery diseases (deep diagnosis of oral and maxillofacial diseases, referred to as DDOM) is brought out; in this method, the DDOM algorithm proposed for patient classification, lesion segmentation, and tooth segmentation, respectively, can effectively process the three-dimensional oral CBCT data of patients and carry out patient-level classification. The segmentation results show that the proposed segmentation method can effectively segment the independent teeth in CBCT images, and the vertical magnification error of tooth CBCT images is clear. The average magnification rate was 7.4%. By correcting the equation of R value and CBCT image vertical magnification rate, the magnification error of tooth image length could be reduced from 7.4. According to the CBCT image length of teeth, the distance R from tooth center to FOV center, and the vertical magnification of CBCT image, the data closer to the real tooth size can be obtained, in which the magnification error is reduced to 1.0%. Therefore, it is proved that the 3D oral cone beam electronic computer based on deep learning can effectively assist doctors in three aspects: patient diagnosis, lesion localization, and surgical planning.
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Affiliation(s)
- Yangdong Lin
- Tianjin First Central Hospital, Tianjin 300192, China
| | - Miao He
- Tianjin First Central Hospital, Tianjin 300192, China
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40
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Liu Y, Zhang Q, Liu L, Li C, Zhang R, Liu G. The Effect of Deep Learning-Based QSM Magnetic Resonance Imaging on the Subthalamic Nucleus. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:8554182. [PMID: 34567489 PMCID: PMC8457984 DOI: 10.1155/2021/8554182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 09/04/2021] [Indexed: 11/29/2022]
Abstract
In order to study the influence of quantitative magnetic susceptibility mapping (QSM) on them. A 2.5D Attention U-Net Network based on multiple input and multiple output, a method for segmenting RN, SN, and STN regions in high-resolution QSM images is proposed, and deep learning realizes accurate segmentation of deep nuclei in brain QSM images. Experimental results show data first cuts each layer of 0 100 case data, based on the image center, from 384 × 288 to the size of 128 × 128. Image combination: each layer of the image in the layer direction combines with two adjacent images into a 2.5D image, i.e., (It - m It; It + i), where It represents the layer i image. At this time, the size of the image changes from 128 × 128 to 128 × 128 × 3, in which 3 represents three consecutive layers of images. The SNR of SWP I to STN is twice that of SWI. The small deep gray matter nuclei (RN, SN, and STN) in QSM images of the brain and the pancreas with irregular shape and large individual differences in abdominal CT images can be automatically segmented.
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Affiliation(s)
- Yuanqin Liu
- Department of Neurosurgery, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250014, China
| | - Qinglu Zhang
- Department of Special Examination, Shandong Provincial Third Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250031, China
| | - Lingchong Liu
- Department of Medical Imaging Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250014, China
| | - Cuiling Li
- Department of Neurosurgery, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250014, China
| | - Rongwei Zhang
- Department of Neurosurgery, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250014, China
| | - Guangcun Liu
- Department of Neurosurgery, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250014, China
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Li W, Wei Q, Hou Y, Lei D, Ai Y, Qin K, Yang J, Kemp GJ, Shang H, Gong Q. Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis. Transl Neurodegener 2021; 10:35. [PMID: 34511130 PMCID: PMC8436442 DOI: 10.1186/s40035-021-00255-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/01/2021] [Indexed: 02/08/2023] Open
Abstract
OBJECTIVE There is increasing evidence that amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease impacting large-scale brain networks. However, it is still unclear which structural networks are associated with the disease and whether the network connectomics are associated with disease progression. This study was aimed to characterize the network abnormalities in ALS and to identify the network-based biomarkers that predict the ALS baseline progression rate. METHODS Magnetic resonance imaging was performed on 73 patients with sporadic ALS and 100 healthy participants to acquire diffusion-weighted magnetic resonance images and construct white matter (WM) networks using tractography methods. The global and regional network properties were compared between ALS and healthy subjects. The single-subject WM network matrices of patients were used to predict the ALS baseline progression rate using machine learning algorithms. RESULTS Compared with the healthy participants, the patients with ALS showed significantly decreased clustering coefficient Cp (P = 0.0034, t = 2.98), normalized clustering coefficient γ (P = 0.039, t = 2.08), and small-worldness σ (P = 0.038, t = 2.10) at the global network level. The patients also showed decreased regional centralities in motor and non-motor systems including the frontal, temporal and subcortical regions. Using the single-subject structural connection matrix, our classification model could distinguish patients with fast versus slow progression rate with an average accuracy of 85%. CONCLUSION Disruption of the WM structural networks in ALS is indicated by weaker small-worldness and disturbances in regions outside of the motor systems, extending the classical pathophysiological understanding of ALS as a motor disorder. The individual WM structural network matrices of ALS patients are potential neuroimaging biomarkers for the baseline disease progression in clinical practice.
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Affiliation(s)
- Wenbin Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, 610000, China
| | - Qianqian Wei
- Laboratory of Neurodegenerative Disorders, Departments of Neurology, West China Hospital of Sichuan University, Chengdu, 610000, China
| | - Yanbing Hou
- Laboratory of Neurodegenerative Disorders, Departments of Neurology, West China Hospital of Sichuan University, Chengdu, 610000, China
| | - Du Lei
- Department of Psychiatry and Behavioral Neuroscience, Division of Bipolar Disorders Research, University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA
| | - Yuan Ai
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, 610000, China
| | - Kun Qin
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, 610000, China
| | - Jing Yang
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, 610000, China
| | - Graham J Kemp
- Department of Musculoskeletal and Ageing Science and MRC - Versus Arthritis Centre for Integrated Research Into Musculoskeletal Ageing, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK
| | - Huifang Shang
- Laboratory of Neurodegenerative Disorders, Departments of Neurology, West China Hospital of Sichuan University, Chengdu, 610000, China.
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, 610000, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610000, China.
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Cheng B, Zhu B, Pu S. Multi-auxiliary domain transfer learning for diagnosis of MCI conversion. Neurol Sci 2021; 43:1721-1739. [PMID: 34510292 DOI: 10.1007/s10072-021-05568-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 08/14/2021] [Indexed: 01/18/2023]
Abstract
In the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has a higher risk of progression to AD, so the prediction of whether an MCI subject will progress to AD (known as progressive MCI, PMCI) or not (known as stable MCI, SMCI) within a certain period is particularly important in practice. It is known that such a task could benefit from jointly learning-related auxiliary tasks such as differentiating AD from PMCI or PMCI from normal control (NC) in order to take full advantage of their shared commonality. However, few existing methods along this line fully consider the correlations between the target and auxiliary tasks according to the clinical practice of AD pathology for diagnosis. To deal with this problem, in this paper, treating each task domain as a different one, we borrow the idea from transfer learning and propose a novel multi-auxiliary domain transfer learning (MaDTL) method, which explicitly utilizes the correlations between the target domain (task) and multi-auxiliary domains (tasks) according to the clinical practice. Specifically, the proposed MaDTL method incorporates two key modules. The first one is a multi-auxiliary domain transfer-based feature selection (MaDTFS) model, which can select a discriminative feature subset shared by the target domain and the multi-auxiliary domains. In the MaDTFS model, to combine more training data from multi-auxiliary domains and simultaneously suppress the negative effects resulting from the irrelevant parts of multi-auxiliary domains, we proposed a sparse group correlation Lasso that includes a proposed group correlation Lasso penalty (i.e., [Formula: see text]) and a proposed correlation Lasso penalty (i.e., [Formula: see text]). The second module in MaDTL is a multi-auxiliary domain transfer-based classification (MaDTC) model that improves the voting with linear weighting-based ensemble learning. This model extends the constraints of the linear weighting method so that it can simultaneously combine training data from multi-auxiliary domains and achieve a robust classifier by minimizing negative effects from the irrelevant part of multi-auxiliary domains. Experimental results on 409 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with the baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data validate the effectiveness of the proposed method by significantly improving the classification accuracy to 80.37% for the identification of MCI-to-AD conversion, outperforming the state-of-the-art methods.
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Affiliation(s)
- Bo Cheng
- Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing, 404100, China.
- College of Computer Science and Engineering, Chongqing Three Gorges University, Chongqing, 404100, China.
| | - Bingli Zhu
- College of Computer Science and Engineering, Chongqing Three Gorges University, Chongqing, 404100, China
| | - Shuchang Pu
- Department of Logistics Management, Chongqing Three Gorges University, Chongqing, 404100, China
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43
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Javaid A, Shahab O, Adorno W, Fernandes P, May E, Syed S. Machine Learning Predictive Outcomes Modeling in Inflammatory Bowel Diseases. Inflamm Bowel Dis 2021; 28:819-829. [PMID: 34417815 PMCID: PMC9165557 DOI: 10.1093/ibd/izab187] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Indexed: 12/14/2022]
Abstract
There is a rising interest in use of big data approaches to personalize treatment of inflammatory bowel diseases (IBDs) and to predict and prevent outcomes such as disease flares and therapeutic nonresponse. Machine learning (ML) provides an avenue to identify and quantify features across vast quantities of data to produce novel insights in disease management. In this review, we cover current approaches in ML-driven predictive outcomes modeling for IBD and relate how advances in other fields of medicine may be applied to improve future IBD predictive models. Numerous studies have incorporated clinical, laboratory, or omics data to predict significant outcomes in IBD, including hospitalizations, outpatient corticosteroid use, biologic response, and refractory disease after colectomy, among others, with considerable health care dollars saved as a result. Encouraging results in other fields of medicine support efforts to use ML image analysis-including analysis of histopathology, endoscopy, and radiology-to further advance outcome predictions in IBD. Though obstacles to clinical implementation include technical barriers, bias within data sets, and incongruence between limited data sets preventing model validation in larger cohorts, ML-predictive analytics have the potential to transform the clinical management of IBD. Future directions include the development of models that synthesize all aforementioned approaches to produce more robust predictive metrics.
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Affiliation(s)
- Aamir Javaid
- Division of Pediatric Gastroenterology and Hepatology, Department of Pediatrics, University of Virginia, Charlottesville, VA, USA
| | - Omer Shahab
- Division of Gastroenterology and Hepatology, Department of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - William Adorno
- School of Data Science, University of Virginia, Charlottesville, VA, USA
| | - Philip Fernandes
- Division of Pediatric Gastroenterology and Hepatology, Department of Pediatrics, University of Virginia, Charlottesville, VA, USA
| | - Eve May
- Division of Gastroenterology and Hepatology, Department of Pediatrics, Children’s National Hospital, Washington, DC, USA
| | - Sana Syed
- Division of Pediatric Gastroenterology and Hepatology, Department of Pediatrics, University of Virginia, Charlottesville, VA, USA,School of Data Science, University of Virginia, Charlottesville, VA, USA,Address Correspondence to: Sana Syed, MD, MSCR, MSDS, Division of Pediatric Gastroenterology and Hepatology, Department of Pediatrics, University of Virginia, 409 Lane Rd, Room 2035B, Charlottesville, VA, 22908, USA ()
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Chong LC, Gandhi G, Lee JM, Yeo WWY, Choi SB. Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review. Int J Mol Sci 2021; 22:8962. [PMID: 34445667 PMCID: PMC8396480 DOI: 10.3390/ijms22168962] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 01/27/2021] [Indexed: 01/02/2023] Open
Abstract
Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects.
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Affiliation(s)
- Li Chuin Chong
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (L.C.C.); (J.M.L.)
| | - Gayatri Gandhi
- Perdana University Graduate School of Medicine, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (G.G.); (W.W.Y.Y.)
| | - Jian Ming Lee
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (L.C.C.); (J.M.L.)
| | - Wendy Wai Yeng Yeo
- Perdana University Graduate School of Medicine, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (G.G.); (W.W.Y.Y.)
| | - Sy-Bing Choi
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (L.C.C.); (J.M.L.)
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Khan A, Eker A, Chefranov A, Demirel H. White blood cell type identification using multi-layer convolutional features with an extreme-learning machine. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102932] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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46
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Ursuleanu TF, Luca AR, Gheorghe L, Grigorovici R, Iancu S, Hlusneac M, Preda C, Grigorovici A. Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images. Diagnostics (Basel) 2021; 11:1373. [PMID: 34441307 PMCID: PMC8393354 DOI: 10.3390/diagnostics11081373] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 07/25/2021] [Accepted: 07/27/2021] [Indexed: 12/13/2022] Open
Abstract
The need for time and attention, given by the doctor to the patient, due to the increased volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes has encouraged the development of the option to support, constructively and effectively, deep learning models. Deep learning (DL) has experienced an exponential development in recent years, with a major impact on interpretations of the medical image. This has influenced the development, diversification and increase of the quality of scientific data, the development of knowledge construction methods and the improvement of DL models used in medical applications. All research papers focus on description, highlighting, classification of one of the constituent elements of deep learning models (DL), used in the interpretation of medical images and do not provide a unified picture of the importance and impact of each constituent in the performance of DL models. The novelty in our paper consists primarily in the unitary approach, of the constituent elements of DL models, namely, data, tools used by DL architectures or specifically constructed DL architecture combinations and highlighting their "key" features, for completion of tasks in current applications in the interpretation of medical images. The use of "key" characteristics specific to each constituent of DL models and the correct determination of their correlations, may be the subject of future research, with the aim of increasing the performance of DL models in the interpretation of medical images.
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Affiliation(s)
- Tudor Florin Ursuleanu
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department of Surgery VI, “Sf. Spiridon” Hospital, 700111 Iasi, Romania
- Department of Surgery I, Regional Institute of Oncology, 700483 Iasi, Romania
| | - Andreea Roxana Luca
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department Obstetrics and Gynecology, Integrated Ambulatory of Hospital “Sf. Spiridon”, 700106 Iasi, Romania
| | - Liliana Gheorghe
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department of Radiology, “Sf. Spiridon” Hospital, 700111 Iasi, Romania
| | - Roxana Grigorovici
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
| | - Stefan Iancu
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
| | - Maria Hlusneac
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
| | - Cristina Preda
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department of Endocrinology, “Sf. Spiridon” Hospital, 700111 Iasi, Romania
| | - Alexandru Grigorovici
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department of Surgery VI, “Sf. Spiridon” Hospital, 700111 Iasi, Romania
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Shen T, Hou R, Ye X, Li X, Xiong J, Zhang Q, Zhang C, Cai X, Yu W, Zhao J, Fu X. Predicting Malignancy and Invasiveness of Pulmonary Subsolid Nodules on CT Images Using Deep Learning. Front Oncol 2021; 11:700158. [PMID: 34381723 PMCID: PMC8351466 DOI: 10.3389/fonc.2021.700158] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 07/08/2021] [Indexed: 12/28/2022] Open
Abstract
Background To develop and validate a deep learning-based model on CT images for the malignancy and invasiveness prediction of pulmonary subsolid nodules (SSNs). Materials and Methods This study retrospectively collected patients with pulmonary SSNs treated by surgery in our hospital from 2012 to 2018. Postoperative pathology was used as the diagnostic reference standard. Three-dimensional convolutional neural network (3D CNN) models were constructed using preoperative CT images to predict the malignancy and invasiveness of SSNs. Then, an observer reader study conducted by two thoracic radiologists was used to compare with the CNN model. The diagnostic power of the models was evaluated with receiver operating characteristic curve (ROC) analysis. Results A total of 2,614 patients were finally included and randomly divided for training (60.9%), validation (19.1%), and testing (20%). For the benign and malignant classification, the best 3D CNN model achieved a satisfactory AUC of 0.913 (95% CI: 0.885-0.940), sensitivity of 86.1%, and specificity of 83.8% at the optimal decision point, which outperformed all observer readers' performance (AUC: 0.846±0.031). For pre-invasive and invasive classification of malignant SSNs, the 3D CNN also achieved satisfactory AUC of 0.908 (95% CI: 0.877-0.939), sensitivity of 87.4%, and specificity of 80.8%. Conclusion The deep-learning model showed its potential to accurately identify the malignancy and invasiveness of SSNs and thus can help surgeons make treatment decisions.
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Affiliation(s)
- Tianle Shen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Runping Hou
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaodan Ye
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaoyang Li
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Junfeng Xiong
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Qin Zhang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Chenchen Zhang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Xuwei Cai
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Wen Yu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaolong Fu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
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Dam Deformation Interpretation and Prediction Based on a Long Short-Term Memory Model Coupled with an Attention Mechanism. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11146625] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management.
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Deep Learning in Automated Region Proposal and Diagnosis of Chronic Otitis Media Based on Computed Tomography. Ear Hear 2021; 41:669-677. [PMID: 31567561 DOI: 10.1097/aud.0000000000000794] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVES The purpose of this study was to develop a deep-learning framework for the diagnosis of chronic otitis media (COM) based on temporal bone computed tomography (CT) scans. DESIGN A total of 562 COM patients with 672 temporal bone CT scans of both ears were included. The final dataset consisted of 1147 ears, and each of them was assigned with a ground truth label from one of the 3 conditions: normal, chronic suppurative otitis media, and cholesteatoma. A random selection of 85% dataset (n = 975) was used for training and validation. The framework contained two deep-learning networks with distinct functions: a region proposal network for extracting regions of interest from 2-dimensional CT slices; and a classification network for diagnosis of COM based on the extracted regions. The performance of this framework was evaluated on the remaining 15% dataset (n = 172) and compared with that of 6 clinical experts who read the same CT images only. The panel included 2 otologists, 3 otolaryngologists, and 1 radiologist. RESULTS The area under the receiver operating characteristic curve of the artificial intelligence model in classifying COM versus normal was 0.92, with sensitivity (83.3%) and specificity (91.4%) exceeding the averages of clinical experts (81.1% and 88.8%, respectively). In a 3-class classification task, this network had higher overall accuracy (76.7% versus 73.8%), higher recall rates in identifying chronic suppurative otitis media (75% versus 70%) and cholesteatoma (76% versus 53%) cases, and superior consistency in duplicated cases (100% versus 81%) compared with clinical experts. CONCLUSIONS This article presented a deep-learning framework that automatically extracted the region of interest from two-dimensional temporal bone CT slices and made diagnosis of COM. The performance of this model was comparable and, in some cases, superior to that of clinical experts. These results implied a promising prospect for clinical application of artificial intelligence in the diagnosis of COM based on CT images.
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Fernandes F, Barbalho I, Barros D, Valentim R, Teixeira C, Henriques J, Gil P, Dourado Júnior M. Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review. Biomed Eng Online 2021; 20:61. [PMID: 34130692 PMCID: PMC8207575 DOI: 10.1186/s12938-021-00896-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 06/09/2021] [Indexed: 12/11/2022] Open
Abstract
INTRODUCTION The use of machine learning (ML) techniques in healthcare encompasses an emerging concept that envisages vast contributions to the tackling of rare diseases. In this scenario, amyotrophic lateral sclerosis (ALS) involves complexities that are yet not demystified. In ALS, the biomedical signals present themselves as potential biomarkers that, when used in tandem with smart algorithms, can be useful to applications within the context of the disease. METHODS This Systematic Literature Review (SLR) consists of searching for and investigating primary studies that use ML techniques and biomedical signals related to ALS. Following the definition and execution of the SLR protocol, 18 articles met the inclusion, exclusion, and quality assessment criteria, and answered the SLR research questions. DISCUSSIONS Based on the results, we identified three classes of ML applications combined with biomedical signals in the context of ALS: diagnosis (72.22%), communication (22.22%), and survival prediction (5.56%). CONCLUSIONS Distinct algorithmic models and biomedical signals have been reported and present promising approaches, regardless of their classes. In summary, this SLR provides an overview of the primary studies analyzed as well as directions for the construction and evolution of technology-based research within the scope of ALS.
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Affiliation(s)
- Felipe Fernandes
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, RN Brazil
| | - Ingridy Barbalho
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, RN Brazil
| | - Daniele Barros
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, RN Brazil
| | - Ricardo Valentim
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, RN Brazil
| | - César Teixeira
- Department of Informatics Engineering, Univ Coimbra, CISUC-Center for Informatics and Systems of the University of Coimbra, Coimbra, Portugal
| | - Jorge Henriques
- Department of Informatics Engineering, Univ Coimbra, CISUC-Center for Informatics and Systems of the University of Coimbra, Coimbra, Portugal
| | - Paulo Gil
- Department of Informatics Engineering, Univ Coimbra, CISUC-Center for Informatics and Systems of the University of Coimbra, Coimbra, Portugal
| | - Mário Dourado Júnior
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, RN Brazil
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