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Momtazmanesh S, Nowroozi A, Rezaei N. Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review. Rheumatol Ther 2022; 9:1249-1304. [PMID: 35849321 PMCID: PMC9510088 DOI: 10.1007/s40744-022-00475-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 06/24/2022] [Indexed: 11/23/2022] Open
Abstract
Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.
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Affiliation(s)
- Sara Momtazmanesh
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran.,Research Center for Immunodeficiencies, Pediatrics Center of Excellence, Children's Medical Center, Tehran University of Medical Sciences, Dr. Gharib St, Keshavarz Blvd, Tehran, Iran
| | - Ali Nowroozi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Nima Rezaei
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran. .,Research Center for Immunodeficiencies, Pediatrics Center of Excellence, Children's Medical Center, Tehran University of Medical Sciences, Dr. Gharib St, Keshavarz Blvd, Tehran, Iran. .,Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
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Mehrpour O, Saeedi F, Hoyte C, Goss F, Shirazi FM. Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System. BMC Pharmacol Toxicol 2022; 23:49. [PMID: 35831909 PMCID: PMC9281002 DOI: 10.1186/s40360-022-00588-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 06/27/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND With diabetes incidence growing globally and metformin still being the first-line for its treatment, metformin's toxicity and overdose have been increasing. Hence, its mortality rate is increasing. For the first time, we aimed to study the efficacy of machine learning algorithms in predicting the outcome of metformin poisoning using two well-known classification methods, including support vector machine (SVM) and decision tree (DT). METHODS This study is a retrospective cohort study of National Poison Data System (NPDS) data, the largest data repository of poisoning cases in the United States. The SVM and DT algorithms were developed using training and test datasets. We also used precision-recall and ROC curves and Area Under the Curve value (AUC) for model evaluation. RESULTS Our model showed that acidosis, hypoglycemia, electrolyte abnormality, hypotension, elevated anion gap, elevated creatinine, tachycardia, and renal failure are the most important determinants in terms of outcome prediction of metformin poisoning. The average negative predictive value for the decision tree and SVM models was 92.30 and 93.30. The AUC of the ROC curve of the decision tree for major, minor, and moderate outcomes was 0.92, 0.92, and 0.89, respectively. While this figure of SVM model for major, minor, and moderate outcomes was 0.98, 0.90, and 0.82, respectively. CONCLUSIONS In order to predict the prognosis of metformin poisoning, machine learning algorithms might help clinicians in the management and follow-up of metformin poisoning cases.
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Affiliation(s)
- Omid Mehrpour
- Data Science Institute, Southern Methodist University, Dallas, TX, USA. .,Rocky Mountain Poison & Drug Safety, Denver Health and Hospital Authority, Denver, CO, USA.
| | - Farhad Saeedi
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran.,Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
| | - Christopher Hoyte
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran.,University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Foster Goss
- University of Colorado Hospital, Aurora, CO, USA.,Department of Emergency Medicine, University of Colorado Hospital, Aurora, CO, USA
| | - Farshad M Shirazi
- Arizona Poison & Drug Information Center, the University of Arizona, College of Pharmacy and University of Arizona, College of Medicine, Tucson, AZ, USA
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Gruson D, Stankovic S, Macq B, Bernardini S, Gouget B, Homsak E, Dabla P. Artificial intelligence and thyroid disease management. Biochem Med (Zagreb) 2022; 32:020601. [PMID: 35799984 PMCID: PMC9195598 DOI: 10.11613/bm.2022.020601] [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: 01/22/2022] [Accepted: 04/05/2022] [Indexed: 12/07/2022] Open
Abstract
Artificial intelligence (AI) is transforming healthcare and offers new tools in clinical research, personalized medicine, and medical diagnostics. Thyroid function tests represent an important asset for physicians in the diagnosis and monitoring of pathologies. Artificial intelligence tools can clearly assist physicians and specialists in laboratory medicine to optimize test prescription, tests interpretation, decision making, process optimization, and assay design. Our article is reviewing several of these aspects. As thyroid AI models rely on large data sets, which often requires distributed learning from multi-center contributions, this article also briefly discusses this issue.
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Affiliation(s)
- Damien Gruson
- Department of Clinical Biochemistry, Cliniques Universitaires Saint-Luc, Université catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium
| | - Sanja Stankovic
- Center for Medical Biochemistry, University Clinical Center of Serbia, Beograd, Serbia
| | - Benoit Macq
- Institute of Information and Communication Technologies, UCLouvain, Ottignies-Louvain-la-Neuve, Belgium
| | - Sergio Bernardini
- Department of Experimental Medicine, University of Tor Vergata, Rome, Italy
| | - Bernard Gouget
- Healthcare Division Committee, Comité Français d’accréditation, Paris, France
| | - Evgenija Homsak
- Department for Laboratory Diagnostics, University Clinical Center Maribor, Maribor, Slovenia
| | - Pradeep Dabla
- Department of Biochemistry, Pant Institute of Postgraduate Medical Education & Research, Delhi, India
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54
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Stafford IS, Gosink MM, Mossotto E, Ennis S, Hauben M. A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation. Inflamm Bowel Dis 2022; 28:1573-1583. [PMID: 35699597 PMCID: PMC9527612 DOI: 10.1093/ibd/izac115] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Inflammatory bowel disease (IBD) is a gastrointestinal chronic disease with an unpredictable disease course. Computational methods such as machine learning (ML) have the potential to stratify IBD patients for the provision of individualized care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time. METHODS On May 6, 2021, a systematic review was conducted through a search of MEDLINE and Embase databases, with the search structure ("machine learning" OR "artificial intelligence") AND ("Crohn* Disease" OR "Ulcerative Colitis" OR "Inflammatory Bowel Disease"). Exclusion criteria included studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, nonautoimmune disease comorbidity research, and record types that were not primary research. RESULTS Seventy-eight (of 409) records met the inclusion criteria. Random forest methods were most prevalent, and there was an increase in neural networks, mainly applied to imaging data sets. The main applications of ML to clinical tasks were diagnosis (18 of 78), disease course (22 of 78), and disease severity (16 of 78). The median sample size was 263. Clinical and microbiome-related data sets were most popular. Five percent of studies used an external data set after training and testing for additional model validation. DISCUSSION Availability of longitudinal and deep phenotyping data could lead to better modeling. Machine learning pipelines that consider imbalanced data and that feature selection only on training data will generate more generalizable models. Machine learning models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalized medicine for IBD.
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Affiliation(s)
| | | | - Enrico Mossotto
- Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
| | - Sarah Ennis
- Address correspondence to: Sarah Ennis, Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK ()
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Flevaris K, Kontoravdi C. Immunoglobulin G N-glycan Biomarkers for Autoimmune Diseases: Current State and a Glycoinformatics Perspective. Int J Mol Sci 2022; 23:ijms23095180. [PMID: 35563570 PMCID: PMC9100869 DOI: 10.3390/ijms23095180] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/02/2022] [Accepted: 05/04/2022] [Indexed: 02/04/2023] Open
Abstract
The effective treatment of autoimmune disorders can greatly benefit from disease-specific biomarkers that are functionally involved in immune system regulation and can be collected through minimally invasive procedures. In this regard, human serum IgG N-glycans are promising for uncovering disease predisposition and monitoring progression, and for the identification of specific molecular targets for advanced therapies. In particular, the IgG N-glycome in diseased tissues is considered to be disease-dependent; thus, specific glycan structures may be involved in the pathophysiology of autoimmune diseases. This study provides a critical overview of the literature on human IgG N-glycomics, with a focus on the identification of disease-specific glycan alterations. In order to expedite the establishment of clinically-relevant N-glycan biomarkers, the employment of advanced computational tools for the interpretation of clinical data and their relationship with the underlying molecular mechanisms may be critical. Glycoinformatics tools, including artificial intelligence and systems glycobiology approaches, are reviewed for their potential to provide insight into patient stratification and disease etiology. Challenges in the integration of such glycoinformatics approaches in N-glycan biomarker research are critically discussed.
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Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test. Sci Rep 2022; 12:7224. [PMID: 35508670 PMCID: PMC9068780 DOI: 10.1038/s41598-022-11361-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/22/2022] [Indexed: 11/08/2022] Open
Abstract
Recent effective therapies enable most rheumatoid arthritis (RA) patients to achieve remission; however, some patients experience relapse. We aimed to predict relapse in RA patients through machine learning (ML) using data on ultrasound (US) examination and blood test. Overall, 210 patients with RA in remission at baseline were dichotomized into remission (n = 150) and relapse (n = 60) based on the disease activity at 2-year follow-up. Three ML classifiers [Logistic Regression, Random Forest, and extreme gradient boosting (XGBoost)] and data on 73 features (14 US examination data, 54 blood test data, and five data on patient information) at baseline were used for predicting relapse. The best performance was obtained using the XGBoost classifier (area under the receiver operator characteristic curve (AUC) = 0.747), compared with Random Forest and Logistic Regression (AUC = 0.719 and 0.701, respectively). In the XGBoost classifier prediction, ten important features, including wrist/metatarsophalangeal superb microvascular imaging scores, were selected using the recursive feature elimination method. The performance was superior to that predicted by researcher-selected features, which are conventional prognostic markers. These results suggest that ML can provide an accurate prediction of relapse in RA patients, and the use of predictive algorithms may facilitate personalized treatment options.
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Kompa B, Hakim JB, Palepu A, Kompa KG, Smith M, Bain PA, Woloszynek S, Painter JL, Bate A, Beam AL. Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review. Drug Saf 2022; 45:477-491. [PMID: 35579812 PMCID: PMC9883349 DOI: 10.1007/s40264-022-01176-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2022] [Indexed: 02/07/2023]
Abstract
INTRODUCTION Artificial intelligence based on machine learning has made large advancements in many fields of science and medicine but its impact on pharmacovigilance is yet unclear. OBJECTIVE The present study conducted a scoping review of the use of artificial intelligence based on machine learning to understand how it is used for pharmacovigilance tasks, characterize differences with other fields, and identify opportunities to improve pharmacovigilance through the use of machine learning. DESIGN The PubMed, Embase, Web of Science, and IEEE Xplore databases were searched to identify articles pertaining to the use of machine learning in pharmacovigilance published from the year 2000 to September 2021. After manual screening of 7744 abstracts, a total of 393 papers met the inclusion criteria for further analysis. Extraction of key data on study design, data sources, sample size, and machine learning methodology was performed. Studies with the characteristics of good machine learning practice were defined and manual review focused on identifying studies that fulfilled these criteria and results that showed promise. RESULTS The majority of studies (53%) were focused on detecting safety signals using traditional statistical methods. Of the studies that used more recent machine learning methods, 61% used off-the-shelf techniques with minor modifications. Temporal analysis revealed that newer methods such as deep learning have shown increased use in recent years. We found only 42 studies (10%) that reflect current best practices and trends in machine learning. In the subset of 154 papers that focused on data intake and ingestion, 30 (19%) were found to incorporate the same best practices. CONCLUSION Advances from artificial intelligence have yet to fully penetrate pharmacovigilance, although recent studies show signs that this may be changing.
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Affiliation(s)
- Benjamin Kompa
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Joe B Hakim
- Department of Health Sciences and Technology, Harvard-MIT, Cambridge, MA, USA
| | - Anil Palepu
- Department of Health Sciences and Technology, Harvard-MIT, Cambridge, MA, USA
| | | | - Michael Smith
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Paul A Bain
- Countway Library of Medicine, Harvard Medical School, Boston, MA, USA
| | | | | | - Andrew Bate
- GlaxoSmithKline, Brentford, UK
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, University of London, London, UK
- Department of Medicine, NYU Grossman School of Medicine, New York, NY, USA
| | - Andrew L Beam
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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Alves P, Green E, Leavy M, Friedler H, Curhan G, Marci C, Boussios C. Validation of a machine learning approach to estimate expanded disability status scale scores for multiple sclerosis. Mult Scler J Exp Transl Clin 2022; 8:20552173221108635. [PMID: 35755008 PMCID: PMC9228644 DOI: 10.1177/20552173221108635] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 06/06/2022] [Indexed: 11/17/2022] Open
Abstract
Background Disability assessment using the Expanded Disability Status Scale (EDSS) is important to inform treatment decisions and monitor the progression of multiple sclerosis. Yet, EDSS scores are documented infrequently in electronic medical records. Objective To validate a machine learning model to estimate EDSS scores for multiple sclerosis patients using clinical notes from neurologists. Methods A machine learning model was developed to estimate EDSS scores on specific encounter dates using clinical notes from neurologist visits. The OM1 MS Registry data were used to create a training cohort of 2632 encounters and a separate validation cohort of 857 encounters, all with clinician-recorded EDSS scores. Model performance was assessed using the area under the receiver-operating-characteristic curve (AUC), positive predictive value (PPV), and negative predictive value (NPV), calculated using a binarized version of the outcome. The Spearman R and Pearson R values were calculated. The model was then applied to encounters without clinician-recorded EDSS scores in the MS Registry. Results The model had a PPV of 0.85, NPV of 0.85, and AUC of 0.91. The model had a Spearman R value of 0.75 and Pearson R value of 0.74 when evaluating performance using the continuous estimated EDSS and clinician-recorded EDSS scores. Application of the model to eligible encounters resulted in the generation of eEDSS scores for an additional 190,282 encounters from 13,249 patients. Conclusion EDSS scores can be estimated with very good performance using a machine learning model applied to clinical notes, thus increasing the utility of real-world data sources for research purposes.
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Affiliation(s)
| | - Eric Green
- Data Science, OM1, Inc., Boston, MA, USA
| | | | | | | | - Carl Marci
- Mental Health and Neuroscience, OM1, Inc., Boston, MA, USA
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Ahsan MM, Luna SA, Siddique Z. Machine-Learning-Based Disease Diagnosis: A Comprehensive Review. Healthcare (Basel) 2022; 10:541. [PMID: 35327018 PMCID: PMC8950225 DOI: 10.3390/healthcare10030541] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/08/2022] [Accepted: 03/10/2022] [Indexed: 02/06/2023] Open
Abstract
Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area.
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Affiliation(s)
- Md Manjurul Ahsan
- School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Shahana Akter Luna
- Medicine & Surgery, Dhaka Medical College & Hospital, Dhaka 1000, Bangladesh;
| | - Zahed Siddique
- Department of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, OK 73019, USA;
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The Past, Present, and Future in Antinuclear Antibodies (ANA). Diagnostics (Basel) 2022; 12:diagnostics12030647. [PMID: 35328200 PMCID: PMC8946865 DOI: 10.3390/diagnostics12030647] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 02/28/2022] [Accepted: 03/05/2022] [Indexed: 12/21/2022] Open
Abstract
Autoantibodies are a hallmark of autoimmunity and, specifically, antinuclear antibodies (ANAs) are the most relevant autoantibodies present in systemic autoimmune rheumatic diseases (SARDs). Over the years, different methods from LE cell to HEp-2 indirect immunofluorescence (IIF), solid-phase assays (SPAs), and finally multianalyte technologies have been developed to study ANA-associated SARDs. All of them provide complementary information that is important to provide the most clinically valuable information. The identification of new biomarkers together with multianalyte platforms will help close the so-called “seronegative gap” and to correctly classify and diagnose patients with SARDs. Finally, artificial intelligence and machine learning is an area still to be exploited but in a next future will help to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management.
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Sanson B, Stalens C, Guien C, Villa L, Eng C, Rabarimeriarijaona S, Bernard R, Cintas P, Solé G, Tiffreau V, Echaniz-Laguna A, Magot A, Juntas Morales R, Boyer FC, Nadaj-Pakleza A, Jacquin-Piques A, Béroud C, Sacconi S. Convergence of patient- and physician-reported outcomes in the French National Registry of Facioscapulohumeral Dystrophy. Orphanet J Rare Dis 2022; 17:96. [PMID: 35236385 PMCID: PMC8890461 DOI: 10.1186/s13023-021-01793-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 03/30/2021] [Indexed: 11/30/2022] Open
Abstract
Background Facioscapulohumeral muscular dystrophy (FSHD) is among the most prevalent muscular dystrophies and currently has no treatment. Clinical and genetic heterogeneity are the main challenges to a full comprehension of the physiopathological mechanism. Improving our knowledge of FSHD is crucial to the development of future therapeutic trials and standards of care. National FSHD registries have been set up to this end. The French National Registry of FSHD combines a clinical evaluation form (CEF) and a self-report questionnaire (SRQ), filled out by a physician with expertise in neuromuscular dystrophies and by the patient, respectively. Aside from favoring recruitment, our strategy was devised to improve data quality. Indeed, the pairwise comparison of data from 281 patients for 39 items allowed for evaluating data accuracy. Kappa or intra-class coefficient (ICC) values were calculated to determine the correlation between answers provided in both the CEF and SRQ. Results Patients and physicians agreed on a majority of questions common to the SRQ and CEF (24 out of 39). Demographic, diagnosis- and care-related questions were generally answered consistently by the patient and the medical practitioner (kappa or ICC values of most items in these groups were greater than 0.8). Muscle function-related items, i.e. FSHD-specific signs, showed an overall medium to poor correlation between data provided in the two forms; the distribution of agreements in this section was markedly spread out and ranged from poor to good. In particular, there was very little agreement regarding the assessment of facial motricity and the presence of a winged scapula. However, patients and physicians agreed very well on the Vignos and Brooke scores. The report of symptoms not specific to FSHD showed general poor consistency. Conclusions Patient and physician answers are largely concordant when addressing quantitative and objective items. Consequently, we updated collection forms by relying more on patient-reported data where appropriate. We hope the revised forms will reduce data collection time while ensuring the same quality standard. With the advent of artificial intelligence and automated decision-making, high-quality and reliable data are critical to develop top-performing algorithms to improve diagnosis, care, and evaluate the efficiency of upcoming treatments.
Supplementary Information The online version contains supplementary material available at 10.1186/s13023-021-01793-6.
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Affiliation(s)
- Benoît Sanson
- Université Côte d'Azur, Service Système Nerveux Périphérique & Muscle, Centre Hospitalier Universitaire de Nice, Nice, France.
| | | | - Céline Guien
- Aix Marseille Univ, INSERM, MMG, Bioinformatics and Genetics, Marseille, France
| | - Luisa Villa
- Université Côte d'Azur, Service Système Nerveux Périphérique & Muscle, Centre Hospitalier Universitaire de Nice, Nice, France
| | - Catherine Eng
- Medical Affairs Department, AFM-Telethon, Evry, France
| | | | - Rafaëlle Bernard
- APHM, Hôpital Timone Enfants, Laboratoire de Génétique Moléculaire, Marseille, France
| | - Pascal Cintas
- Department of Neurology, Toulouse University Hospital, Toulouse, France
| | - Guilhem Solé
- Centre de Référence des Maladies Neuromusculaires AOC, Hôpital Pellegrin, CHU de Bordeaux, Bordeaux, France
| | - Vincent Tiffreau
- Centre de Référence des Maladies Neuromusculaires, Service de Médecine Physique et de Réadaptation, CHU de Lille, Lille, France
| | - Andoni Echaniz-Laguna
- Department of Neurology, APHP, Bicêtre University Hospital, Le Kremlin-Bicêtre, France.,French National Reference Center for Rare Neuropathies (NNERF), Le Kremlin-Bicêtre, France.,INSERM U1195 and Paris-Saclay University, Le Kremlin-Bicêtre, France
| | - Armelle Magot
- Referral Center for Neuromuscular Diseases Atlantique-Occitanie-Caraïbes, CHU Nantes, Nantes, France
| | | | | | - Aleksandra Nadaj-Pakleza
- Centre de Référence des Maladies Neuromusculaires Atlantique-Occitanie-Caraïbes, FILNEMUS, Service de Neurologie, CHU d'Angers, Angers, France.,Centre de Référence des Maladies Neuromusculaires Nord/Est/Île-de-France, Service de Neurologie, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | | | - Christophe Béroud
- Aix Marseille Univ, INSERM, MMG, Bioinformatics and Genetics, Marseille, France.,APHM, Hôpital Timone Enfants, Laboratoire de Génétique Moléculaire, Marseille, France
| | - Sabrina Sacconi
- Université Côte d'Azur, Service Système Nerveux Périphérique & Muscle, Centre Hospitalier Universitaire de Nice, Nice, France.,Institute for Research on Cancer and Aging of Nice (IRCAN), INSERM U1081, CNRS UMR 7284, Faculté de Médecine, Université Côte d'Azur (UCA), Nice, France
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Shah NN, Wass S, Hajjari J, Heisler AC, Malakooti S, Janus SE, Al-Kindi SG. Proportionate Cardiovascular Mortality in Chronic Inflammatory Disease in Adults in the United States From 1999 to 2019. J Clin Rheumatol 2022; 28:97-103. [PMID: 35067506 DOI: 10.1097/rhu.0000000000001818] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Despite a rising prevalence of chronic inflammatory disease (CID), the recent trends in cardiovascular disease (CVD) mortality of patients with CID is scarce. In this study, we investigated patterns of CVD mortality in systemic lupus erythematosus (SLE), inflammatory bowel disease (IBD), and rheumatoid arthritis (RA) compared with the general population. METHODS We used the 1999 to 2019 multiple causes of death files from the national center for health statistics to analyze patterns and trends of proportionate CVD mortality in CID compared with the general population. RESULTS We analyzed a total of 11,154 CVD deaths in IBD, 58,337 CVD deaths in RA, 6227 CVD deaths in SLE, and 17,826,871 CVD deaths in the general population. Between 1999 and 2019, we found that proportionate CVD mortality decreased significantly in the IBD group (25% to 16%), RA group (34% to 25%), and the general population (41% to 31%), but did not change for the SLE group (15% to 15%). Patients with SLE who died of CVD were approximately 10 years younger compared with CVD decedents with RA, IBD, or general population. The White population had higher proportionate CVD mortality than African American (IBD [19% vs 16%-18%] and SLE [14%-16% vs 12-14%], respectively). CONCLUSIONS This study identifies current trends in CVD mortality in the CID population and elucidates current demographics in CVD mortality in CID. Although proportionate CVD mortality decreased in the general population, and in patients with RA and IBD, there was no change among patients with SLE. Further studies are needed to elucidate these differences.
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Affiliation(s)
| | | | | | - Andrew C Heisler
- Department of Rheumatology, Bronson Rheumatology Specialists, Kalamazoo, MI
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Marques KF, Marques AF, Lopes MA, Beraldo RF, Lima TB, Sassaki LY. Artificial intelligence in colorectal cancer screening in patients with inflammatory bowel disease. Artif Intell Gastrointest Endosc 2022; 3:1-8. [DOI: 10.37126/aige.v3.i1.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 02/14/2022] [Accepted: 02/24/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a branch of computer science that develops intelligent machines. In recent years, medicine has been contemplated with this recent modality to aid in the diagnosis of diseases in several specialties, including gastroenterology and gastrointestinal endoscopy. This new technology has superior ability to perform tasks mimicking human behavior and can identify possible pathological alterations, such as pre-malignant lesions and dysplasia, precursor lesions of colorectal cancer (CRC), and support medical decision-making. CRC is among the three most prevalent cancer types, and the second most common cause of cancer-related deaths worldwide; in addition, it is a leading cause of death in patients with inflammatory bowel disease (IBD). Patients with IBD tend to have greater inflammatory cell activity in the intestinal mucosa, which can favor cell proliferation and CRC development. AI can contribute to the detection of pre-neoplastic lesions in patients at risk of CRC development, such as those with extensive IBD or when additional CRC risk factors, such as smoking, are present. In fact, AI systems could improve all aspects of care related to both the detection of pre-malignant and malignant lesions and the screening of patients with IBD. In this review, we aimed to show the benefits and innovations of AI in the screening of CRC in patients with IBD. The promising applications of AI have the potential to revolutionize clinical practice and gastrointestinal endoscopy, especially in at-risk patients, such as those with IBD.
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Affiliation(s)
- Kêmily Fuentes Marques
- Curso de Medicina, Faculdades de Dracena, Fundação Dracenense de Educação e Cultura, Rua Bahia, 332, Dracena, SP, 17900-000, São Paulo, Brasil
| | - Alana Fuentes Marques
- Curso de Medicina, Faculdades de Dracena, Fundação Dracenense de Educação e Cultura, Rua Bahia, 332, Dracena, SP, 17900-000, São Paulo, Brasil
| | - Marina Amorim Lopes
- Department of Internal Medicine, São Paulo State University, Medical School, Botucatu, Brazil
| | - Rodrigo Fedatto Beraldo
- Curso de Medicina, Faculdades de Dracena, Fundação Dracenense de Educação e Cultura, Rua Bahia, 332, Dracena, SP, 17900-000, São Paulo, Brasil
- Department of Internal Medicine, São Paulo State University, Medical School, Botucatu, Brazil
| | - Talles Bazeia Lima
- Department of Internal Medicine, São Paulo State University, Medical School, Botucatu, Brazil
| | - Ligia Yukie Sassaki
- Department of Internal Medicine, São Paulo State University, Medical School, Botucatu, Brazil
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64
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AIM in Haematology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_182] [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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65
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Lim ACY, Natarajan P, Fonseka RD, Maharaj M, Mobbs RJ. The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature. Digit Health 2022; 8:20552076221074128. [PMID: 35111331 PMCID: PMC8801637 DOI: 10.1177/20552076221074128] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 12/27/2021] [Indexed: 12/11/2022] Open
Abstract
Background The purpose of this scoping review was to explore the current applications of objective gait analysis using inertial measurement units, custom algorithms and artificial intelligence algorithms in detecting neurological and musculoskeletal gait altering pathologies from healthy gait patterns. Methods Literature searches were conducted of four electronic databases (Medline, PubMed, Embase and Web of Science) to identify studies that assessed the accuracy of these custom gait analysis models with inputs derived from wearable devices. Data was collected according to the preferred reporting items for systematic reviews and meta-analysis statement guidelines. Results A total of 23 eligible studies were identified for inclusion in the present review, including 10 custom algorithms articles and 13 artificial intelligence algorithms articles. Nine studies evaluated patients with Parkinson’s disease of varying severity and subtypes. Support vector machine was the commonest adopted artificial intelligence algorithm model, followed by random forest and neural networks. Overall classification accuracy was promising for articles that use artificial intelligence algorithms, with nine articles achieving more than 90% accuracy. Conclusions Current applications of artificial intelligence algorithms are reasonably effective discrimination between pathological and non-pathological gait. Of these, machine learning algorithms demonstrate the additional capacity to handle complicated data input, when compared to other custom algorithms. Notably, there has been increasing application of machine learning algorithms for conducting gait analysis. More studies are needed with unsupervised methods and in non-clinical settings to better reflect the community and home-based usage.
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Affiliation(s)
- Ashley Cha Yin Lim
- NeuroSpine Surgery Research Group (NSURG), Australia.,Faculty of Health and Medicine, The University of Newcastle, Australia
| | - Pragadesh Natarajan
- NeuroSpine Surgery Research Group (NSURG), Australia.,Neuro Spine Clinic, Prince of Wales Private Hospital, Australia.,Faculty of Medicine, University of New South Wales (UNSW), Australia
| | - R Dineth Fonseka
- NeuroSpine Surgery Research Group (NSURG), Australia.,Neuro Spine Clinic, Prince of Wales Private Hospital, Australia.,Faculty of Medicine, University of New South Wales (UNSW), Australia
| | - Monish Maharaj
- NeuroSpine Surgery Research Group (NSURG), Australia.,Neuro Spine Clinic, Prince of Wales Private Hospital, Australia.,Faculty of Medicine, University of New South Wales (UNSW), Australia
| | - Ralph J Mobbs
- NeuroSpine Surgery Research Group (NSURG), Australia.,Neuro Spine Clinic, Prince of Wales Private Hospital, Australia.,Faculty of Medicine, University of New South Wales (UNSW), Australia
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66
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Jiang Z, Shao M, Dai X, Pan Z, Liu D. Identification of Diagnostic Biomarkers in Systemic Lupus Erythematosus Based on Bioinformatics Analysis and Machine Learning. Front Genet 2022; 13:865559. [PMID: 35495164 PMCID: PMC9047905 DOI: 10.3389/fgene.2022.865559] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 04/01/2022] [Indexed: 02/05/2023] Open
Abstract
Systemic lupus erythematosus (SLE) is a complex autoimmune disease that affects several organs and causes variable clinical symptoms. Exploring new insights on genetic factors may help reveal SLE etiology and improve the survival of SLE patients. The current study is designed to identify key genes involved in SLE and develop potential diagnostic biomarkers for SLE in clinical practice. Expression data of all genes of SLE and control samples in GSE65391 and GSE72509 datasets were downloaded from the Gene Expression Omnibus (GEO) database. A total of 11 accurate differentially expressed genes (DEGs) were identified by the "limma" and "RobustRankAggreg" R package. All these genes were functionally associated with several immune-related biological processes and a single KEGG (Kyoto Encyclopedia of Genes and Genome) pathway of necroptosis. The PPI analysis showed that IFI44, IFI44L, EIF2AK2, IFIT3, IFITM3, ZBP1, TRIM22, PRIC285, XAF1, and PARP9 could interact with each other. In addition, the expression patterns of these DEGs were found to be consistent in GSE39088. Moreover, Receiver operating characteristic (ROC) curves analysis indicated that all these DEGs could serve as potential diagnostic biomarkers according to the area under the ROC curve (AUC) values. Furthermore, we constructed the transcription factor (TF)-diagnostic biomarker-microRNA (miRNA) network composed of 278 nodes and 405 edges, and a drug-diagnostic biomarker network consisting of 218 nodes and 459 edges. To investigate the relationship between diagnostic biomarkers and the immune system, we evaluated the immune infiltration landscape of SLE and control samples from GSE6539. Finally, using a variety of machine learning methods, IFI44 was determined to be the optimal diagnostic biomarker of SLE and then verified by quantitative real-time PCR (qRT-PCR) in an independent cohort. Our findings may benefit the diagnosis of patients with SLE and guide in developing novel targeted therapy in treating SLE patients.
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Affiliation(s)
- Zhihang Jiang
- Department of Rheumatology and Immunology, Shengjing Hospital, China Medical University, Shenyang, China
| | - Mengting Shao
- Computational Systems Biology Laboratory, Department of Bioinformatics, Shantou University Medical College (SUMC), Shantou, China
| | - Xinzhu Dai
- Department of Rheumatology and Immunology, Shengjing Hospital, China Medical University, Shenyang, China
| | - Zhixin Pan
- Department of Rheumatology and Immunology, Shengjing Hospital, China Medical University, Shenyang, China
| | - Dongmei Liu
- Department of Rheumatology and Immunology, Shengjing Hospital, China Medical University, Shenyang, China
- *Correspondence: Dongmei Liu,
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67
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AI and Immunoinformatics. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_113] [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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68
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Saurabh R, Fouodo CJK, König IR, Busch H, Wohlers I. A survey of genome-wide association studies, polygenic scores and UK Biobank highlights resources for autoimmune disease genetics. Front Immunol 2022; 13:972107. [PMID: 35990650 PMCID: PMC9388859 DOI: 10.3389/fimmu.2022.972107] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 07/12/2022] [Indexed: 12/04/2022] Open
Abstract
Autoimmune diseases share a general mechanism of auto-antigens harming tissues. Still. they are phenotypically diverse, with genetic as well as environmental factors contributing to their etiology at varying degrees. Associated genomic loci and variants have been identified in numerous genome-wide association studies (GWAS), whose results are increasingly used for polygenic scores (PGS) that are used to predict disease risk. At the same time, a technological shift from genotyping arrays to next generation sequencing (NGS) is ongoing. NGS allows the identification of virtually all - including rare - genetic variants, which in combination with methodological developments promises to improve the prediction of disease risk and elucidate molecular mechanisms underlying disease. Here we review current, publicly available autoimmune disease GWAS and PGS data based on information from the GWAS and PGS catalog, respectively. We summarize autoimmune diseases investigated, respective studies conducted and their results. Further, we review genetic data and autoimmune disease patients in the UK Biobank (UKB), the largest resource for genetic and phenotypic data available for academic research. We find that only comparably prevalent autoimmune diseases are covered by the UKB and at the same time assessed by both GWAS and PGS catalogs. These are systemic (systemic lupus erythematosus) as well as organ-specific, affecting the gastrointestinal tract (inflammatory bowel disease as well as specifically Crohn's disease and ulcerative colitis), joints (juvenile ideopathic arthritis, psoriatic arthritis, rheumatoid arthritis, ankylosing spondylitis), glands (Sjögren syndrome), the nervous system (multiple sclerosis), and the skin (vitiligo).
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Affiliation(s)
- Rochi Saurabh
- Medical Systems Biology, Lübeck Institute for Experimental Dermatology (LIED) and Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany
| | - Césaire J K Fouodo
- Institute of Medical Biometry and Statistics (IMBS), University of Lübeck, Lübeck, Germany
| | - Inke R König
- Institute of Medical Biometry and Statistics (IMBS), University of Lübeck, Lübeck, Germany
| | - Hauke Busch
- Medical Systems Biology, Lübeck Institute for Experimental Dermatology (LIED) and Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany
| | - Inken Wohlers
- Medical Systems Biology, Lübeck Institute for Experimental Dermatology (LIED) and Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany
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69
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OUP accepted manuscript. Rheumatology (Oxford) 2022; 61:4175-4186. [DOI: 10.1093/rheumatology/keac032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 01/11/2022] [Indexed: 11/12/2022] Open
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70
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Adegboro CO, Choudhury A, Asan O, Kelly MM. Artificial Intelligence to Improve Health Outcomes in the NICU and PICU: A Systematic Review. Hosp Pediatr 2022; 12:93-110. [PMID: 34890453 DOI: 10.1542/hpeds.2021-006094] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
CONTEXT Artificial intelligence (AI) technologies are increasingly used in pediatrics and have the potential to help inpatient physicians provide high-quality care for critically ill children. OBJECTIVE We aimed to describe the use of AI to improve any health outcome(s) in neonatal and pediatric intensive care. DATA SOURCE PubMed, IEEE Xplore, Cochrane, and Web of Science databases. STUDY SELECTION We used peer-reviewed studies published between June 1, 2010, and May 31, 2020, in which researchers described (1) AI, (2) pediatrics, and (3) intensive care. Studies were included if researchers assessed AI use to improve at least 1 health outcome (eg, mortality). DATA EXTRACTION Data extraction was conducted independently by 2 researchers. Articles were categorized by direct or indirect impact of AI, defined by the European Institute of Innovation and Technology Health joint report. RESULTS Of the 287 publications screened, 32 met inclusion criteria. Approximately 22% (n = 7) of studies revealed a direct impact and improvement in health outcomes after AI implementation. Majority were in prototype testing, and few were deployed into an ICU setting. Among the remaining 78% (n = 25) AI models outperformed standard clinical modalities and may have indirectly influenced patient outcomes. Quantitative assessment of health outcomes using statistical measures, such as area under the receiver operating curve (56%; n = 18) and specificity (38%; n = 12), revealed marked heterogeneity in metrics and standardization. CONCLUSIONS Few studies have revealed that AI has directly improved health outcomes for pediatric critical care patients. Further prospective, experimental studies are needed to assess AI's impact by using established implementation frameworks, standardized metrics, and validated outcome measures.
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Affiliation(s)
- Claudette O Adegboro
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Avishek Choudhury
- Division of Engineering Management, School of Systems and Enterprise, Stevens Institute of Technology, Hoboken, New Jersey
| | - Onur Asan
- Division of Engineering Management, School of Systems and Enterprise, Stevens Institute of Technology, Hoboken, New Jersey
| | - Michelle M Kelly
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
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Pettit RW, Fullem R, Cheng C, Amos CI. Artificial intelligence, machine learning, and deep learning for clinical outcome prediction. Emerg Top Life Sci 2021; 5:ETLS20210246. [PMID: 34927670 PMCID: PMC8786279 DOI: 10.1042/etls20210246] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/03/2021] [Accepted: 12/07/2021] [Indexed: 12/12/2022]
Abstract
AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.
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Affiliation(s)
- Rowland W. Pettit
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
| | - Robert Fullem
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, U.S.A
| | - Chao Cheng
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, U.S.A
| | - Christopher I. Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, U.S.A
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, U.S.A
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Desvaux E, Aussy A, Hubert S, Keime-Guibert F, Blesius A, Soret P, Guedj M, Pers JO, Laigle L, Moingeon P. Model-based computational precision medicine to develop combination therapies for autoimmune diseases. Expert Rev Clin Immunol 2021; 18:47-56. [PMID: 34842494 DOI: 10.1080/1744666x.2022.2012452] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
INTRODUCTION The complex pathophysiology of autoimmune diseases (AIDs) is being progressively deciphered, providing evidence for a multiplicity of pro-inflammatory pathways underlying heterogeneous clinical phenotypes and disease evolution. AREAS COVERED Treatment strategies involving drug combinations are emerging as a preferred option to achieve remission in a vast majority of patients affected by systemic AIDs. The design of appropriate drug combinations can benefit from AID modeling following a comprehensive multi-omics molecular profiling of patients combined with Artificial Intelligence (AI)-powered computational analyses. Such disease models support patient stratification in homogeneous subgroups, shed light on dysregulated pro-inflammatory pathways and yield hypotheses regarding potential therapeutic targets and candidate biomarkers to stratify and monitor patients during treatment. AID models inform the rational design of combination therapies interfering with independent pro-inflammatory pathways related to either one of five prominent immune compartments contributing to the pathophysiology of AIDs, i.e. pro-inflammatory signals originating from tissues, innate immune mechanisms, T lymphocyte activation, autoantibodies and B cell activation, as well as soluble mediators involved in immune cross-talk. EXPERT OPINION The optimal management of AIDs in the future will rely upon rationally designed combination therapies, as a modality of a model-based Computational Precision Medicine taking into account the patients' biological and clinical specificities.
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Affiliation(s)
- Emiko Desvaux
- Servier, Research and Development, Suresnes Cedex, France.,U1227 -Laboratoire d'Immunologie, Univ Brest, CHRU Morvan, Brest Cedex, France
| | - Audrey Aussy
- Servier, Research and Development, Suresnes Cedex, France
| | - Sandra Hubert
- Servier, Research and Development, Suresnes Cedex, France
| | | | - Alexia Blesius
- Servier, Research and Development, Suresnes Cedex, France
| | - Perrine Soret
- Servier, Research and Development, Suresnes Cedex, France
| | - Mickaël Guedj
- Servier, Research and Development, Suresnes Cedex, France
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Chung CW, Hsiao TH, Huang CJ, Chen YJ, Chen HH, Lin CH, Chou SC, Chen TS, Chung YF, Yang HI, Chen YM. Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus. BioData Min 2021; 14:52. [PMID: 34895289 PMCID: PMC8666017 DOI: 10.1186/s13040-021-00284-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 11/21/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Rheumatoid arthritis (RA) and systemic lupus erythematous (SLE) are autoimmune rheumatic diseases that share a complex genetic background and common clinical features. This study's purpose was to construct machine learning (ML) models for the genomic prediction of RA and SLE. METHODS A total of 2,094 patients with RA and 2,190 patients with SLE were enrolled from the Taichung Veterans General Hospital cohort of the Taiwan Precision Medicine Initiative. Genome-wide single nucleotide polymorphism (SNP) data were obtained using Taiwan Biobank version 2 array. The ML methods used were logistic regression (LR), random forest (RF), support vector machine (SVM), gradient tree boosting (GTB), and extreme gradient boosting (XGB). SHapley Additive exPlanation (SHAP) values were calculated to clarify the contribution of each SNPs. Human leukocyte antigen (HLA) imputation was performed using the HLA Genotype Imputation with Attribute Bagging package. RESULTS Compared with LR (area under the curve [AUC] = 0.8247), the RF approach (AUC = 0.9844), SVM (AUC = 0.9828), GTB (AUC = 0.9932), and XGB (AUC = 0.9919) exhibited significantly better prediction performance. The top 20 genes by feature importance and SHAP values included HLA class II alleles. We found that imputed HLA-DQA1*05:01, DQB1*0201 and DRB1*0301 were associated with SLE; HLA-DQA1*03:03, DQB1*0401, DRB1*0405 were more frequently observed in patients with RA. CONCLUSIONS We established ML methods for genomic prediction of RA and SLE. Genetic variations at HLA-DQA1, HLA-DQB1, and HLA-DRB1 were crucial for differentiating RA from SLE. Future studies are required to verify our results and explore their mechanistic explanation.
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Affiliation(s)
- Chih-Wei Chung
- Department of Information Management, National Taiwan University, Taipei, Taiwan
| | - Tzu-Hung Hsiao
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Chih-Jen Huang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Yen-Ju Chen
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
- Division of Allergy, Immunology and Rheumatology, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Hsin-Hua Chen
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
- Division of Allergy, Immunology and Rheumatology, Taichung Veterans General Hospital, Taichung, Taiwan
- Rong Hsing Research Center for Translational Medicine & Ph.D. Program in Translational Medicine, National Chung Hsing University, Taichung, Taiwan
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ching-Heng Lin
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Seng-Cho Chou
- Department of Information Management, National Taiwan University, Taipei, Taiwan
| | - Tzer-Shyong Chen
- Department of Information Management, Tunghai University, Taichung, Taiwan
| | - Yu-Fang Chung
- Department of Electrical Engineering, Tunghai University, Taichung, Taiwan
| | - Hwai-I Yang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Yi-Ming Chen
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.
- Division of Allergy, Immunology and Rheumatology, Taichung Veterans General Hospital, Taichung, Taiwan.
- Rong Hsing Research Center for Translational Medicine & Ph.D. Program in Translational Medicine, National Chung Hsing University, Taichung, Taiwan.
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- College of Medicine, National Chung Hsing University, 40227, Taichung City, Taiwan.
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Kedra J, Davergne T, Braithwaite B, Servy H, Gossec L. Machine learning approaches to improve disease management of patients with rheumatoid arthritis: review and future directions. Expert Rev Clin Immunol 2021; 17:1311-1321. [PMID: 34890271 DOI: 10.1080/1744666x.2022.2017773] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Although the management of rheumatoid arthritis (RA) has improved in major way over the last decades, this disease still leads to an important burden for patients and society, and there is a need to develop more personalized approaches. Machine learning (ML) methods are more and more used in health-related studies and can be applied to different sorts of data (clinical, radiological, or 'omics' data). Such approaches may improve the management of patients with RA. AREAS COVERED In this paper, we propose a review regarding ML approaches applied to RA. A scoping literature search was performed in PubMed, in September 2021 using the following MeSH terms: 'arthritis, rheumatoid' and 'machine learning'. Based on this search, the usefulness of ML methods for RA diagnosis, monitoring, and prediction of response to treatment and RA outcomes, is discussed. EXPERT OPINION ML methods have the potential to revolutionize RA-related research and improve disease management and patient care. Nevertheless, these models are not yet ready to contribute fully to rheumatologists' daily practice. Indeed, these methods raise technical, methodological, and ethical issues, which should be addressed properly to allow their implementation. Collaboration between data scientists, clinical researchers, and physicians is therefore required to move this field forward.
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Affiliation(s)
- Joanna Kedra
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France.,Rheumatology Department, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
| | - Thomas Davergne
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France
| | | | | | - Laure Gossec
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France.,Rheumatology Department, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
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Garner AJ, Saatchi R, Ward O, Hawley DP. Juvenile Idiopathic Arthritis: A Review of Novel Diagnostic and Monitoring Technologies. Healthcare (Basel) 2021; 9:1683. [PMID: 34946409 PMCID: PMC8700900 DOI: 10.3390/healthcare9121683] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 12/29/2022] Open
Abstract
Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease of childhood and is characterized by an often insidious onset and a chronic relapsing-remitting course, once diagnosed. With successive flares of joint inflammation, joint damage accrues, often associated with pain and functional disability. The progressive nature and potential for chronic damage and disability caused by JIA emphasizes the critical need for a prompt and accurate diagnosis. This article provides a review of recent studies related to diagnosis, monitoring and management of JIA and outlines recent novel tools and techniques (infrared thermal imaging, three-dimensional imaging, accelerometry, artificial neural networks and fuzzy logic) which have demonstrated potential value in assessment and monitoring of JIA. The emergence of novel techniques to assist clinicians' assessments for diagnosis and monitoring of JIA has demonstrated promise; however, further research is required to confirm their clinical utility.
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Affiliation(s)
- Amelia J. Garner
- The Medical School, University of Sheffield, Sheffield S10 2TN, UK
| | - Reza Saatchi
- Industry and Innovation Research Institute, Sheffield Hallam University, Sheffield S1 1WB, UK;
| | - Oliver Ward
- Department of Paediatric Rheumatology, Sheffield Children’s Hospital, Sheffield S10 2TH, UK; (O.W.); (D.P.H.)
| | - Daniel P. Hawley
- Department of Paediatric Rheumatology, Sheffield Children’s Hospital, Sheffield S10 2TH, UK; (O.W.); (D.P.H.)
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76
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Al-Harazi O, Kaya IH, El Allali A, Colak D. A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer. Front Genet 2021; 12:721949. [PMID: 34790220 PMCID: PMC8591094 DOI: 10.3389/fgene.2021.721949] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 09/28/2021] [Indexed: 12/30/2022] Open
Abstract
The development of reliable methods for identification of robust biomarkers for complex diseases is critical for disease diagnosis and prognosis efforts. Integrating multi-omics data with protein-protein interaction (PPI) networks to investigate diseases may help better understand disease characteristics at the molecular level. In this study, we developed and tested a novel network-based method to detect subnetwork markers for patients with colorectal cancer (CRC). We performed an integrated omics analysis using whole-genome gene expression profiling and copy number alterations (CNAs) datasets followed by building a gene interaction network for the significantly altered genes. We then clustered the constructed gene network into subnetworks and assigned a score for each significant subnetwork. We developed a support vector machine (SVM) classifier using these scores as feature values and tested the methodology in independent CRC transcriptomic datasets. The network analysis resulted in 15 subnetwork markers that revealed several hub genes that may play a significant role in colorectal cancer, including PTP4A3, FGFR2, PTX3, AURKA, FEN1, INHBA, and YES1. The 15-subnetwork classifier displayed over 98 percent accuracy in detecting patients with CRC. In comparison to individual gene biomarkers, subnetwork markers based on integrated multi-omics and network analyses may lead to better disease classification, diagnosis, and prognosis.
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Affiliation(s)
- Olfat Al-Harazi
- Biostatistics, Epidemiology and Scientific Computing Department, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Ibrahim H Kaya
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Achraf El Allali
- African Genome Center, Mohammed VI Polytechnic University, Benguerir, Morocco
| | - Dilek Colak
- Biostatistics, Epidemiology and Scientific Computing Department, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
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Kingsmore KM, Puglisi CE, Grammer AC, Lipsky PE. An introduction to machine learning and analysis of its use in rheumatic diseases. Nat Rev Rheumatol 2021; 17:710-730. [PMID: 34728818 DOI: 10.1038/s41584-021-00708-w] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/04/2021] [Indexed: 02/07/2023]
Abstract
Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.
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Affiliation(s)
| | | | - Amrie C Grammer
- AMPEL BioSolutions and RILITE Research Institute, Charlottesville, VA, USA
| | - Peter E Lipsky
- AMPEL BioSolutions and RILITE Research Institute, Charlottesville, VA, USA
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Clinical impact and quality of randomized controlled trials involving interventions evaluating artificial intelligence prediction tools: a systematic review. NPJ Digit Med 2021; 4:154. [PMID: 34711955 PMCID: PMC8553754 DOI: 10.1038/s41746-021-00524-2] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 09/30/2021] [Indexed: 12/23/2022] Open
Abstract
The evidence of the impact of traditional statistical (TS) and artificial intelligence (AI) tool interventions in clinical practice was limited. This study aimed to investigate the clinical impact and quality of randomized controlled trials (RCTs) involving interventions evaluating TS, machine learning (ML), and deep learning (DL) prediction tools. A systematic review on PubMed was conducted to identify RCTs involving TS/ML/DL tool interventions in the past decade. A total of 65 RCTs from 26,082 records were included. A majority of them had model development studies and generally good performance was achieved. The function of TS and ML tools in the RCTs mainly included assistive treatment decisions, assistive diagnosis, and risk stratification, but DL trials were only conducted for assistive diagnosis. Nearly two-fifths of the trial interventions showed no clinical benefit compared to standard care. Though DL and ML interventions achieved higher rates of positive results than TS in the RCTs, in trials with low risk of bias (17/65) the advantage of DL to TS was reduced while the advantage of ML to TS disappeared. The current applications of DL were not yet fully spread performed in medicine. It is predictable that DL will integrate more complex clinical problems than ML and TS tools in the future. Therefore, rigorous studies are required before the clinical application of these tools.
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Hügle T, Kalweit M. [Artificial intelligence-supported treatment in rheumatology : Principles, current situation and perspectives]. Z Rheumatol 2021; 80:914-927. [PMID: 34618208 PMCID: PMC8651581 DOI: 10.1007/s00393-021-01096-y] [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] [Accepted: 06/21/2021] [Indexed: 11/02/2022]
Abstract
Computer-guided clinical decision support systems have been finding their way into practice for some time, mostly integrated into electronic medical records. The primary goals are to improve the quality of treatment, save time and avoid errors. These are mostly rule-based algorithms that recognize drug interactions or provide reminder functions. Through artificial intelligence (AI), clinical decision support systems can be disruptively further developed. New knowledge is constantly being created from data through machine learning in order to predict the individual course of a patient's disease, identify phenotypes or support treatment decisions. Such algorithms already exist for rheumatological diseases. Automated image recognition and disease prediction in rheumatoid arthritis are the most advanced; however, these have not yet been sufficiently tested or integrated into existing decision support systems. Rather than dictating the AI-assisted choice of treatment to the doctor, future clinical decision systems are seen as hybrid decision support, always involving both the expert and the patient. There is also a great need for security through comprehensible and auditable algorithms to sustainably guarantee the quality and transparency of AI-assisted treatment recommendations in the long term.
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Affiliation(s)
- Thomas Hügle
- Abteilung Rheumatologie, Universitätsspital Lausanne (CHUV) und Universität Lausanne, Avenue Pierre-Decker 4, 1011, Lausanne, Schweiz.
| | - Maria Kalweit
- Institut für Informatik, Albert-Ludwigs-Universität Freiburg, Universität Freiburg im Breisgau, Georges-Koehler-Allee 80, 79110, Freiburg im Breisgau, Deutschland
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Peng J, Jury EC, Dönnes P, Ciurtin C. Machine Learning Techniques for Personalised Medicine Approaches in Immune-Mediated Chronic Inflammatory Diseases: Applications and Challenges. Front Pharmacol 2021; 12:720694. [PMID: 34658859 PMCID: PMC8514674 DOI: 10.3389/fphar.2021.720694] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 09/14/2021] [Indexed: 12/12/2022] Open
Abstract
In the past decade, the emergence of machine learning (ML) applications has led to significant advances towards implementation of personalised medicine approaches for improved health care, due to the exceptional performance of ML models when utilising complex big data. The immune-mediated chronic inflammatory diseases are a group of complex disorders associated with dysregulated immune responses resulting in inflammation affecting various organs and systems. The heterogeneous nature of these diseases poses great challenges for tailored disease management and addressing unmet patient needs. Applying novel ML techniques to the clinical study of chronic inflammatory diseases shows promising results and great potential for precision medicine applications in clinical research and practice. In this review, we highlight the clinical applications of various ML techniques for prediction, diagnosis and prognosis of autoimmune rheumatic diseases, inflammatory bowel disease, autoimmune chronic kidney disease, and multiple sclerosis, as well as ML applications for patient stratification and treatment selection. We highlight the use of ML in drug development, including target identification, validation and drug repurposing, as well as challenges related to data interpretation and validation, and ethical concerns related to the use of artificial intelligence in clinical research.
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Affiliation(s)
- Junjie Peng
- Department of Medicine, Centre for Adolescent Rheumatology Versus Arthritis, University College London, London, United Kingdom
| | - Elizabeth C. Jury
- Department of Medicine, Centre for Adolescent Rheumatology Versus Arthritis, University College London, London, United Kingdom
- Department of Medicine, Centre for Rheumatology Research, University College London, London, United Kingdom
| | | | - Coziana Ciurtin
- Department of Medicine, Centre for Adolescent Rheumatology Versus Arthritis, University College London, London, United Kingdom
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Fiocchi C, Dragoni G, Iliopoulos D, Katsanos K, Ramirez VH, Suzuki K, Torres J, Scharl M. Results of the Seventh Scientific Workshop of ECCO: Precision Medicine in IBD-What, Why, and How. J Crohns Colitis 2021; 15:1410-1430. [PMID: 33733656 DOI: 10.1093/ecco-jcc/jjab051] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Many diseases that affect modern humans fall in the category of complex diseases, thus called because they result from a combination of multiple aetiological and pathogenic factors. Regardless of the organ or system affected, complex diseases present major challenges in diagnosis, classification, and management. Current forms of therapy are usually applied in an indiscriminate fashion based on clinical information, but even the most advanced drugs only benefit a limited number of patients and to a variable and unpredictable degree. This 'one measure does not fit all' situation has spurred the notion that therapy for complex disease should be tailored to individual patients or groups of patients, giving rise to the notion of 'precision medicine' [PM]. Inflammatory bowel disease [IBD] is a prototypical complex disease where the need for PM has become increasingly clear. This prompted the European Crohn's and Colitis Organisation to focus the Seventh Scientific Workshop on this emerging theme. The articles in this special issue of the Journal address the various complementary aspects of PM in IBD, including what PM is; why it is needed and how it can be used; how PM can contribute to prediction and prevention of IBD; how IBD PM can aid in prognosis and improve response to therapy; and the challenges and future directions of PM in IBD. This first article of this series is structured on three simple concepts [what, why, and how] and addresses the definition of PM, discusses the rationale for the need of PM in IBD, and outlines the methodology required to implement PM in IBD in a correct and clinically meaningful way.
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Affiliation(s)
- Claudio Fiocchi
- Department of Inflammation & Immunity, Lerner Research Institute, and Department of Gastroenterology, Hepatology & Nutrition, Digestive Disease Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Gabriele Dragoni
- Gastroenterology Research Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence,Italy.,IBD Referral Center, Gastroenterology Department, Careggi University Hospital, Florence,Italy
| | | | - Konstantinos Katsanos
- Division of Gastroenterology, Department of Internal Medicine, University of Ioannina School of Health Sciences, Ioannina,Greece
| | - Vicent Hernandez Ramirez
- Department of Gastroenterology, Xerencia Xestión Integrada de Vigo, and Research Group in Digestive Diseases, Galicia Sur Health Research Institute [IIS Galicia Sur], SERGAS-UVIGO, Vigo, Spain
| | - Kohei Suzuki
- Division of Digestive and Liver Diseases, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX,USA
| | | | - Joana Torres
- Division of Gastroenterology, Hospital Beatriz Ângelo, Loures, Portugal
| | - Michael Scharl
- Department of Gastroenterology and Hepatology, University Hospital Zürich, Zürich, Switzerland
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Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications. Genes (Basel) 2021; 12:genes12091438. [PMID: 34573420 PMCID: PMC8466305 DOI: 10.3390/genes12091438] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/21/2021] [Accepted: 09/14/2021] [Indexed: 12/14/2022] Open
Abstract
Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.
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83
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Bartoloni E, Perricone C, Cafaro G, Alunno A, Gerli R. The facts and fictions of precision medicine in autoimmune diseases: is machine learning approach the response? Rheumatology (Oxford) 2021; 61:484-485. [PMID: 34534273 DOI: 10.1093/rheumatology/keab715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/30/2021] [Accepted: 09/10/2021] [Indexed: 11/12/2022] Open
Affiliation(s)
- Elena Bartoloni
- Rheumatology Unit. Department of Medicine and Surgery. University of Perugia, Perugia, Italy
| | - Carlo Perricone
- Rheumatology Unit. Department of Medicine and Surgery. University of Perugia, Perugia, Italy
| | - Giacomo Cafaro
- Rheumatology Unit. Department of Medicine and Surgery. University of Perugia, Perugia, Italy
| | - Alessia Alunno
- Rheumatology Unit. Department of Medicine and Surgery. University of Perugia, Perugia, Italy
| | - Roberto Gerli
- Rheumatology Unit. Department of Medicine and Surgery. University of Perugia, Perugia, Italy
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Foo LL, Ng WY, Lim GYS, Tan TE, Ang M, Ting DSW. Artificial intelligence in myopia: current and future trends. Curr Opin Ophthalmol 2021; 32:413-424. [PMID: 34310401 DOI: 10.1097/icu.0000000000000791] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
PURPOSE OF REVIEW Myopia is one of the leading causes of visual impairment, with a projected increase in prevalence globally. One potential approach to address myopia and its complications is early detection and treatment. However, current healthcare systems may not be able to cope with the growing burden. Digital technological solutions such as artificial intelligence (AI) have emerged as a potential adjunct for myopia management. RECENT FINDINGS There are currently four significant domains of AI in myopia, including machine learning (ML), deep learning (DL), genetics and natural language processing (NLP). ML has been demonstrated to be a useful adjunctive for myopia prediction and biometry for cataract surgery in highly myopic individuals. DL techniques, particularly convoluted neural networks, have been applied to various image-related diagnostic and predictive solutions. Applications of AI in genomics and NLP appear to be at a nascent stage. SUMMARY Current AI research is mainly focused on disease classification and prediction in myopia. Through greater collaborative research, we envision AI will play an increasingly critical role in big data analysis by aggregating a greater variety of parameters including genomics and environmental factors. This may enable the development of generalizable adjunctive DL systems that could help realize predictive and individualized precision medicine for myopic patients.
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Affiliation(s)
- Li Lian Foo
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Wei Yan Ng
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | | | - Tien-En Tan
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Marcus Ang
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Daniel Shu Wei Ting
- Singapore National Eye Centre, Singapore Eye Research Institute
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
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85
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Ding D, Lang T, Zou D, Tan J, Chen J, Zhou L, Wang D, Li R, Li Y, Liu J, Ma C, Zhou Q. Machine learning-based prediction of survival prognosis in cervical cancer. BMC Bioinformatics 2021; 22:331. [PMID: 34134623 PMCID: PMC8207793 DOI: 10.1186/s12859-021-04261-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 06/11/2021] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Accurately forecasting the prognosis could improve cervical cancer management, however, the currently used clinical features are difficult to provide enough information. The aim of this study is to improve forecasting capability by developing a miRNAs-based machine learning survival prediction model. RESULTS The expression characteristics of miRNAs were chosen as features for model development. The cervical cancer miRNA expression data was obtained from The Cancer Genome Atlas database. Preprocessing, including unquantified data removal, missing value imputation, samples normalization, log transformation, and feature scaling, was performed. In total, 42 survival-related miRNAs were identified by Cox Proportional-Hazards analysis. The patients were optimally clustered into four groups with three different 5-years survival outcome (≥ 90%, ≈ 65%, ≤ 40%) by K-means clustering algorithm base on top 10 survival-related miRNAs. According to the K-means clustering result, a prediction model with high performance was established. The pathways analysis indicated that the miRNAs used play roles involved in the regulation of cancer stem cells. CONCLUSION A miRNAs-based machine learning cervical cancer survival prediction model was developed that robustly stratifies cervical cancer patients into high survival rate (5-years survival rate ≥ 90%), moderate survival rate (5-years survival rate ≈ 65%), and low survival rate (5-years survival rate ≤ 40%).
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Affiliation(s)
- Dongyan Ding
- Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, People's Republic of China
- Department of Gynecologic Oncology, School of Medicine, Chongqing University Cancer Hospital, , Chongqing University, Chongqing, 400030, People's Republic of China
| | - Tingyuan Lang
- Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, People's Republic of China.
- Department of Gynecologic Oncology, School of Medicine, Chongqing University Cancer Hospital, , Chongqing University, Chongqing, 400030, People's Republic of China.
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, School of Medicine, Chongqing University Cancer Hospital, Chongqing University, Chongqing, 400030, People's Republic of China.
| | - Dongling Zou
- Department of Gynecologic Oncology, School of Medicine, Chongqing University Cancer Hospital, , Chongqing University, Chongqing, 400030, People's Republic of China
| | - Jiawei Tan
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, 130012, People's Republic of China
| | - Jia Chen
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, 130012, People's Republic of China
| | - Lei Zhou
- Singapore Eye Research Institute, The academia, 20 College Road, Discovery Tower Level 6, Singapore, 169856, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Duke-NUS Medical School, Ophthalmology and Visual Sciences Academic Clinical Research Program, National University of Singapore, Singapore, Singapore
| | - Dong Wang
- Department of Gynecologic Oncology, School of Medicine, Chongqing University Cancer Hospital, , Chongqing University, Chongqing, 400030, People's Republic of China
| | - Rong Li
- Department of Gynecologic Oncology, School of Medicine, Chongqing University Cancer Hospital, , Chongqing University, Chongqing, 400030, People's Republic of China
| | - Yunzhe Li
- Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, People's Republic of China
- Department of Gynecologic Oncology, School of Medicine, Chongqing University Cancer Hospital, , Chongqing University, Chongqing, 400030, People's Republic of China
| | - Jingshu Liu
- Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, People's Republic of China
- Department of Gynecologic Oncology, School of Medicine, Chongqing University Cancer Hospital, , Chongqing University, Chongqing, 400030, People's Republic of China
| | - Cui Ma
- Department of Pediatric Hematology, First Hospital of Jilin University, Changchun, 130023, Jilin, People's Republic of China
| | - Qi Zhou
- Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, People's Republic of China.
- Department of Gynecologic Oncology, School of Medicine, Chongqing University Cancer Hospital, , Chongqing University, Chongqing, 400030, People's Republic of China.
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, School of Medicine, Chongqing University Cancer Hospital, Chongqing University, Chongqing, 400030, People's Republic of China.
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de la Calle-Fabregat C, Niemantsverdriet E, Cañete JD, Li T, van der Helm-van Mil AHM, Rodríguez-Ubreva J, Ballestar E. The DNA methylation Profile of Undifferentiated Arthritis Patients Anticipates their Subsequent Differentiation to Rheumatoid Arthritis. Arthritis Rheumatol 2021; 73:2229-2239. [PMID: 34105306 DOI: 10.1002/art.41885] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 05/27/2021] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Undifferentiated arthritis (UA) is the term used to cover all the cases of arthritis that do not fit a specific diagnosis. A significant percentage of UA patients progress to rheumatoid arthritis (RA), others to a different definite rheumatic disease, and the rest undergo spontaneous remission. Therapeutic intervention in patients with UA can delay or halt disease progression and its long-term consequences. It is therefore of inherent interest to identify those UA patients with a high probability of progressing to RA who would benefit from early appropriate therapy. We hypothesized that alterations in the DNA methylation profiles of immune cells may inform on the genetically- or environmentally-determined status of patients and potentially discriminate between disease subtypes. METHODS In this study, we performed DNA methylation profiling of a UA patient cohort, in which progression into RA occurs for a significant proportion of the patients. RESULTS We find differential DNA methylation in UA patients compared to healthy controls. Most importantly, our analysis identifies a DNA methylation signature characteristic of those UA cases that differentiate to RA. We demonstrate that the methylome of peripheral mononuclear cells can be used to anticipate the evolution of UA to RA, and that this methylome is associated with a number of inflammatory pathways and transcription factors. Finally, we design a machine-learning strategy for DNA methylation-based classification that predicts the differentiation of UA patients towards RA. CONCLUSION DNA methylation profiling provides a good predictor of UA-to-RA progression to anticipate targeted treatments and improve clinical management.
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Affiliation(s)
| | - Ellis Niemantsverdriet
- Department of Rheumatology, Leiden University Medical Center, P.O. Box 9600, 2300 RC, Leiden, the Netherlands
| | - Juan D Cañete
- Rheumatology Service, Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036, Barcelona, Spain
| | - Tianlu Li
- Epigenetics and Immune Disease Group, Josep Carreras Research Institute (IJC), Barcelona, Spain
| | | | - Javier Rodríguez-Ubreva
- Epigenetics and Immune Disease Group, Josep Carreras Research Institute (IJC), Barcelona, Spain
| | - Esteban Ballestar
- Epigenetics and Immune Disease Group, Josep Carreras Research Institute (IJC), Barcelona, Spain
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87
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Prediction of obstructive sleep apnea using Fast Fourier Transform of overnight breath recordings. MACHINE LEARNING WITH APPLICATIONS 2021. [DOI: 10.1016/j.mlwa.2021.100022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
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88
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Pai VV, Pai RB. Artificial intelligence in dermatology and healthcare: An overview. Indian J Dermatol Venereol Leprol 2021; 87:457-467. [PMID: 34114421 DOI: 10.25259/ijdvl_518_19] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Accepted: 12/01/2020] [Indexed: 12/15/2022]
Abstract
Many aspects of our life are affected by technology. One of the most discussed advancements of modern technologies is artificial intelligence. It involves computational methods which in some way mimic the human thought process. Just like other branches, the medical field also has come under the ambit of artificial intelligence. Almost every field in medicine has been touched by its effect in one way or the other. Prominent among them are medical diagnosis, medical statistics, robotics, and human biology. Medical imaging is one of the foremost specialties with artificial intelligence applications, wherein deep learning methods like artificial neural networks are commonly used. artificial intelligence application in dermatology was initially restricted to the analysis of melanoma and pigmentary skin lesions, has now expanded and covers many dermatoses. Though the applications of artificial intelligence are ever increasing, large data requirements, interpretation of data and ethical concerns are some of its limitations in the present day.
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Affiliation(s)
| | - Rohini Bhat Pai
- Department of Anaesthesiology, Goa Medical College, Bambolim, Goa, India
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Therapeutic prospects of MicroRNAs carried by mesenchymal stem cells-derived extracellular vesicles in autoimmune diseases. Life Sci 2021; 277:119458. [PMID: 33831424 DOI: 10.1016/j.lfs.2021.119458] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 01/31/2021] [Accepted: 03/26/2021] [Indexed: 02/05/2023]
Abstract
Autoimmune diseases (ADs) are a class of chronic disease conditions with impaired tolerance to autoantigens. Currently, there is no effective treatment for ADs, and the existing medications have limitations due to non-specific targets and side effects. Accumulating evidence has shown that mesenchymal stem cells (MSCs) play a role in ADs treatment. These beneficial effects mainly rely on cell-to-cell communication through the secretion of extracellular vesicles (EVs) and soluble factors. MSC-derived EVs (MSC-EVs) could modulate adjacent and distinct cells by transferring various DNA, mRNA, non-coding RNAs, proteins, and lipids from parent cells to recipient cells. MicroRNAs (miRNAs) are small non-coding RNAs that negatively regulate multiple target genes at the post-transcriptional level and are involved in chronic inflammatory and immune processes. Compared to fluid, MSC-EVs delivery can protect miRNAs from the degradation of ribonucleases, ensuring that miRNAs are able to perform their respective crucial roles in AD recipient cells. In this review, we discussed the therapeutic prospects and challenges of miRNAs secreted by MSC-EVs (MSC-EV-miRNAs) by reviewing the experimentally verified therapeutic outcomes of MSC-EV-miRNAs for several ADs, including rheumatoid arthritis (RA), autoimmune hepatitis (AIH), asthma, colitis, systemic sclerosis (SSc) and graft-versus-host disease (GVHD).
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90
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Munger E, Hickey JW, Dey AK, Jafri MS, Kinser JM, Mehta NN. Application of machine learning in understanding atherosclerosis: Emerging insights. APL Bioeng 2021; 5:011505. [PMID: 33644628 PMCID: PMC7889295 DOI: 10.1063/5.0028986] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 01/21/2021] [Indexed: 01/18/2023] Open
Abstract
Biological processes are incredibly complex—integrating molecular signaling networks involved in multicellular communication and function, thus maintaining homeostasis. Dysfunction of these processes can result in the disruption of homeostasis, leading to the development of several disease processes including atherosclerosis. We have significantly advanced our understanding of bioprocesses in atherosclerosis, and in doing so, we are beginning to appreciate the complexities, intricacies, and heterogeneity atherosclerosi. We are also now better equipped to acquire, store, and process the vast amount of biological data needed to shed light on the biological circuitry involved. Such data can be analyzed within machine learning frameworks to better tease out such complex relationships. Indeed, there has been an increasing number of studies applying machine learning methods for patient risk stratification based on comorbidities, multi-modality image processing, and biomarker discovery pertaining to atherosclerotic plaque formation. Here, we focus on current applications of machine learning to provide insight into atherosclerotic plaque formation and better understand atherosclerotic plaque progression in patients with cardiovascular disease.
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Affiliation(s)
| | - John W Hickey
- Stanford University, Stanford, California 94306, USA
| | - Amit K Dey
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | | | | | - Nehal N Mehta
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
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91
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Seccia R, Romano S, Salvetti M, Crisanti A, Palagi L, Grassi F. Machine Learning Use for Prognostic Purposes in Multiple Sclerosis. Life (Basel) 2021; 11:life11020122. [PMID: 33562572 PMCID: PMC7914671 DOI: 10.3390/life11020122] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 01/29/2021] [Accepted: 01/30/2021] [Indexed: 12/28/2022] Open
Abstract
The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.
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Affiliation(s)
- Ruggiero Seccia
- Department of Computer, Control and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, 00185 Rome, Italy; (R.S.); (L.P.)
| | - Silvia Romano
- Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, 00189 Rome, Italy; (S.R.); (M.S.)
| | - Marco Salvetti
- Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, 00189 Rome, Italy; (S.R.); (M.S.)
- Mediterranean Neurological Institute Neuromed, 86077 Pozzilli, Italy
| | - Andrea Crisanti
- Department of Physics, Sapienza University of Rome, 00185 Rome, Italy;
| | - Laura Palagi
- Department of Computer, Control and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, 00185 Rome, Italy; (R.S.); (L.P.)
| | - Francesca Grassi
- Department of Physiology and Pharmacology, Sapienza University of Rome, 00185 Rome, Italy
- Correspondence:
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92
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Vázquez-Marrufo M, Sarrias-Arrabal E, García-Torres M, Martín-Clemente R, Izquierdo G. A systematic review of the application of machine-learning algorithms in multiple sclerosis. Neurologia 2021; 38:S0213-4853(20)30431-X. [PMID: 33549371 DOI: 10.1016/j.nrl.2020.10.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 08/20/2020] [Accepted: 10/11/2020] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years. OBJECTIVE We present a systematic review of the application of ML algorithms in MS. MATERIALS AND METHODS We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords "machine learning" and "multiple sclerosis." We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected. CONCLUSIONS After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.
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Affiliation(s)
- M Vázquez-Marrufo
- Departamento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Sevilla, España.
| | - E Sarrias-Arrabal
- Departamento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Sevilla, España
| | - M García-Torres
- Escuela Politécnica Superior, Universidad Pablo de Olavide, Sevilla, España
| | - R Martín-Clemente
- Departamento de Teoría de la Señal y Comunicaciones, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Sevilla, España
| | - G Izquierdo
- Unidad de Esclerosis Múltiple, Hospital VITHAS, Sevilla, España
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93
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Davids J, Ashrafian H. AIM in Haematology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_182-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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94
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Akay M, Du Y, Sershen CL, Wu M, Chen TY, Assassi S, Mohan C, Akay YM. Deep Learning Classification of Systemic Sclerosis Skin Using the MobileNetV2 Model. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2021; 2:104-110. [PMID: 35402975 PMCID: PMC8901014 DOI: 10.1109/ojemb.2021.3066097] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/03/2021] [Accepted: 03/08/2021] [Indexed: 11/21/2022] Open
Abstract
Goal: Systemic sclerosis (SSc) is a rare autoimmune, systemic disease with prominent fibrosis of skin and internal organs. Early diagnosis of the disease is crucial for designing effective therapy and management plans. Machine learning algorithms, especially deep learning, have been found to be greatly useful in biology, medicine, healthcare, and biomedical applications, in the areas of medical image processing and speech recognition. However, the need for a large training data set and the requirement for a graphics processing unit (GPU) have hindered the wide application of machine learning algorithms as a diagnostic tool in resource-constrained environments (e.g., clinics). Methods: In this paper, we propose a novel mobile deep learning network for the characterization of SSc skin. The proposed network architecture consists of the UNet, a dense connectivity convolutional neural network (CNN) with added classifier layers that when combined with limited training data, yields better image segmentation and more accurate classification, and a mobile training module. In addition, to improve the computational efficiency and diagnostic accuracy, the highly efficient training model called “MobileNetV2,” which is designed for mobile and embedded applications, was used to train the network. Results: The proposed network was implemented using a standard laptop (2.5 GHz Intel Core i7). After fine tuning, our results showed the proposed network reached 100% accuracy on the training image set, 96.8% accuracy on the validation image set, and 95.2% on the testing image set. The training time was less than 5 hours. We also analyzed the same normal vs SSc skin image sets using the CNN using the same laptop. The CNN reached 100% accuracy on the training image set, 87.7% accuracy on the validation image set, and 82.9% on the testing image set. Additionally, it took more than 14 hours to train the CNN architecture. We also utilized the MobileNetV2 model to analyze an additional dataset of images and classified them as normal, early (mid and moderate) SSc or late (severe) SSc skin images. The network reached 100% accuracy on the training image set, 97.2% on the validation set, and 94.8% on the testing image set. Using the same normal, early and late phase SSc skin images, the CNN reached 100% accuracy on the training image set, 87.7% accuracy on the validation image set, and 82.9% on the testing image set. These results indicated that the MobileNetV2 architecture is more accurate and efficient compared to the CNN to classify normal, early and late phase SSc skin images. Conclusions: Our preliminary study, intended to show the efficacy of the proposed network architecture, holds promise in the characterization of SSc. We believe that the proposed network architecture could easily be implemented in a clinical setting, providing a simple, inexpensive, and accurate screening tool for SSc.
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Affiliation(s)
- Metin Akay
- Biomedical Engineering DepartmentUniversity of Houston Houston TX 77204 USA
| | - Yong Du
- Biomedical Engineering DepartmentUniversity of Houston Houston TX 77204 USA
| | - Cheryl L Sershen
- Biomedical Engineering DepartmentUniversity of Houston Houston TX 77204 USA
| | - Minghua Wu
- Division of Rheumatology and Clinical Immunogenetics, Department of Internal Medicine UTHealth Houston TX 77030 USA
| | - Ting Y Chen
- Biomedical Engineering DepartmentUniversity of Houston Houston TX 77204 USA
| | - Shervin Assassi
- Division of Rheumatology and Clinical Immunogenetics, Department of Internal Medicine UTHealth Houston TX 77030 USA
| | - Chandra Mohan
- Biomedical Engineering DepartmentUniversity of Houston Houston TX 77204 USA
| | - Yasemin M Akay
- Biomedical Engineering DepartmentUniversity of Houston Houston TX 77204 USA
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95
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Kohli A, Holzwanger EA, Levy AN. Emerging use of artificial intelligence in inflammatory bowel disease. World J Gastroenterol 2020; 26:6923-6928. [PMID: 33311940 PMCID: PMC7701951 DOI: 10.3748/wjg.v26.i44.6923] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/24/2020] [Accepted: 11/12/2020] [Indexed: 02/06/2023] Open
Abstract
Inflammatory bowel disease (IBD) is a complex, immune-mediated gastrointestinal disorder with ill-defined etiology, multifaceted diagnostic criteria, and unpredictable treatment response. Innovations in IBD diagnostics, including developments in genomic sequencing and molecular analytics, have generated tremendous interest in leveraging these large data platforms into clinically meaningful tools. Artificial intelligence, through machine learning facilitates the interpretation of large arrays of data, and may provide insight to improving IBD outcomes. While potential applications of machine learning models are vast, further research is needed to generate standardized models that can be adapted to target IBD populations.
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Affiliation(s)
- Arushi Kohli
- Department of Internal Medicine, Tufts Medical Center, Boston, MA 02111, United States
| | - Erik A Holzwanger
- Division of Gastroenterology and Hepatology, Tufts Medical Center, Boston, MA 02111, United States
| | - Alexander N Levy
- Division of Gastroenterology and Hepatology, Tufts Medical Center, Boston, MA 02111, United States
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96
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Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment. Diagnostics (Basel) 2020; 10:diagnostics10110958. [PMID: 33212774 PMCID: PMC7697204 DOI: 10.3390/diagnostics10110958] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 11/13/2020] [Indexed: 11/17/2022] Open
Abstract
In the last decade, machine learning has been widely used in different fields, especially because of its capacity to work with complex data. With the support of machine learning techniques, different studies have been using data-driven approaches to better understand some syndromes like mild cognitive impairment, Alzheimer’s disease, schizophrenia, and chronic pain. Chronic pain is a complex disease that can recurrently be misdiagnosed due to its comorbidities with other syndromes with which it shares symptoms. Within that context, several studies have been suggesting different machine learning algorithms to classify or predict chronic pain conditions. Those algorithms were fed with a diversity of data types, from self-report data based on questionnaires to the most advanced brain imaging techniques. In this study, we assessed the sensitivity of different algorithms and datasets classifying chronic pain syndromes. Together with this assessment, we highlighted important methodological steps that should be taken into account when an experiment using machine learning is conducted. The best results were obtained by ensemble-based algorithms and the dataset containing the greatest diversity of information, resulting in area under the receiver operating curve (AUC) values of around 0.85. In addition, the performance of the algorithms is strongly related to the hyper-parameters. Thus, a good strategy for hyper-parameter optimization should be used to extract the most from the algorithm. These findings support the notion that machine learning can be a powerful tool to better understand chronic pain conditions.
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97
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Choudhury A, Renjilian E, Asan O. Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review. JAMIA Open 2020; 3:459-471. [PMID: 33215079 PMCID: PMC7660963 DOI: 10.1093/jamiaopen/ooaa034] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 06/26/2020] [Accepted: 07/11/2020] [Indexed: 12/13/2022] Open
Abstract
Objectives Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients’ (age 65 years and above) functional ability, physical health, and cognitive well-being. The majority of these patients suffer from multiple chronic conditions and require special attention. Recently, hospitals utilize various artificial intelligence (AI) systems to improve care for elderly patients. The purpose of this systematic literature review is to understand the current use of AI systems, particularly machine learning (ML), in geriatric clinical care for chronic diseases. Materials and Methods We restricted our search to eight databases, namely PubMed, WorldCat, MEDLINE, ProQuest, ScienceDirect, SpringerLink, Wiley, and ERIC, to analyze research articles published in English between January 2010 and June 2019. We focused on studies that used ML algorithms in the care of geriatrics patients with chronic conditions. Results We identified 35 eligible studies and classified in three groups: psychological disorder (n = 22), eye diseases (n = 6), and others (n = 7). This review identified the lack of standardized ML evaluation metrics and the need for data governance specific to health care applications. Conclusion More studies and ML standardization tailored to health care applications are required to confirm whether ML could aid in improving geriatric clinical care.
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Affiliation(s)
- Avishek Choudhury
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Emily Renjilian
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Onur Asan
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, New Jersey, USA
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98
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Nakashima A, Takeya M, Kuba K, Takano M, Nakashima N. Virus database annotations assist in tracing information on patients infected with emerging pathogens. INFORMATICS IN MEDICINE UNLOCKED 2020; 21:100442. [PMID: 33052312 PMCID: PMC7543791 DOI: 10.1016/j.imu.2020.100442] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/20/2020] [Accepted: 10/03/2020] [Indexed: 01/01/2023] Open
Abstract
The global pandemic of SARS-CoV-2 has disrupted human social activities. In restarting economic activities, successive outbreaks by new variants are concerning. Here, we evaluated the applicability of public database annotations to estimate the virulence, transmission trends and origins of emerging SARS-CoV-2 variants. Among the detectable multiple mutations, we retraced the mutation in the spike protein. With the aid of the protein database, structural modelling yielded a testable scientific hypothesis on viral entry to host cells. Simultaneously, annotations for locations and collection dates suggested that the variant virus emerged somewhere in the world in approximately February 2020, entered the USA and propagated nationwide with periodic sampling fluctuation likely due to an approximately 5-day incubation delay. Thus, public database annotations are useful for automated elucidation of the early spreading patterns in relation to human behaviours, which should provide objective reference for local governments for social decision making to contain emerging substrains. We propose that additional annotations for past paths and symptoms of the patients should further assist in characterizing the exact virulence and origins of emerging pathogens.
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Affiliation(s)
- Akiko Nakashima
- Department of Physiology, Kurume University School of Medicine, Asahi-machi 67, Kurume, Fukuoka, 830-0011, Japan
| | - Mitsue Takeya
- Department of Physiology, Kurume University School of Medicine, Asahi-machi 67, Kurume, Fukuoka, 830-0011, Japan
| | - Keiji Kuba
- Department of Biochemistry and Metabolic Science, Akita University Graduate School of Medicine, 1-1-1 Hondo, Akita, 010-8543, Japan
| | - Makoto Takano
- Department of Physiology, Kurume University School of Medicine, Asahi-machi 67, Kurume, Fukuoka, 830-0011, Japan
| | - Noriyuki Nakashima
- Department of Physiology, Kurume University School of Medicine, Asahi-machi 67, Kurume, Fukuoka, 830-0011, Japan
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99
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Choudhury A, Asan O. Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review. JMIR Med Inform 2020; 8:e18599. [PMID: 32706688 PMCID: PMC7414411 DOI: 10.2196/18599] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/26/2020] [Accepted: 06/13/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. OBJECTIVE The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. METHODS We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. RESULTS We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. CONCLUSIONS This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.
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Affiliation(s)
- Avishek Choudhury
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Onur Asan
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States
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100
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Brichetto G. We should monitor our patients with wearable technology instead of neurological examination - Commentary. Mult Scler 2020; 26:1028-1030. [PMID: 32669039 DOI: 10.1177/1352458520930985] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Giampaolo Brichetto
- Scientific Research Area, Italian MS Society Foundation, Genoa, Italy/AISM Rehabilitation Centers, Italian MS Society, Genoa, Italy
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