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Kommuru S, Adekunle F, Niño S, Arefin S, Thalvayapati SP, Kuriakose D, Ahmadi Y, Vinyak S, Nazir Z. Role of Artificial Intelligence in the Diagnosis of Gastroesophageal Reflux Disease. Cureus 2024; 16:e62206. [PMID: 39006681 PMCID: PMC11240074 DOI: 10.7759/cureus.62206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/09/2024] [Indexed: 07/16/2024] Open
Abstract
Gastroesophageal reflux disease (GERD) is a disorder that usually presents with heartburn. GERD is diagnosed clinically, but most patients are misdiagnosed due to atypical presentations. The increased use of artificial intelligence (AI) in healthcare has provided multiple ways of diagnosing and treating patients accurately. In this review, multiple studies in which AI models were used to diagnose GERD are discussed. According to the studies, using AI models helped to diagnose GERD in patients accurately. AI, although considered one of the most potent emerging aspects of medicine with its accuracy in patient diagnosis, presents limitations of its own, which explains why healthcare providers may hesitate to use AI in patient care. The challenges and limitations should be addressed before AI is fully incorporated into the healthcare system.
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Affiliation(s)
- Sravani Kommuru
- Medical School, Dr. Pinnamaneni Siddhartha Institute of Medical Sciences & Research Foundation, Vijayawada, IND
| | - Faith Adekunle
- Medical School, American University of the Carribbean, Cupecoy, SXM
| | - Santiago Niño
- Surgery, Colegio Mayor de Nuestra Señora del Rosario, Bogota, COL
| | - Shamsul Arefin
- Internal Medicine, Nottingham University Hospitals NHS Trust, Nottingham, GBR
| | | | - Dona Kuriakose
- Internal Medicine, Petre Shotadze Tbilisi Medical Academy, Tbilisi, GEO
| | - Yasmin Ahmadi
- Medical School, Royal College of Surgeons in Ireland - Medical University of Bahrain, Busaiteen, BHR
| | - Suprada Vinyak
- Internal Medicine, Wellmont Health System/Norton Community Hospital, Norton, USA
| | - Zahra Nazir
- Internal Medicine, Combined Military Hospital, Quetta, PAK
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2
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Reeve K, On BI, Havla J, Burns J, Gosteli-Peter MA, Alabsawi A, Alayash Z, Götschi A, Seibold H, Mansmann U, Held U. Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis. Cochrane Database Syst Rev 2023; 9:CD013606. [PMID: 37681561 PMCID: PMC10486189 DOI: 10.1002/14651858.cd013606.pub2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
BACKGROUND Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system that affects millions of people worldwide. The disease course varies greatly across individuals and many disease-modifying treatments with different safety and efficacy profiles have been developed recently. Prognostic models evaluated and shown to be valid in different settings have the potential to support people with MS and their physicians during the decision-making process for treatment or disease/life management, allow stratified and more precise interpretation of interventional trials, and provide insights into disease mechanisms. Many researchers have turned to prognostic models to help predict clinical outcomes in people with MS; however, to our knowledge, no widely accepted prognostic model for MS is being used in clinical practice yet. OBJECTIVES To identify and summarise multivariable prognostic models, and their validation studies for quantifying the risk of clinical disease progression, worsening, and activity in adults with MS. SEARCH METHODS We searched MEDLINE, Embase, and the Cochrane Database of Systematic Reviews from January 1996 until July 2021. We also screened the reference lists of included studies and relevant reviews, and references citing the included studies. SELECTION CRITERIA We included all statistically developed multivariable prognostic models aiming to predict clinical disease progression, worsening, and activity, as measured by disability, relapse, conversion to definite MS, conversion to progressive MS, or a composite of these in adult individuals with MS. We also included any studies evaluating the performance of (i.e. validating) these models. There were no restrictions based on language, data source, timing of prognostication, or timing of outcome. DATA COLLECTION AND ANALYSIS Pairs of review authors independently screened titles/abstracts and full texts, extracted data using a piloted form based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), assessed risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST), and assessed reporting deficiencies based on the checklist items in Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). The characteristics of the included models and their validations are described narratively. We planned to meta-analyse the discrimination and calibration of models with at least three external validations outside the model development study but no model met this criterion. We summarised between-study heterogeneity narratively but again could not perform the planned meta-regression. MAIN RESULTS We included 57 studies, from which we identified 75 model developments, 15 external validations corresponding to only 12 (16%) of the models, and six author-reported validations. Only two models were externally validated multiple times. None of the identified external validations were performed by researchers independent of those that developed the model. The outcome was related to disease progression in 39 (41%), relapses in 8 (8%), conversion to definite MS in 17 (18%), and conversion to progressive MS in 27 (28%) of the 96 models or validations. The disease and treatment-related characteristics of included participants, and definitions of considered predictors and outcome, were highly heterogeneous amongst the studies. Based on the publication year, we observed an increase in the percent of participants on treatment, diversification of the diagnostic criteria used, an increase in consideration of biomarkers or treatment as predictors, and increased use of machine learning methods over time. Usability and reproducibility All identified models contained at least one predictor requiring the skills of a medical specialist for measurement or assessment. Most of the models (44; 59%) contained predictors that require specialist equipment likely to be absent from primary care or standard hospital settings. Over half (52%) of the developed models were not accompanied by model coefficients, tools, or instructions, which hinders their application, independent validation or reproduction. The data used in model developments were made publicly available or reported to be available on request only in a few studies (two and six, respectively). Risk of bias We rated all but one of the model developments or validations as having high overall risk of bias. The main reason for this was the statistical methods used for the development or evaluation of prognostic models; we rated all but two of the included model developments or validations as having high risk of bias in the analysis domain. None of the model developments that were externally validated or these models' external validations had low risk of bias. There were concerns related to applicability of the models to our research question in over one-third (38%) of the models or their validations. Reporting deficiencies Reporting was poor overall and there was no observable increase in the quality of reporting over time. The items that were unclearly reported or not reported at all for most of the included models or validations were related to sample size justification, blinding of outcome assessors, details of the full model or how to obtain predictions from it, amount of missing data, and treatments received by the participants. Reporting of preferred model performance measures of discrimination and calibration was suboptimal. AUTHORS' CONCLUSIONS The current evidence is not sufficient for recommending the use of any of the published prognostic prediction models for people with MS in clinical routine today due to lack of independent external validations. The MS prognostic research community should adhere to the current reporting and methodological guidelines and conduct many more state-of-the-art external validation studies for the existing or newly developed models.
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Affiliation(s)
- Kelly Reeve
- Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Zurich, Switzerland
| | - Begum Irmak On
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Joachim Havla
- lnstitute of Clinical Neuroimmunology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Jacob Burns
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | | | - Albraa Alabsawi
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Zoheir Alayash
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
- Institute of Health Services Research in Dentistry, University of Münster, Muenster, Germany
| | - Andrea Götschi
- Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Zurich, Switzerland
| | | | - Ulrich Mansmann
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Ulrike Held
- Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Zurich, Switzerland
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3
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Gniadek T, Kang J, Theparee T, Krive J. Framework for Classifying Explainable Artificial Intelligence (XAI) Algorithms in Clinical Medicine. Online J Public Health Inform 2023; 15:e50934. [PMID: 38046562 PMCID: PMC10689048 DOI: 10.2196/50934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 02/27/2023] [Indexed: 12/05/2023] Open
Abstract
Artificial intelligence (AI) applied to medicine offers immense promise, in addition to safety and regulatory concerns. Traditional AI produces a core algorithm result, typically without a measure of statistical confidence or an explanation of its biological-theoretical basis. Efforts are underway to develop explainable AI (XAI) algorithms that not only produce a result but also an explanation to support that result. Here we present a framework for classifying XAI algorithms applied to clinical medicine: An algorithm's clinical scope is defined by whether the core algorithm output leads to observations (eg, tests, imaging, clinical evaluation), interventions (eg, procedures, medications), diagnoses, and prognostication. Explanations are classified by whether they provide empiric statistical information, association with a historical population or populations, or association with an established disease mechanism or mechanisms. XAI implementations can be classified based on whether algorithm training and validation took into account the actions of health care providers in response to the insights and explanations provided or whether training was performed using only the core algorithm output as the end point. Finally, communication modalities used to convey an XAI explanation can be used to classify algorithms and may affect clinical outcomes. This framework can be used when designing, evaluating, and comparing XAI algorithms applied to medicine.
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Affiliation(s)
- Thomas Gniadek
- Department of Pathology and Laboratory Medicine NorthShore University Health System Evanston, IL United States
| | - Jason Kang
- Department of Pathology and Laboratory Medicine NorthShore University Health System Evanston, IL United States
| | - Talent Theparee
- Department of Pathology and Laboratory Medicine NorthShore University Health System Evanston, IL United States
| | - Jacob Krive
- Department of Biomedical and Health Information Sciences University of Illinois at Chicago Chicago, IL United States
- Department of Health Information Technology NorthShore University Health System Evanston, IL United States
- Department of Health Informatics Dr Kiran C Patel School of Osteopathic Medicine Nova Southeastern University Fort Lauderdale, FL United States
- Pritzker School of Medicine University of Chicago Chicago, IL United States
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4
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Wandell GM, Law AB, Maxin A, Ha VT, Wilson EC, Nash MG, Merati AL, Whipple ME, Meyer TK. Defining the Performance of Clinician's Ability to Screen for Laryngeal Mass From Voice. Otolaryngol Head Neck Surg 2023; 168:1371-1380. [PMID: 36939403 DOI: 10.1002/ohn.206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 10/12/2022] [Accepted: 11/03/2022] [Indexed: 01/25/2023]
Abstract
OBJECTIVE Defining a clinician's ability to perceptually identify mass from voice will inform the feasibility, design priorities, and performance standards for tools developed to screen for laryngeal mass from voice. This study defined clinician ability of and examined the impact of expertise on screening for laryngeal mass from voice. STUDY DESIGN Task comparison study between experts and nonexperts rating voices for the probability of a laryngeal mass. SETTING Online, remote. METHODS Experts (voice-focused speech-language pathologists and otolaryngologists) and nonexperts (general medicine providers) rated 5-s/i/voice samples (with pathology defined by laryngoscopy) for the probability of laryngeal mass via an online survey. The intraclass correlation coefficient (ICC) estimated interrater and intrarater reliability. Diagnostic performance metrics were calculated. A linear mixed effects model examined the impact of expertise and pathology on ratings. RESULTS Forty clinicians (21 experts and 19 nonexperts) evaluated 344 voice samples. Experts outperformed nonexperts, with a higher area under the curve (70% vs 61%), sensitivity (49% vs 36%), and specificity (83% vs 77%) (all comparisons p < .05). Interrater reliability was fair for experts and poor for nonexperts (ICC: 0.48 vs 0.34), while intrarater reliability was excellent and good, respectively (ICC: 0.9 and 0.6). The main effects of expertise and underlying pathology were significant in the linear model (p < .001). CONCLUSION Clinicians demonstrate inadequate performance screening for laryngeal mass from voice to use auditory perception for dysphonia triage. Experts' superior performance indicates that there is acoustic information in a voice that may be utilized to detect laryngeal mass based on voice.
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Affiliation(s)
- Grace M Wandell
- Department of Otolaryngology-Head and Neck Surgery, University of Washington School of Medicine, Seattle, Washington, USA
| | - Anthony B Law
- Department of Otolaryngology-Head and Neck Surgery, Emory School of Medicine, Atlanta, Georgia, USA
| | - Anthony Maxin
- School of Medicine, Creighton University, Nebraska, Omaha, USA
| | - Vivian T Ha
- Department of Otolaryngology-Head and Neck Surgery, University of Washington School of Medicine, Seattle, Washington, USA
| | - Emily C Wilson
- Department of Otolaryngology-Head and Neck Surgery, University of Washington School of Medicine, Seattle, Washington, USA.,Department of Speech and Hearing Sciences, University of Washington, Seattle, Washington, USA
| | - Michael G Nash
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Albert L Merati
- Department of Otolaryngology-Head and Neck Surgery, University of Washington School of Medicine, Seattle, Washington, USA
| | - Mark E Whipple
- Department of Otolaryngology-Head and Neck Surgery, University of Washington School of Medicine, Seattle, Washington, USA
| | - Tanya K Meyer
- Department of Otolaryngology-Head and Neck Surgery, University of Washington School of Medicine, Seattle, Washington, USA
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5
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Ed-Driouch C, Mars F, Gourraud PA, Dumas C. Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice: An Innovative Method to Hybrid Human-Machine Intelligence. SENSORS (BASEL, SWITZERLAND) 2022; 22:8313. [PMID: 36366011 PMCID: PMC9653746 DOI: 10.3390/s22218313] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/25/2022] [Accepted: 10/26/2022] [Indexed: 06/12/2023]
Abstract
Machine learning (ML) models have proven their potential in acquiring and analyzing large amounts of data to help solve real-world, complex problems. Their use in healthcare is expected to help physicians make diagnoses, prognoses, treatment decisions, and disease outcome predictions. However, ML solutions are not currently deployed in most healthcare systems. One of the main reasons for this is the provenance, transparency, and clinical utility of the training data. Physicians reject ML solutions if they are not at least based on accurate data and do not clearly include the decision-making process used in clinical practice. In this paper, we present a hybrid human-machine intelligence method to create predictive models driven by clinical practice. We promote the use of quality-approved data and the inclusion of physician reasoning in the ML process. Instead of training the ML algorithms on the given data to create predictive models (conventional method), we propose to pre-categorize the data according to the expert physicians' knowledge and experience. Comparing the results of the conventional method of ML learning versus the hybrid physician-algorithm method showed that the models based on the latter can perform better. Physicians' engagement is the most promising condition for the safe and innovative use of ML in healthcare.
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Affiliation(s)
- Chadia Ed-Driouch
- École Centrale Nantes, IMT Atlantique, Nantes Université, CNRS, LS2N, UMR 6004, F-44000 Nantes, France
| | - Franck Mars
- Centrale Nantes, Nantes Université, CNRS, LS2N, UMR 6004, F-44000 Nantes, France
| | - Pierre-Antoine Gourraud
- Clinique des Données, Pôle Hospitalo-Universitaire 11: Santé Publique, CHU Nantes, Nantes Université, INSERM, CIC 1413, F-44000 Nantes, France
| | - Cédric Dumas
- Département Automatique, Productique et Informatique, IMT Atlantique, CNRS, LS2N, UMR CNRS 6004, F-44000 Nantes, France
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6
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Swanberg KM, Kurada AV, Prinsen H, Juchem C. Multiple sclerosis diagnosis and phenotype identification by multivariate classification of in vivo frontal cortex metabolite profiles. Sci Rep 2022; 12:13888. [PMID: 35974117 PMCID: PMC9381573 DOI: 10.1038/s41598-022-17741-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 07/29/2022] [Indexed: 12/04/2022] Open
Abstract
Multiple sclerosis (MS) is a heterogeneous autoimmune disease for which diagnosis continues to rely on subjective clinical judgment over a battery of tests. Proton magnetic resonance spectroscopy (1H MRS) enables the noninvasive in vivo detection of multiple small-molecule metabolites and is therefore in principle a promising means of gathering information sufficient for multiple sclerosis diagnosis and subtype classification. Here we show that supervised classification using 1H-MRS-visible normal-appearing frontal cortex small-molecule metabolites alone can indeed differentiate individuals with progressive MS from control (held-out validation sensitivity 79% and specificity 68%), as well as between relapsing and progressive MS phenotypes (held-out validation sensitivity 84% and specificity 74%). Post hoc assessment demonstrated the disproportionate contributions of glutamate and glutamine to identifying MS status and phenotype, respectively. Our finding establishes 1H MRS as a viable means of characterizing progressive multiple sclerosis disease status and paves the way for continued refinement of this method as an auxiliary or mainstay of multiple sclerosis diagnostics.
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Affiliation(s)
- Kelley M. Swanberg
- grid.25879.310000 0004 1936 8972Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, 351 Engineering Terrace, 1210 Amsterdam Avenue, Mail Code: 8904, New York, NY 10027 USA ,grid.47100.320000000419368710Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT USA
| | - Abhinav V. Kurada
- grid.25879.310000 0004 1936 8972Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, 351 Engineering Terrace, 1210 Amsterdam Avenue, Mail Code: 8904, New York, NY 10027 USA
| | - Hetty Prinsen
- grid.47100.320000000419368710Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT USA
| | - Christoph Juchem
- grid.25879.310000 0004 1936 8972Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, 351 Engineering Terrace, 1210 Amsterdam Avenue, Mail Code: 8904, New York, NY 10027 USA ,grid.47100.320000000419368710Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT USA ,grid.21729.3f0000000419368729Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY USA ,grid.47100.320000000419368710Department of Neurology, Yale University School of Medicine, New Haven, CT USA
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7
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Termine A, Fabrizio C, Caltagirone C, Petrosini L. A Reproducible Deep-Learning-Based Computer-Aided Diagnosis Tool for Frontotemporal Dementia Using MONAI and Clinica Frameworks. Life (Basel) 2022; 12:947. [PMID: 35888037 PMCID: PMC9323676 DOI: 10.3390/life12070947] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/16/2022] [Accepted: 06/21/2022] [Indexed: 12/16/2022] Open
Abstract
Despite Artificial Intelligence (AI) being a leading technology in biomedical research, real-life implementation of AI-based Computer-Aided Diagnosis (CAD) tools into the clinical setting is still remote due to unstandardized practices during development. However, few or no attempts have been made to propose a reproducible CAD development workflow for 3D MRI data. In this paper, we present the development of an easily reproducible and reliable CAD tool using the Clinica and MONAI frameworks that were developed to introduce standardized practices in medical imaging. A Deep Learning (DL) algorithm was trained to detect frontotemporal dementia (FTD) on data from the NIFD database to ensure reproducibility. The DL model yielded 0.80 accuracy (95% confidence intervals: 0.64, 0.91), 1 sensitivity, 0.6 specificity, 0.83 F1-score, and 0.86 AUC, achieving a comparable performance with other FTD classification approaches. Explainable AI methods were applied to understand AI behavior and to identify regions of the images where the DL model misbehaves. Attention maps highlighted that its decision was driven by hallmarking brain areas for FTD and helped us to understand how to improve FTD detection. The proposed standardized methodology could be useful for benchmark comparison in FTD classification. AI-based CAD tools should be developed with the goal of standardizing pipelines, as varying pre-processing and training methods, along with the absence of model behavior explanations, negatively impact regulators' attitudes towards CAD. The adoption of common best practices for neuroimaging data analysis is a step toward fast evaluation of efficacy and safety of CAD and may accelerate the adoption of AI products in the healthcare system.
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Affiliation(s)
- Andrea Termine
- Data Science Unit, IRCCS Santa Lucia Foundation, 00143 Rome, Italy; (A.T.); (C.F.)
| | - Carlo Fabrizio
- Data Science Unit, IRCCS Santa Lucia Foundation, 00143 Rome, Italy; (A.T.); (C.F.)
| | - Carlo Caltagirone
- Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy;
| | - Laura Petrosini
- Experimental and Behavioral Neurophysiology, IRCCS Santa Lucia Foundation, 00143 Rome, Italy
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8
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Denissen S, Chén OY, De Mey J, De Vos M, Van Schependom J, Sima DM, Nagels G. Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis. J Pers Med 2021; 11:1349. [PMID: 34945821 PMCID: PMC8707909 DOI: 10.3390/jpm11121349] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/06/2021] [Accepted: 12/09/2021] [Indexed: 12/23/2022] Open
Abstract
Multiple sclerosis (MS) manifests heterogeneously among persons suffering from it, making its disease course highly challenging to predict. At present, prognosis mostly relies on biomarkers that are unable to predict disease course on an individual level. Machine learning is a promising technique, both in terms of its ability to combine multimodal data and through the capability of making personalized predictions. However, most investigations on machine learning for prognosis in MS were geared towards predicting physical deterioration, while cognitive deterioration, although prevalent and burdensome, remained largely overlooked. This review aims to boost the field of machine learning for cognitive prognosis in MS by means of an introduction to machine learning and its pitfalls, an overview of important elements for study design, and an overview of the current literature on cognitive prognosis in MS using machine learning. Furthermore, the review discusses new trends in the field of machine learning that might be adopted for future studies in the field.
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Affiliation(s)
- Stijn Denissen
- AIMS Laboratory, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, 1050 Brussels, Belgium; (J.D.M.); (J.V.S.); (D.M.S.); (G.N.)
- icometrix, 3012 Leuven, Belgium
| | - Oliver Y. Chén
- Faculty of Social Sciences and Law, University of Bristol, Bristol BS8 1QU, UK;
- Department of Engineering, University of Oxford, Oxford OX1 3PJ, UK
| | - Johan De Mey
- AIMS Laboratory, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, 1050 Brussels, Belgium; (J.D.M.); (J.V.S.); (D.M.S.); (G.N.)
- Department of Radiology, UZ Brussel, Vrije Universiteit Brussel, 1090 Brussels, Belgium
| | - Maarten De Vos
- Faculty of Engineering Science, KU Leuven, 3001 Leuven, Belgium;
- Faculty of Medicine, KU Leuven, 3001 Leuven, Belgium
| | - Jeroen Van Schependom
- AIMS Laboratory, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, 1050 Brussels, Belgium; (J.D.M.); (J.V.S.); (D.M.S.); (G.N.)
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, 1050 Brussels, Belgium
| | - Diana Maria Sima
- AIMS Laboratory, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, 1050 Brussels, Belgium; (J.D.M.); (J.V.S.); (D.M.S.); (G.N.)
- icometrix, 3012 Leuven, Belgium
| | - Guy Nagels
- AIMS Laboratory, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, 1050 Brussels, Belgium; (J.D.M.); (J.V.S.); (D.M.S.); (G.N.)
- icometrix, 3012 Leuven, Belgium
- St Edmund Hall, Queen’s Ln, Oxford OX1 4AR, UK
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9
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Voigt I, Inojosa H, Dillenseger A, Haase R, Akgün K, Ziemssen T. Digital Twins for Multiple Sclerosis. Front Immunol 2021; 12:669811. [PMID: 34012452 PMCID: PMC8128142 DOI: 10.3389/fimmu.2021.669811] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 04/16/2021] [Indexed: 12/16/2022] Open
Abstract
An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient's characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters - including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient's life circumstances and plans, and medical procedures - a digital twin paired to the patient's characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients' well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life.
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Affiliation(s)
| | | | | | | | | | - Tjalf Ziemssen
- Center of Clinical Neuroscience, Department of Neurology, University Hospital Carl Gustav Carus, Technical University of Dresden, Dresden, Germany
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10
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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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11
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Hwang YM, Kim JH, Kim YR. Comparison of Mobile Application-Based ECG Consultation by Collective Intelligence and ECG Interpretation by Conventional System in a Tertiary-Level Hospital. Korean Circ J 2021; 51:351-357. [PMID: 33821585 PMCID: PMC8022025 DOI: 10.4070/kcj.2020.0364] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 11/16/2020] [Accepted: 12/08/2020] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND AND OBJECTIVES A mobile application (app)-based electrocardiogram (ECG) consultation system (InterMD Co., Ltd., Seoul, Korea) using the collective intelligence (CI) and the availability of large-scale digitized ECG data would extend the utility of ECGs beyond their current limitations, while at the same time preserving interpretability that remains critical to medical decision-making. METHODS We developed a new mobile app-based ECG consultation system by CI for general practitioners. We compared the responses of ECG reading between the mobile app-based CI system and the conventional system in a tertiary referring hospital. RESULTS We analyzed 376 consecutive ECGs between December 2017 and May 2019. Of these, 159 ECGs (42.3%) were interpreted by CI through the mobile app-based ECG consultation system and 217 ECGs (57.7%) were analyzed by cardiologists in the conventional systems based on electronic medical record data in a tertiary hospital. All ECG readings were confirmed by an electrophysiologist (EP). The time to an initial response by the CI system was faster than that of the conventional system (6.6 hours vs. 35.8 hours, p<0.0001). The number of responses of each ECG in CI system outnumbered those of the conventional system in the tertiary hospital (3.1 vs. 1.2, p<0.0001). The consensus of the ECG readings with EP was similar in both systems (98.6% vs. 100%, p=0.158). CONCLUSIONS The mobile app-based ECG consultation system by CI is as reliable method as the conventional referral system. It would expand the app of the 12-lead ECG with the collaboration of physicians in clinics and hospitals without time and space constraints.
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Affiliation(s)
- You Mi Hwang
- Department of Cardiology, St.Vincent's Hospital, The Catholic University of Korea, Seoul, Korea
| | - Ji Hyun Kim
- Division of Cardiology, Department of Internal Medicine, Dongguk University College of Medicine, Goyang, Korea
| | - Yoo Ri Kim
- Division of Cardiology, Department of Internal Medicine, Dongguk University College of Medicine, Goyang, Korea.
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12
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Seccia R, Gammelli D, Dominici F, Romano S, Landi AC, Salvetti M, Tacchella A, Zaccaria A, Crisanti A, Grassi F, Palagi L. Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis. PLoS One 2020; 15:e0230219. [PMID: 32196512 PMCID: PMC7083323 DOI: 10.1371/journal.pone.0230219] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 02/24/2020] [Indexed: 12/27/2022] Open
Abstract
Multiple Sclerosis (MS) progresses at an unpredictable rate, but predictions on the disease course in each patient would be extremely useful to tailor therapy to the individual needs. We explore different machine learning (ML) approaches to predict whether a patient will shift from the initial Relapsing-Remitting (RR) to the Secondary Progressive (SP) form of the disease, using only "real world" data available in clinical routine. The clinical records of 1624 outpatients (207 in the SP phase) attending the MS service of Sant'Andrea hospital, Rome, Italy, were used. Predictions at 180, 360 or 720 days from the last visit were obtained considering either the data of the last available visit (Visit-Oriented setting), comparing four classical ML methods (Random Forest, Support Vector Machine, K-Nearest Neighbours and AdaBoost) or the whole clinical history of each patient (History-Oriented setting), using a Recurrent Neural Network model, specifically designed for historical data. Missing values were handled by removing either all clinical records presenting at least one missing parameter (Feature-saving approach) or the 3 clinical parameters which contained missing values (Record-saving approach). The performances of the classifiers were rated using common indicators, such as Recall (or Sensitivity) and Precision (or Positive predictive value). In the visit-oriented setting, the Record-saving approach yielded Recall values from 70% to 100%, but low Precision (5% to 10%), which however increased to 50% when considering only predictions for which the model returned a probability above a given "confidence threshold". For the History-oriented setting, both indicators increased as prediction time lengthened, reaching values of 67% (Recall) and 42% (Precision) at 720 days. We show how "real world" data can be effectively used to forecast the evolution of MS, leading to high Recall values and propose innovative approaches to improve Precision towards clinically useful values.
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Affiliation(s)
- Ruggiero Seccia
- Dept. of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Daniele Gammelli
- Dept. of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Fabio Dominici
- Dept. of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Silvia Romano
- Dept. of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Anna Chiara Landi
- Dept. of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Marco Salvetti
- Dept. of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
- IRCCS Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Italy
| | - Andrea Tacchella
- Dept. of Physics, Istituto dei Sistemi Complessi (ISC)-CNR, UOS Sapienza, Rome, Italy
| | - Andrea Zaccaria
- Dept. of Physics, Istituto dei Sistemi Complessi (ISC)-CNR, UOS Sapienza, Rome, Italy
| | | | - Francesca Grassi
- Dept. of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Laura Palagi
- Dept. of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
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13
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Stafford IS, Kellermann M, Mossotto E, Beattie RM, MacArthur BD, Ennis S. A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases. NPJ Digit Med 2020; 3:30. [PMID: 32195365 PMCID: PMC7062883 DOI: 10.1038/s41746-020-0229-3] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 01/17/2020] [Indexed: 02/07/2023] Open
Abstract
Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.
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Affiliation(s)
- I. S. Stafford
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
- Institute for Life Sciences, University of Southampton, Southampton, UK
| | - M. Kellermann
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
| | - E. Mossotto
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
- Institute for Life Sciences, University of Southampton, Southampton, UK
| | - R. M. Beattie
- Department of Paediatric Gastroenterology, Southampton Children’s Hospital, Southampton, UK
| | - B. D. MacArthur
- Institute for Life Sciences, University of Southampton, Southampton, UK
| | - S. Ennis
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
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14
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El Naqa I, Haider MA, Giger ML, Ten Haken RK. Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century. Br J Radiol 2020; 93:20190855. [PMID: 31965813 PMCID: PMC7055429 DOI: 10.1259/bjr.20190855] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 01/12/2020] [Accepted: 01/13/2020] [Indexed: 12/15/2022] Open
Abstract
Advances in computing hardware and software platforms have led to the recent resurgence in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for automating complex tasks or providing superior predictive analytics. AI applications are currently spanning many diverse fields from economics to entertainment, to manufacturing, as well as medicine. Since modern AI's inception decades ago, practitioners in radiological sciences have been pioneering its development and implementation in medicine, particularly in areas related to diagnostic imaging and therapy. In this anniversary article, we embark on a journey to reflect on the learned lessons from past AI's chequered history. We further summarize the current status of AI in radiological sciences, highlighting, with examples, its impressive achievements and effect on re-shaping the practice of medical imaging and radiotherapy in the areas of computer-aided detection, diagnosis, prognosis, and decision support. Moving beyond the commercial hype of AI into reality, we discuss the current challenges to overcome, for AI to achieve its promised hope of providing better precision healthcare for each patient while reducing cost burden on their families and the society at large.
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Affiliation(s)
- Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Masoom A Haider
- Department of Medical Imaging and Lunenfeld-Tanenbaum Research Institute, University of Toronto, Toronto, ON, Canada
| | | | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
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15
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Novaes Tump A, Wolf M, Krause J, Kurvers RHJM. Individuals fail to reap the collective benefits of diversity because of over-reliance on personal information. J R Soc Interface 2019; 15:rsif.2018.0155. [PMID: 29769409 DOI: 10.1098/rsif.2018.0155] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Accepted: 04/23/2018] [Indexed: 11/12/2022] Open
Abstract
Collective intelligence refers to the ability of groups to outperform individuals in solving cognitive tasks. Although numerous studies have demonstrated this effect, the mechanisms underlying collective intelligence remain poorly understood. Here, we investigate diversity in cue beliefs as a mechanism potentially promoting collective intelligence. In our experimental study, human groups observed a sequence of cartoon characters, and classified each character as a cooperator or defector based on informative and uninformative cues. Participants first made an individual decision. They then received social information consisting of their group members' decisions before making a second decision. Additionally, individuals reported their beliefs about the cues. Our results showed that individuals made better decisions after observing the decisions of others. Interestingly, individuals developed different cue beliefs, including many wrong ones, despite receiving identical information. Diversity in cue beliefs, however, did not predict collective improvement. Using simulations, we found that diverse collectives did provide better social information, but that individuals failed to reap those benefits because they relied too much on personal information. Our results highlight the potential of belief diversity for promoting collective intelligence, but suggest that this potential often remains unexploited because of over-reliance on personal information.
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Affiliation(s)
- Alan Novaes Tump
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - Max Wolf
- Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany
| | - Jens Krause
- Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany.,Faculty of Life Science, Albrecht Daniel Thaer-Institut, Humboldt University, Invalidenstraße 42, 10115 Berlin, Germany
| | - Ralf H J M Kurvers
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany.,Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany
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16
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Das R, Keep B, Washington P, Riedel-Kruse IH. Scientific Discovery Games for Biomedical Research. Annu Rev Biomed Data Sci 2019; 2:253-279. [PMID: 34308269 DOI: 10.1146/annurev-biodatasci-072018-021139] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Over the past decade, scientific discovery games (SDGs) have emerged as a viable approach for biomedical research, engaging hundreds of thousands of volunteer players and resulting in numerous scientific publications. After describing the origins of this novel research approach, we review the scientific output of SDGs across molecular modeling, sequence alignment, neuroscience, pathology, cellular biology, genomics, and human cognition. We find compelling results and technical innovations arising in problem-oriented games such as Foldit and Eterna and in data-oriented games such as EyeWire and Project Discovery. We discuss emergent properties of player communities shared across different projects, including the diversity of communities and the extraordinary contributions of some volunteers, such as paper writing. Finally, we highlight connections to artificial intelligence, biological cloud laboratories, new game genres, science education, and open science that may drive the next generation of SDGs.
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Affiliation(s)
- Rhiju Das
- Department of Biochemistry and Department of Physics, Stanford University, Stanford, California 94305, USA
| | - Benjamin Keep
- Department of Learning Sciences, Stanford University, Stanford, California 94305, USA
| | - Peter Washington
- Department of Bioengineering, Stanford University, Stanford, California 94305, USA
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17
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Bland JS. What is Evidence-Based Functional Medicine in the 21st Century? Integr Med (Encinitas) 2019; 18:14-18. [PMID: 32549804 PMCID: PMC7217393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The 21st century has already demonstrated itself to be an era of change for medicine and science. There is a new openness-to ideas, to a shift in perspectives, to a redefinition of evidence and the many ways it can be gathered. New interest in real-world data, patient-experience information has also become an increasingly important contributor to the evaluation of treatment effectiveness. It is a fertile time on many fronts, including an expanded reach for a systems biology formalism and the Functional Medicine movement.
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18
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Tacchella A, Romano S, Ferraldeschi M, Salvetti M, Zaccaria A, Crisanti A, Grassi F. Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study. F1000Res 2017; 6:2172. [PMID: 29904574 PMCID: PMC5990125 DOI: 10.12688/f1000research.13114.2] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/30/2018] [Indexed: 11/21/2022] Open
Abstract
Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen and generalize this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.
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Affiliation(s)
- Andrea Tacchella
- Institute for Complex Systems, National Research Council - UOS Sapienza, Rome, 00185, Italy
| | - Silvia Romano
- Center for Experimental Neurological Therapies (CENTERS), Dept. of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, 00189, Italy
| | - Michela Ferraldeschi
- Center for Experimental Neurological Therapies (CENTERS), Dept. of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, 00189, Italy
| | - Marco Salvetti
- Center for Experimental Neurological Therapies (CENTERS), Dept. of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, 00189, Italy.,IRCCS Neuromed , Istituto Neurologico Mediterraneo, Pozzilli, 86077, Italy
| | - Andrea Zaccaria
- Institute for Complex Systems, National Research Council - UOS Sapienza, Rome, 00185, Italy
| | - Andrea Crisanti
- Department of Physics, Sapienza University of Rome, Rome, 00185, Italy
| | - Francesca Grassi
- Institute Pasteur-Cenci Bolognetti Foundation, Dept. Physiology and Pharmacology, Sapienza University of Rome, Rome, 00185, Italy
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