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Droppelmann G, Rodríguez C, Jorquera C, Feijoo F. Artificial intelligence in diagnosing upper limb musculoskeletal disorders: a systematic review and meta-analysis of diagnostic tests. EFORT Open Rev 2024; 9:241-251. [PMID: 38579757 PMCID: PMC11044087 DOI: 10.1530/eor-23-0174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/07/2024] Open
Abstract
Purpose The integration of artificial intelligence (AI) in radiology has revolutionized diagnostics, optimizing precision and decision-making. Specifically in musculoskeletal imaging, AI tools can improve accuracy for upper extremity pathologies. This study aimed to assess the diagnostic performance of AI models in detecting musculoskeletal pathologies of the upper extremity using different imaging modalities. Methods A meta-analysis was conducted, involving searches on MEDLINE/PubMed, SCOPUS, Cochrane Library, Lilacs, and SciELO. The quality of the studies was assessed using the QUADAS-2 tool. Diagnostic accuracy measures including sensitivity, specificity, diagnostic odds ratio (DOR), positive and negative likelihood ratios (PLR, NLR), area under the curve (AUC), and summary receiver operating characteristic were pooled using a random-effects model. Heterogeneity and subgroup analyses were also included. All statistical analyses and plots were performed using the R software package. Results Thirteen models from ten articles were analyzed. The sensitivity and specificity of the AI models to detect musculoskeletal conditions in the upper extremity were 0.926 (95% CI: 0.900; 0.945) and 0.908 (95% CI: 0.810; 0.958). The PLR, NLR, lnDOR, and the AUC estimates were found to be 19.18 (95% CI: 8.90; 29.34), 0.11 (95% CI: 0.18; 0.46), 4.62 (95% CI: 4.02; 5.22) with a (P < 0.001), and 95%, respectively. Conclusion The AI models exhibited strong univariate and bivariate performance in detecting both positive and negative cases within the analyzed dataset of musculoskeletal pathologies in the upper extremity.
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Affiliation(s)
- Guillermo Droppelmann
- Research Center on Medicine, Exercise, Sport and Health, MEDS Clinic, Santiago, RM, Chile
- Health Sciences PhD Program, Universidad Católica de Murcia UCAM, Murcia, Spain
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | | | - Carlos Jorquera
- Facultad de Ciencias, Escuela de Nutrición y Dietética, Universidad Mayor, Santiago, RM, Chile
| | - Felipe Feijoo
- School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
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2
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Deschênes MF, Fernandez N, Lechasseur K, Caty MÈ, Azimzadeh D, Mai TC, Lavoie P. Transformation and Articulation of Clinical Data to Understand Students' and Health Professionals' Clinical Reasoning: Protocol for a Scoping Review. JMIR Res Protoc 2023; 12:e50797. [PMID: 38090795 PMCID: PMC10753415 DOI: 10.2196/50797] [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: 07/12/2023] [Revised: 11/02/2023] [Accepted: 11/23/2023] [Indexed: 12/30/2023] Open
Abstract
BACKGROUND There are still unanswered questions regarding effective educational strategies to promote the transformation and articulation of clinical data while teaching and learning clinical reasoning. Additionally, understanding how this process can be analyzed and assessed is crucial, particularly considering the rapid growth of natural language processing in artificial intelligence. OBJECTIVE The aim of this study is to map educational strategies to promote the transformation and articulation of clinical data among students and health care professionals and to explore the methods used to assess these individuals' transformation and articulation of clinical data. METHODS This scoping review follows the Joanna Briggs Institute framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist for the analysis. A literature search was performed in November 2022 using 5 databases: CINAHL (EBSCOhost), MEDLINE (Ovid), Embase (Ovid), PsycINFO (Ovid), and Web of Science (Clarivate). The protocol was registered on the Open Science Framework in November 2023. The scoping review will follow the 9-step framework proposed by Peters and colleagues of the Joanna Briggs Institute. A data extraction form has been developed using key themes from the research questions. RESULTS After removing duplicates, the initial search yielded 6656 results, and study selection is underway. The extracted data will be qualitatively analyzed and presented in a diagrammatic or tabular form alongside a narrative summary. The review will be completed by February 2024. CONCLUSIONS By synthesizing the evidence on semantic transformation and articulation of clinical data during clinical reasoning education, this review aims to contribute to the refinement of educational strategies and assessment methods used in academic and continuing education programs. The insights gained from this review will help educators develop more effective semantic approaches for teaching or learning clinical reasoning, as opposed to fragmented, purely symptom-based or probabilistic approaches. Besides, the results may suggest some ways to address challenges related to the assessment of clinical reasoning and ensure that the assessment tasks accurately reflect learners' developing competencies and educational progress. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/50797.
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Affiliation(s)
| | | | | | - Marie-Ève Caty
- Département d'orthophonie, Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada
| | - Dina Azimzadeh
- Faculté des sciences infirmières, Université de Montréal, Montréal, QC, Canada
| | - Tue-Chieu Mai
- Faculté des sciences infirmières, Université de Montréal, Montréal, QC, Canada
| | - Patrick Lavoie
- Faculté des sciences infirmières, Université de Montréal, Montreal, QC, QC, Canada
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3
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Grassi G, Laino ME, Fantini MC, Argiolas GM, Cherchi MV, Nicola R, Gerosa C, Cerrone G, Mannelli L, Balestrieri A, Suri JS, Carriero A, Saba L. Advanced imaging and Crohn’s disease: An overview of clinical application and the added value of artificial intelligence. Eur J Radiol 2022; 157:110551. [DOI: 10.1016/j.ejrad.2022.110551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 09/23/2022] [Accepted: 09/27/2022] [Indexed: 11/03/2022]
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4
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Cowan RP, Rapoport AM, Blythe J, Rothrock J, Knievel K, Peretz AM, Ekpo E, Sanjanwala BM, Woldeamanuel YW. Diagnostic accuracy of an artificial intelligence online engine in migraine: A multi‐center study. Headache 2022; 62:870-882. [PMID: 35657603 PMCID: PMC9378575 DOI: 10.1111/head.14324] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 04/18/2022] [Accepted: 04/21/2022] [Indexed: 11/28/2022]
Abstract
Objective This study assesses the concordance in migraine diagnosis between an online, self‐administered, Computer‐based, Diagnostic Engine (CDE) and semi‐structured interview (SSI) by a headache specialist, both using International Classification of Headache Disorders, 3rd edition (ICHD‐3) criteria. Background Delay in accurate diagnosis is a major barrier to headache care. Accurate computer‐based algorithms may help reduce the need for SSI‐based encounters to arrive at correct ICHD‐3 diagnosis. Methods Between March 2018 and August 2019, adult participants were recruited from three academic headache centers and the community via advertising to our cross‐sectional study. Participants completed two evaluations: phone interview conducted by headache specialists using the SSI and a web‐based expert questionnaire and analytics, CDE. Participants were randomly assigned to either the SSI followed by the web‐based questionnaire or the web‐based questionnaire followed by the SSI. Participants completed protocols a few minutes apart. The concordance in migraine/probable migraine (M/PM) diagnosis between SSI and CDE was measured using Cohen’s kappa statistics. The diagnostic accuracy of CDE was assessed using the SSI as reference standard. Results Of the 276 participants consented, 212 completed both SSI and CDE (study completion rate = 77%; median age = 32 years [interquartile range: 28–40], female:male ratio = 3:1). Concordance in M/PM diagnosis between SSI and CDE was: κ = 0.83 (95% confidence interval [CI]: 0.75–0.91). CDE diagnostic accuracy: sensitivity = 90.1% (118/131), 95% CI: 83.6%–94.6%; specificity = 95.8% (68/71), 95% CI: 88.1%–99.1%. Positive and negative predictive values = 97.0% (95% CI: 91.3%–99.0%) and 86.6% (95% CI: 79.3%–91.5%), respectively, using identified migraine prevalence of 60%. Assuming a general migraine population prevalence of 10%, positive and negative predictive values were 70.3% (95% CI: 43.9%–87.8%) and 98.9% (95% CI: 98.1%–99.3%), respectively. Conclusion The SSI and CDE have excellent concordance in diagnosing M/PM. Positive CDE helps rule in M/PM, through high specificity and positive likelihood ratio. A negative CDE helps rule out M/PM through high sensitivity and low negative likelihood ratio. CDE that mimics SSI logic is a valid tool for migraine diagnosis.
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Affiliation(s)
- Robert P. Cowan
- Division of Headache and Facial Pain, Department of Neurology and Neurological Sciences Stanford University School of Medicine Stanford California USA
| | | | - Jim Blythe
- Information Sciences Institute University of Southern California Los Angeles California USA
| | - John Rothrock
- Neurology The George Washington University School of Medicine and Health Sciences Washington District of Columbia USA
| | - Kerry Knievel
- Neurology Barrow Neurological Institute Phoenix Arizona USA
| | - Addie M. Peretz
- Division of Headache and Facial Pain, Department of Neurology and Neurological Sciences Stanford University School of Medicine Stanford California USA
| | - Elizabeth Ekpo
- Neurology University of California Davis Davis California USA
| | - Bharati M. Sanjanwala
- Division of Headache and Facial Pain, Department of Neurology and Neurological Sciences Stanford University School of Medicine Stanford California USA
| | - Yohannes W. Woldeamanuel
- Division of Headache and Facial Pain, Department of Neurology and Neurological Sciences Stanford University School of Medicine Stanford California USA
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Auconi P, Gili T, Capuani S, Saccucci M, Caldarelli G, Polimeni A, Di Carlo G. The Validity of Machine Learning Procedures in Orthodontics: What Is Still Missing? J Pers Med 2022; 12:jpm12060957. [PMID: 35743742 PMCID: PMC9225071 DOI: 10.3390/jpm12060957] [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: 04/21/2022] [Revised: 05/31/2022] [Accepted: 06/05/2022] [Indexed: 11/16/2022] Open
Abstract
Artificial intelligence (AI) models and procedures hold remarkable predictive efficiency in the medical domain through their ability to discover hidden, non-obvious clinical patterns in data. However, due to the sparsity, noise, and time-dependency of medical data, AI procedures are raising unprecedented issues related to the mismatch between doctors’ mentalreasoning and the statistical answers provided by algorithms. Electronic systems can reproduce or even amplify noise hidden in the data, especially when the diagnosis of the subjects in the training data set is inaccurate or incomplete. In this paper we describe the conditions that need to be met for AI instruments to be truly useful in the orthodontic domain. We report some examples of computational procedures that are capable of extracting orthodontic knowledge through ever deeper patient representation. To have confidence in these procedures, orthodontic practitioners should recognize the benefits, shortcomings, and unintended consequences of AI models, as algorithms that learn from human decisions likewise learn mistakes and biases.
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Affiliation(s)
- Pietro Auconi
- Private Practice of Orthodontics, 00012 Rome, Italy;
| | - Tommaso Gili
- Networks Unit, IMT School for Advanced Studies Lucca, Piazza San Francesco 19, 55100 Lucca, Italy
- ISC CNR, Department of Physics, University of Rome “Sapienza”, P.le Aldo Moro 5, 00185 Rome, Italy; (S.C.); (G.C.)
- Correspondence:
| | - Silvia Capuani
- ISC CNR, Department of Physics, University of Rome “Sapienza”, P.le Aldo Moro 5, 00185 Rome, Italy; (S.C.); (G.C.)
| | - Matteo Saccucci
- Department of Oral and Maxillo-Facial Sciences, Sapienza University of Rome, Viale Regina Elena 287a, 00161 Rome, Italy; (M.S.); (A.P.); (G.D.C.)
| | - Guido Caldarelli
- ISC CNR, Department of Physics, University of Rome “Sapienza”, P.le Aldo Moro 5, 00185 Rome, Italy; (S.C.); (G.C.)
- Department of Molecular Sciences and Nanosystems, Ca’Foscari University of Venice, Via Torino 155, Venezia Mestre, 30172 Venice, Italy
- ECLT, Ca’ Bottacin, Dorsoduro 3246, 30123 Venice, Italy
| | - Antonella Polimeni
- Department of Oral and Maxillo-Facial Sciences, Sapienza University of Rome, Viale Regina Elena 287a, 00161 Rome, Italy; (M.S.); (A.P.); (G.D.C.)
| | - Gabriele Di Carlo
- Department of Oral and Maxillo-Facial Sciences, Sapienza University of Rome, Viale Regina Elena 287a, 00161 Rome, Italy; (M.S.); (A.P.); (G.D.C.)
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“I’m afraid I can’t let you do that, Doctor”: meaningful disagreements with AI in medical contexts. AI & SOCIETY 2022. [DOI: 10.1007/s00146-022-01418-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
AbstractThis paper explores the role and resolution of disagreements between physicians and their diagnostic AI-based decision support systems (DSS). With an ever-growing number of applications for these independently operating diagnostic tools, it becomes less and less clear what a physician ought to do in case their diagnosis is in faultless conflict with the results of the DSS. The consequences of such uncertainty can ultimately lead to effects detrimental to the intended purpose of such machines, e.g. by shifting the burden of proof towards a physician. Thus, we require normative clarity for integrating these machines without affecting established, trusted, and relied upon workflows. In reconstructing different causes of conflicts between physicians and their AI-based tools—inspired by the approach of “meaningful human control” over autonomous systems and the challenges to resolve them—we will delineate normative conditions for “meaningful disagreements”. These incorporate the potential of DSS to take on more tasks and outline how the moral responsibility of a physician can be preserved in an increasingly automated clinical work environment.
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7
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Majidova K, Handfield J, Kafi K, Martin RD, Kubinski R. Role of Digital Health and Artificial Intelligence in Inflammatory Bowel Disease: A Scoping Review. Genes (Basel) 2021; 12:1465. [PMID: 34680860 PMCID: PMC8535572 DOI: 10.3390/genes12101465] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 09/14/2021] [Indexed: 12/12/2022] Open
Abstract
Inflammatory bowel diseases (IBD), subdivided into Crohn's disease (CD) and ulcerative colitis (UC), are chronic diseases that are characterized by relapsing and remitting periods of inflammation in the gastrointestinal tract. In recent years, the amount of research surrounding digital health (DH) and artificial intelligence (AI) has increased. The purpose of this scoping review is to explore this growing field of research to summarize the role of DH and AI in the diagnosis, treatment, monitoring and prognosis of IBD. A review of 21 articles revealed the impact of both AI algorithms and DH technologies; AI algorithms can improve diagnostic accuracy, assess disease activity, and predict treatment response based on data modalities such as endoscopic imaging and genetic data. In terms of DH, patients utilizing DH platforms experienced improvements in quality of life, disease literacy, treatment adherence, and medication management. In addition, DH methods can reduce the need for in-person appointments, decreasing the use of healthcare resources without compromising the standard of care. These articles demonstrate preliminary evidence of the potential of DH and AI for improving the management of IBD. However, the majority of these studies were performed in a regulated clinical environment. Therefore, further validation of these results in a real-world environment is required to assess the efficacy of these methods in the general IBD population.
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Affiliation(s)
| | | | | | | | - Ryszard Kubinski
- Phyla Technologies Inc., Montréal, QC H3C 4J9, Canada; (K.M.); (J.H.); (K.K.); (R.D.M.)
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8
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Adamichou C, Genitsaridi I, Nikolopoulos D, Nikoloudaki M, Repa A, Bortoluzzi A, Fanouriakis A, Sidiropoulos P, Boumpas DT, Bertsias GK. Lupus or not? SLE Risk Probability Index (SLERPI): a simple, clinician-friendly machine learning-based model to assist the diagnosis of systemic lupus erythematosus. Ann Rheum Dis 2021. [DOI: 10.1136/annrheumdis-2020-219069
expr 893510318 + 842823336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
ObjectivesDiagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing diagnoses. We applied machine learning in well-characterised patient data sets to develop an algorithm that can aid SLE diagnosis.MethodsFrom a discovery cohort of randomly selected 802 adults with SLE or control rheumatologic diseases, clinically selected panels of deconvoluted classification criteria and non-criteria features were analysed. Feature selection and model construction were done with Random Forests and Least Absolute Shrinkage and Selection Operator-logistic regression (LASSO-LR). The best model in 10-fold cross-validation was tested in a validation cohort (512 SLE, 143 disease controls).ResultsA novel LASSO-LR model had the best performance and included 14 variably weighed features with thrombocytopenia/haemolytic anaemia, malar/maculopapular rash, proteinuria, low C3 and C4, antinuclear antibodies (ANA) and immunologic disorder being the strongest SLE predictors. Our model produced SLE risk probabilities (depending on the combination of features) correlating positively with disease severity and organ damage, and allowing the unbiased classification of a validation cohort into diagnostic certainty levels (unlikely, possible, likely, definitive SLE) based on the likelihood of SLE against other diagnoses. Operating the model as binary (lupus/not-lupus), we noted excellent accuracy (94.8%) for identifying SLE, and high sensitivity for early disease (93.8%), nephritis (97.9%), neuropsychiatric (91.8%) and severe lupus requiring immunosuppressives/biologics (96.4%). This was converted into a scoring system, whereby a score >7 has 94.2% accuracy.ConclusionsWe have developed and validated an accurate, clinician-friendly algorithm based on classical disease features for early SLE diagnosis and treatment to improve patient outcomes.
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9
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Kaplan A, Cao H, FitzGerald JM, Iannotti N, Yang E, Kocks JWH, Kostikas K, Price D, Reddel HK, Tsiligianni I, Vogelmeier CF, Pfister P, Mastoridis P. Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY-IN PRACTICE 2021; 9:2255-2261. [PMID: 33618053 DOI: 10.1016/j.jaip.2021.02.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 02/11/2021] [Accepted: 02/13/2021] [Indexed: 02/09/2023]
Abstract
Artificial intelligence (AI) and machine learning, a subset of AI, are increasingly used in medicine. AI excels at performing well-defined tasks, such as image recognition; for example, classifying skin biopsy lesions, determining diabetic retinopathy severity, and detecting brain tumors. This article provides an overview of the use of AI in medicine and particularly in respiratory medicine, where it is used to evaluate lung cancer images, diagnose fibrotic lung disease, and more recently is being developed to aid the interpretation of pulmonary function tests and the diagnosis of a range of obstructive and restrictive lung diseases. The development and validation of AI algorithms requires large volumes of well-structured data, and the algorithms must work with variable levels of data quality. It is important that clinicians understand how AI can function in the context of heterogeneous conditions such as asthma and chronic obstructive pulmonary disease where diagnostic criteria overlap, how AI use fits into everyday clinical practice, and how issues of patient safety should be addressed. AI has a clear role in providing support for doctors in the clinical workplace, but its relatively recent introduction means that confidence in its use still has to be fully established. Overall, AI is expected to play a key role in aiding clinicians in the diagnosis and management of respiratory diseases in the future, and it will be exciting to see the benefits that arise for patients and doctors from its use in everyday clinical practice.
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Affiliation(s)
- Alan Kaplan
- Family Physician Airways Group of Canada, University of Toronto, Toronto, Canada.
| | - Hui Cao
- Novartis Pharmaceuticals Corporation, East Hanover, NJ
| | - J Mark FitzGerald
- Division of Respiratory Medicine, Department of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Nick Iannotti
- Novartis Institutes for Biomedical Research, Cambridge, Mass
| | - Eric Yang
- Novartis Institutes for Biomedical Research, Cambridge, Mass
| | - Janwillem W H Kocks
- General Practitioners Research Institute, Groningen, The Netherlands; University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands; Observational and Pragmatic Research Institute, Singapore, Singapore
| | - Konstantinos Kostikas
- Respiratory Medicine Department, University of Ioannina School of Medicine, Ioannina, Greece
| | - David Price
- Observational and Pragmatic Research Institute, Singapore, Singapore; Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | - Helen K Reddel
- Woolcock Institute of Medical Research, University of Sydney, Sydney, NSW, Australia
| | - Ioanna Tsiligianni
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Greece
| | - Claus F Vogelmeier
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps-Universität Marburg, Member of the German Center for Lung Research (DZL), Marburg, Germany
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10
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Adamichou C, Genitsaridi I, Nikolopoulos D, Nikoloudaki M, Repa A, Bortoluzzi A, Fanouriakis A, Sidiropoulos P, Boumpas DT, Bertsias GK. Lupus or not? SLE Risk Probability Index (SLERPI): a simple, clinician-friendly machine learning-based model to assist the diagnosis of systemic lupus erythematosus. Ann Rheum Dis 2021; 80:758-766. [PMID: 33568388 PMCID: PMC8142436 DOI: 10.1136/annrheumdis-2020-219069] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 11/23/2020] [Accepted: 12/09/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVES Diagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing diagnoses. We applied machine learning in well-characterised patient data sets to develop an algorithm that can aid SLE diagnosis. METHODS From a discovery cohort of randomly selected 802 adults with SLE or control rheumatologic diseases, clinically selected panels of deconvoluted classification criteria and non-criteria features were analysed. Feature selection and model construction were done with Random Forests and Least Absolute Shrinkage and Selection Operator-logistic regression (LASSO-LR). The best model in 10-fold cross-validation was tested in a validation cohort (512 SLE, 143 disease controls). RESULTS A novel LASSO-LR model had the best performance and included 14 variably weighed features with thrombocytopenia/haemolytic anaemia, malar/maculopapular rash, proteinuria, low C3 and C4, antinuclear antibodies (ANA) and immunologic disorder being the strongest SLE predictors. Our model produced SLE risk probabilities (depending on the combination of features) correlating positively with disease severity and organ damage, and allowing the unbiased classification of a validation cohort into diagnostic certainty levels (unlikely, possible, likely, definitive SLE) based on the likelihood of SLE against other diagnoses. Operating the model as binary (lupus/not-lupus), we noted excellent accuracy (94.8%) for identifying SLE, and high sensitivity for early disease (93.8%), nephritis (97.9%), neuropsychiatric (91.8%) and severe lupus requiring immunosuppressives/biologics (96.4%). This was converted into a scoring system, whereby a score >7 has 94.2% accuracy. CONCLUSIONS We have developed and validated an accurate, clinician-friendly algorithm based on classical disease features for early SLE diagnosis and treatment to improve patient outcomes.
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Affiliation(s)
- Christina Adamichou
- Rheumatology, Clinical Immunology and Allergy, University of Crete School of Medicine, Heraklion, Crete, Greece
| | - Irini Genitsaridi
- Rheumatology, Clinical Immunology and Allergy, University of Crete School of Medicine, Heraklion, Crete, Greece
| | - Dionysis Nikolopoulos
- Rheumatology and Clinical Immunology Unit, 4th Department of Internal Medicine, Attikon University Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Myrto Nikoloudaki
- Rheumatology, Clinical Immunology and Allergy, University of Crete School of Medicine, Heraklion, Crete, Greece
| | - Argyro Repa
- Rheumatology, Clinical Immunology and Allergy, University of Crete School of Medicine, Heraklion, Crete, Greece
| | - Alessandra Bortoluzzi
- Section of Rheumatology, Department of Medical Sciences, Azienda Ospedaliero Universitaria di Ferrara Arcispedale Sant'Anna, Cona, Emilia-Romagna, Italy
| | - Antonis Fanouriakis
- Rheumatology and Clinical Immunology Unit, 4th Department of Internal Medicine, Attikon University Hospital, National and Kapodistrian University of Athens, Athens, Greece.,Rheumatology, "Asklepieion" General Hospital, Athens, Greece
| | - Prodromos Sidiropoulos
- Rheumatology, Clinical Immunology and Allergy, University of Crete School of Medicine, Heraklion, Crete, Greece.,Institute of Molecular Biology and Biotechnology, Foundation of Research and Technology-Hellas, Heraklion, Crete, Greece
| | - Dimitrios T Boumpas
- Rheumatology and Clinical Immunology Unit, 4th Department of Internal Medicine, Attikon University Hospital, National and Kapodistrian University of Athens, Athens, Greece.,Laboratory of Immune Regulation and Tolerance, Autoimmunity and Inflammation, Biomedical Research Foundation of the Academy of Athens, Athens, Attica, Greece
| | - George K Bertsias
- Rheumatology, Clinical Immunology and Allergy, University of Crete School of Medicine, Heraklion, Crete, Greece .,Institute of Molecular Biology and Biotechnology, Foundation of Research and Technology-Hellas, Heraklion, Crete, Greece
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11
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Leung YW, Wouterloot E, Adikari A, Hirst G, de Silva D, Wong J, Bender JL, Gancarz M, Gratzer D, Alahakoon D, Esplen MJ. Natural Language Processing-Based Virtual Cofacilitator for Online Cancer Support Groups: Protocol for an Algorithm Development and Validation Study. JMIR Res Protoc 2021; 10:e21453. [PMID: 33410754 PMCID: PMC7819785 DOI: 10.2196/21453] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 09/04/2020] [Accepted: 11/24/2020] [Indexed: 02/02/2023] Open
Abstract
Background Cancer and its treatment can significantly impact the short- and long-term psychological well-being of patients and families. Emotional distress and depressive symptomatology are often associated with poor treatment adherence, reduced quality of life, and higher mortality. Cancer support groups, especially those led by health care professionals, provide a safe place for participants to discuss fear, normalize stress reactions, share solidarity, and learn about effective strategies to build resilience and enhance coping. However, in-person support groups may not always be accessible to individuals; geographic distance is one of the barriers for access, and compromised physical condition (eg, fatigue, pain) is another. Emerging evidence supports the effectiveness of online support groups in reducing access barriers. Text-based and professional-led online support groups have been offered by Cancer Chat Canada. Participants join the group discussion using text in real time. However, therapist leaders report some challenges leading text-based online support groups in the absence of visual cues, particularly in tracking participant distress. With multiple participants typing at the same time, the nuances of the text messages or red flags for distress can sometimes be missed. Recent advances in artificial intelligence such as deep learning–based natural language processing offer potential solutions. This technology can be used to analyze online support group text data to track participants’ expressed emotional distress, including fear, sadness, and hopelessness. Artificial intelligence allows session activities to be monitored in real time and alerts the therapist to participant disengagement. Objective We aim to develop and evaluate an artificial intelligence–based cofacilitator prototype to track and monitor online support group participants’ distress through real-time analysis of text-based messages posted during synchronous sessions. Methods An artificial intelligence–based cofacilitator will be developed to identify participants who are at-risk for increased emotional distress and track participant engagement and in-session group cohesion levels, providing real-time alerts for therapist to follow-up; generate postsession participant profiles that contain discussion content keywords and emotion profiles for each session; and automatically suggest tailored resources to participants according to their needs. The study is designed to be conducted in 4 phases consisting of (1) development based on a subset of data and an existing natural language processing framework, (2) performance evaluation using human scoring, (3) beta testing, and (4) user experience evaluation. Results This study received ethics approval in August 2019. Phase 1, development of an artificial intelligence–based cofacilitator, was completed in January 2020. As of December 2020, phase 2 is underway. The study is expected to be completed by September 2021. Conclusions An artificial intelligence–based cofacilitator offers a promising new mode of delivery of person-centered online support groups tailored to individual needs. International Registered Report Identifier (IRRID) DERR1-10.2196/21453
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Affiliation(s)
- Yvonne W Leung
- de Souza Institute, University Health Network, Toronto, ON, Canada.,Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Elise Wouterloot
- de Souza Institute, University Health Network, Toronto, ON, Canada
| | - Achini Adikari
- Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia
| | - Graeme Hirst
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Daswin de Silva
- Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia
| | - Jiahui Wong
- de Souza Institute, University Health Network, Toronto, ON, Canada.,Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jacqueline L Bender
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Mathew Gancarz
- de Souza Institute, University Health Network, Toronto, ON, Canada
| | - David Gratzer
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Damminda Alahakoon
- Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia
| | - Mary Jane Esplen
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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12
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Gratzer D, Torous J, Lam RW, Patten SB, Kutcher S, Chan S, Vigo D, Pajer K, Yatham LN. Our Digital Moment: Innovations and Opportunities in Digital Mental Health Care. CANADIAN JOURNAL OF PSYCHIATRY. REVUE CANADIENNE DE PSYCHIATRIE 2021; 66:5-8. [PMID: 32603188 PMCID: PMC7890581 DOI: 10.1177/0706743720937833] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- David Gratzer
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Ontario, Canada
| | - John Torous
- Department of Psychiatry, 1859Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Scott B Patten
- Departments of Psychiatry and Community Health Sciences, 2129University of Calgary, Alberta, Canada
| | - Stanley Kutcher
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada.,Senate of Canada, Ottawa, Ontario, Canada
| | - Steven Chan
- Department of Psychiatry, Stanford University School of Medicine, Stanford, CA, USA.,Palo Alto VA Health, Palo Alto, CA, USA
| | - Daniel Vigo
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | - Kathleen Pajer
- Department of Psychiatry, University of Ottawa, Ontario, Canada
| | - Lakshmi N Yatham
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
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13
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Affiliation(s)
- David M Burns
- Orthopedic surgery resident, University of Toronto, Toronto, Ont
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14
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Oostendorp RAB, Elvers JWH, van Trijffel E, Rutten GM, Scholten–Peeters GGM, Heijmans M, Hendriks E, Mikolajewska E, De Kooning M, Laekeman M, Nijs J, Roussel N, Samwel H. Relationships Between Context, Process, and Outcome Indicators to Assess Quality of Physiotherapy Care in Patients with Whiplash-Associated Disorders: Applying Donabedian's Model of Care. Patient Prefer Adherence 2020; 14:425-442. [PMID: 32184572 PMCID: PMC7060032 DOI: 10.2147/ppa.s234800] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 01/28/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Quality indicators (QIs) are measurable elements of practice performance and may relate to context, process, outcome and structure. A valid set of QIs have been developed, reflecting the clinical reasoning used in primary care physiotherapy for patients with whiplash-associated disorders (WAD). Donabedian's model postulates relationships between the constructs of quality of care, acting in a virtuous circle. AIM To explore the relative strengths of the relationships between context, process, and outcome indicators in the assessment of primary care physiotherapy in patients with WAD. MATERIALS AND METHODS Data on WAD patients (N=810) were collected over a period of 16 years in primary care physiotherapy practices by means of patients records. This routinely collected dataset (RCD-WAD) was classified in context, process, and outcome variables and analyzed retrospectively. Clinically relevant variables were selected based on expert consensus. Associations were expressed, using zero-order, as Spearman rank correlation coefficients (criterion: rs >0.25 [minimum: fair]; α-value = 0.05). RESULTS In round 1, 62 of 85 (72.9%) variables were selected by an expert panel as relevant for clinical reasoning; in round 2, 34 of 62 (54.8%) (context variables 9 of 18 [50.0%]; process variables 18 of 34 [52.9]; outcome variables 8 of 10 [90.0%]) as highly relevant. Associations between the selected context and process variables ranged from 0.27 to 0.53 (p≤0.00), between selected context and outcome variables from 0.26 to 0.55 (p≤0.00), and between selected process and outcome variables from 0.29 to 0.59 (p≤0.00). Moderate associations (rs >0.50; p≤0.00) were found between "pain coping" and "fear avoidance" as process variables, and "pain intensity" and "functioning" as outcome variables. CONCLUSION The identified associations between selected context, process, and outcome variables were fair to moderate. Ongoing work may clarify some of these associations and provide guidance to physiotherapists on how best to improve the quality of clinical reasoning in terms of relationships between context, process, and outcome in the management of patients with WAD.
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Affiliation(s)
- Rob A B Oostendorp
- Scientific Institute for Quality of Healthcare, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
- Department of Manual Therapy, Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Belgium
- Pain in Motion International Research Group, Department of Physiotherapy, Human Physiology and Anatomy, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, Brussels, Belgium
- Practice Physiotherapy and Manual Therapy, Heeswijk-Dinther, the Netherlands
| | - J W Hans Elvers
- Department of Public Health and Research, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
- Methodological Health-Skilled Institute, Beuningen, the Netherlands
| | - Emiel van Trijffel
- SOMT University of Physiotherapy, Amersfoort, the Netherlands
- Department of Physiotherapy, Human Physiology and Anatomy, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, Brussels, Belgium
| | - Geert M Rutten
- Institute of Health Studies, Faculty of Health and Social Studies, HAN University of Applied Science, Nijmegen, the Netherlands
- Faculty of Science and Engineering, Maastricht University, Maastricht, the Netherlands
| | - Gwendolyne G M Scholten–Peeters
- Department of Human Movement Sciences, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Free University Amsterdam, Amsterdam Movement Sciences, Amsterdam, the Netherlands
| | - Marcel Heijmans
- Practice Physiotherapy and Manual Therapy, Heeswijk-Dinther, the Netherlands
| | - Erik Hendriks
- Department of Epidemiology, Center of Evidence Based Physiotherapy, Maastricht University, Maastricht, the Netherlands
- Practice Physiotherapy ‘Klepperheide’, Druten, the Netherlands
| | - Emilia Mikolajewska
- Department of Physiotherapy, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus Univerisity, Toruń, Poland
- Neurocognitive Laboratory, Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń, Poland
| | - Margot De Kooning
- Pain in Motion International Research Group, Department of Physiotherapy, Human Physiology and Anatomy, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, Brussels, Belgium
- Department of Physical Medicine and Physiotherapy, University Hospital Brussels, Brussels, Belgium
| | - Marjan Laekeman
- Department of Nursing Sciences, Ph.D.-Kolleg, Faculty of Health, University Witten/Herdecke, Witten, Germany
| | - Jo Nijs
- Pain in Motion International Research Group, Department of Physiotherapy, Human Physiology and Anatomy, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, Brussels, Belgium
- Department of Physical Medicine and Physiotherapy, University Hospital Brussels, Brussels, Belgium
| | - Nathalie Roussel
- Department of Physiotherapy and Rehabilitation Sciences (MOVANT), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Han Samwel
- Revalis Pain Rehabilitation Centre, ‘s Hertogenbosch, the Netherlands
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15
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Pelaccia T, Forestier G, Wemmert C. Une intelligence artificielle raisonne-t-elle de la même façon que les cliniciens pour poser des diagnostics ? Rev Med Interne 2020; 41:192-195. [DOI: 10.1016/j.revmed.2019.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 12/23/2019] [Indexed: 11/26/2022]
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16
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Schmidt N. Mechanizing medicine - Tomorrows history started yesterday. Am J Surg 2020; 219:813-815. [PMID: 31902524 DOI: 10.1016/j.amjsurg.2019.12.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
History is by nature a retrospective subject, there usually being an interval between any event, a review or impact of the subject being considered. This NPSA Historian's paper, takes a long and quick historical view of influences that fostered changes resulting in the current state of affairs in the field of medicine and medical care. The fields of medicine and surgery, are undergoing rapid changes as a result of technological and other advances that are making tomorrow's medical history seemingly happening yesterday. Prospectively, the impact of current change and its rapidity has the potential to radically change the future practice of the art and craft of our profession.
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Affiliation(s)
- Nis Schmidt
- Emeritus Professor Clinical Surgery, Faculty of Medicne, University of British Columbia, Canada.
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