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Perronne L, Binvignat M, Foulquier N, Saraux A, Laredo JD, de Margerie-Mellon C, Fournier L, Sellam J. Algorithmic approaches in hand imaging for rheumatic musculoskeletal diseases: A systematic literature review. Semin Arthritis Rheum 2025; 73:152750. [PMID: 40349420 DOI: 10.1016/j.semarthrit.2025.152750] [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: 03/05/2025] [Revised: 04/18/2025] [Accepted: 04/24/2025] [Indexed: 05/14/2025]
Abstract
OBJECTIVE This systematic literature review provides a comprehensive overview of the use of machine learning (ML) in hand imaging of rheumatic musculoskeletal diseases (RMDs). The review evaluates ML algorithms, imaging modalities, patient populations, validation methods, and areas for improvement. METHODS The review was conducted following PRISMA guidelines and registered with PROSPERO. Articles were retrieved from PubMed, EMBASE, and Scopus using relevant MeSH terms and keywords. The search, executed in October 2024, was conducted manually and with BiBot, an AI-based tool for literature reviews. Studies focusing on ML applications in osteoarthritis (OA), rheumatoid arthritis (RA), and psoriatic arthritis (PsA) were included. RESULTS From 400 initially identified studies, 32 met the inclusion criteria. RA was the most studied disease (88 %), followed by OA (22 %) and PsA (9 %). Convolutional neural networks (CNNs) were the most frequently used algorithms (50 %). Standard radiographs (59 %) were the predominant imaging modality, followed by MRI (16 %). Despite recommendations for ML studies, external validation was conducted in only 15 % of studies, and just 6 % of datasets were publicly available. Interpretability tools were employed in 28 % of studies to enhance clinical relevance. CONCLUSION ML has significant potential to improve diagnostics and disease management in hand imaging of RMDs. However, key challenges remain, including the need for increased external validation, broader disease coverage (OA and PsA), and improved data-sharing practices to enhance reproducibility and clinical adoption.
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Affiliation(s)
- Laetitia Perronne
- PARCC UMRS 970, INSERM, Paris, France; Quantitative Imaging Core Lab, Northwestern University Feinberg School of Medicine, 676 North Saint Clair Street, Suite 800, Chicago, IL 60611, USA.
| | - Marie Binvignat
- Immunology, Immunopathology, Immunotherapy I3 Lab, INSERM UMRS-959, Sorbonne Université, Paris, France; Department of Rheumatology, Saint-Antoine Hospital, Assistance Publique-Hopitaux de Paris, Sorbonne Université; Centre de Recherche Saint-Antoine, Inserm UMRS_938, 184 rue du Faubourg Saint-Antoine, 75012 Paris, France
| | - Nathan Foulquier
- LBAI, UMR1227, INSERM, University of Western Brittany, Brest France and Centre Hospitalier Universitaire de Brest, Brest, France
| | - Alain Saraux
- Université de Bretagne Occidentale (Univ Brest), Department of Rheumatology; Pôle PHARES, CHU Brest, INSERM (U1227), LabEx IGO, Brest, France 29200 Brest, France
| | - Jean Denis Laredo
- Assistance Publique-Hôpitaux de Paris, Hôpital Lariboisière, Service de Chirurgie Orthopédique Et Traumatologique, 75010 Paris, France
| | | | - Laure Fournier
- PARCC UMRS 970, INSERM, Paris, France; Université Paris Cité, AP-HP, Hopital européen Georges Pompidou, Paris, France
| | - Jérémie Sellam
- Department of Rheumatology, Saint-Antoine Hospital, Assistance Publique-Hopitaux de Paris, Sorbonne Université; Centre de Recherche Saint-Antoine, Inserm UMRS_938, 184 rue du Faubourg Saint-Antoine, 75012 Paris, France
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Omar M, Agbareia R, Naffaa ME, Watad A, Glicksberg BS, Nadkarni GN, Klang E. Applications of Artificial Intelligence in Vasculitides: A Systematic Review. ACR Open Rheumatol 2025; 7:e70016. [PMID: 40091457 PMCID: PMC11911543 DOI: 10.1002/acr2.70016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2024] [Revised: 01/05/2025] [Accepted: 02/03/2025] [Indexed: 03/19/2025] Open
Abstract
OBJECTIVE Vasculitides are rare inflammatory disorders that sometimes can be difficult to diagnose due to their diverse presentations. This review examines the use of artificial intelligence (AI) to improve diagnosis and outcome prediction in vasculitis. METHODS A systematic search of PubMed, Embase, Web of Science, Institute of Electrical and Electronics Engineers Xplore, and Scopus identified relevant studies from 2000 to 2024. AI applications were categorized by data type (clinical, imaging, textual) and by task (diagnosis or prediction). Studies were assessed for risk of bias using the Prediction Model Risk of Bias Assessment Tool and Quality Assessment of Diagnostic Accuracy Studies-2. RESULTS A total of 46 studies were included. AI models achieved high diagnostic performance in Kawasaki disease, with sensitivities up to 92.5% and specificities up to 97.3%. Predictive models for complications, such as intravenous Ig resistance in Kawasaki disease, showed areas under the curves between 0.716 and 0.834. Other vasculitis types, especially those using imaging data, were less studied and often limited by small datasets. CONCLUSION The current literature shows that AI algorithms can enhance vasculitis diagnosis and prediction, with deep- and machine-learning models showing promise in Kawasaki disease. However, broader datasets, more external validation, and the integration of newer models like large language models are needed to advance their clinical applicability across different vasculitis types.
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Affiliation(s)
- Mahmud Omar
- Icahn School of Medicine at Mount Sinai, New York, New York, and Maccabi Healthcare ServicesTel AvivIsrael
| | - Reem Agbareia
- Ophthalmology DepartmentHadassah Medical CenterJerusalemIsrael
- Hadassah Medical CenterJerusalemIsrael
| | | | | | | | | | - Eyal Klang
- Icahn School of Medicine at Mount SinaiNew YorkNew York
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Sequí-Sabater JM, Benavent D. Artificial intelligence in rheumatology research: what is it good for? RMD Open 2025; 11:e004309. [PMID: 39778924 PMCID: PMC11748787 DOI: 10.1136/rmdopen-2024-004309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2024] [Accepted: 12/08/2024] [Indexed: 01/11/2025] Open
Abstract
Artificial intelligence (AI) is transforming rheumatology research, with a myriad of studies aiming to improve diagnosis, prognosis and treatment prediction, while also showing potential capability to optimise the research workflow, improve drug discovery and clinical trials. Machine learning, a key element of discriminative AI, has demonstrated the ability of accurately classifying rheumatic diseases and predicting therapeutic outcomes by using diverse data types, including structured databases, imaging and text. In parallel, generative AI, driven by large language models, is becoming a powerful tool for optimising the research workflow by supporting with content generation, literature review automation and clinical decision support. This review explores the current applications and future potential of both discriminative and generative AI in rheumatology. It also highlights the challenges posed by these technologies, such as ethical concerns and the need for rigorous validation and regulatory oversight. The integration of AI in rheumatology promises substantial advancements but requires a balanced approach to optimise benefits and minimise potential possible downsides.
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Affiliation(s)
- José Miguel Sequí-Sabater
- Rheumatology Department, La Ribera University Hospital, Alzira, Spain
- Rheumatology Deparment, La Fe University and Polytechnic Hospital, Valencia, Spain
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Diego Benavent
- Rheumatology Department, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
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Thirumal M, Monika S. Transforming Healthcare: The power and potential of digital medicine. Future Sci OA 2024; 10:2430357. [PMID: 39575639 PMCID: PMC11587863 DOI: 10.1080/20565623.2024.2430357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 10/15/2024] [Indexed: 11/27/2024] Open
Affiliation(s)
- M. Thirumal
- Department of Pharmacognosy, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India
| | - S. Monika
- Department of Pharmacognosy, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India
- Department of Pharmacognosy, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India
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Purohit R, Saineni S, Chalise S, Mathai R, Sambandam R, Medina-Perez R, Bhanusali N. Artificial intelligence in rheumatology: perspectives and insights from a nationwide survey of U.S. rheumatology fellows. Rheumatol Int 2024; 44:3053-3061. [PMID: 39453506 DOI: 10.1007/s00296-024-05737-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2024] [Accepted: 10/10/2024] [Indexed: 10/26/2024]
Abstract
Artificial Intelligence (AI) is poised to revolutionize healthcare by enhancing clinical practice, diagnostics, and patient care. Although AI offers potential benefits through data-driven insights and personalized treatments, challenges related to implementation, barriers, and ethical considerations necessitate further investigation. We conducted a cross-sectional survey using Qualtrics from October to December 2023 to evaluate U.S. rheumatology fellows' perspectives on AI in healthcare. The survey was disseminated via email to program directors, who forwarded it to their fellows. It included multiple-choice, Likert scale, and open-ended questions covering demographics, AI awareness, usage, and perceptions. Statistical analyses were performed using Spearman correlation and Chi-Square tests. The study received IRB approval and adhered to STROBE guidelines. The survey aimed to reach 528 U.S. rheumatology fellows. 95 fellows accessed the survey with response rate to each question varying between 85 and 95 participants. 57.6% were females, 66.3% aged 30-35, and 60.2% in their first fellowship year. There was a positive correlation between AI familiarity and confidence (Spearman's rho = 0.216, p = 0.044). Furthermore, 67.9% supported incorporating AI education into fellowship programs, with a significant relationship (p < 0.005) between AI confidence and support for AI education. Fellows recognized AI's benefits in reducing chart time (86.05%) and automating tasks (73.26%), but expressed concerns about charting errors (67.86%) and over-reliance (61.90%). Most (84.52%) disagreed with the notion of AI replacing them. Rheumatology fellows exhibit enthusiasm for AI integration yet have reservations about its implementation and ethical implications. Addressing these challenges through collaborative efforts can ensure responsible AI integration, prioritizing patient safety and ethical standards in rheumatology and beyond.
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Affiliation(s)
- Richa Purohit
- Concentra Urgent Care, 8119 S Orange Avenue, Orlando, FL, 32809, USA.
| | - Sathvik Saineni
- Department of Internal Medicine, University of Central Florida College of Medicine, Orlando, FL, USA
| | | | - Reanne Mathai
- University of Central Florida HCA Healthcare GME, Greater Orlando, FL, USA
| | | | - Richard Medina-Perez
- Department of Rheumatology, University of Central Florida College of Medicine, Orlando, FL, USA
| | - Neha Bhanusali
- University of Central Florida College of Medicine, Orlando, FL, USA
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Matias P, Araujo R, Graca R, Henriques AR, Belo D, Valada M, Lotfi NN, Mateus EF, Radner H, Rodrigues AM, Studenic P, Nunes F. COTIDIANA Dataset - Smartphone-Collected Data on the Mobility, Finger Dexterity, and Mental Health of People With Rheumatic and Musculoskeletal Diseases. IEEE J Biomed Health Inform 2024; 28:6538-6547. [PMID: 39250356 DOI: 10.1109/jbhi.2024.3456069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Rheumatic and Musculoskeletal Diseases (RMDs) are very common and can negatively impact patients' quality of life. The current care of patients with RMDs is episodic, based on a few yearly doctor visits, which may not provide an adequate picture of the patient's condition. Researchers have hypothesized that RMDs could be passively monitored using smartphones or sensors, however, there are no datasets to support this development. We introduce the COTIDIANA Dataset: a holistic, multimodal, multidimensional, and open-access resource that gathers data on mobility and physical activity, finger dexterity, and mental health, key dimensions affected by RMDs. We gathered smartphone and self-reported data from 31 patients and 28 age-matched controls, including inertial sensors, keyboard metrics, communication logs, and reference tests/scales. A preliminary analysis showed the potential for extracted metrics to predict RMD diagnosis and condition characteristics. Our dataset shall enable the community to create mobile and wearable-based solutions for patients with RMDs.
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Koker O, Sahin S, Yildiz M, Adrovic A, Kasapcopur O. The emerging paradigm in pediatric rheumatology: harnessing the power of artificial intelligence. Rheumatol Int 2024; 44:2315-2325. [PMID: 39012357 PMCID: PMC11424736 DOI: 10.1007/s00296-024-05661-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Accepted: 07/05/2024] [Indexed: 07/17/2024]
Abstract
Artificial intelligence algorithms, with roots extending into the past but experiencing a resurgence and evolution in recent years due to their superiority over traditional methods and contributions to human capabilities, have begun to make their presence felt in the field of pediatric rheumatology. In the ever-evolving realm of pediatric rheumatology, there have been incremental advancements supported by artificial intelligence in understanding and stratifying diseases, developing biomarkers, refining visual analyses, and facilitating individualized treatment approaches. However, like in many other domains, these strides have yet to gain clinical applicability and validation, and ethical issues remain unresolved. Furthermore, mastering different and novel terminologies appears challenging for clinicians. This review aims to provide a comprehensive overview of the current literature, categorizing algorithms and their applications, thus offering a fresh perspective on the nascent relationship between pediatric rheumatology and artificial intelligence, highlighting both its advancements and constraints.
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Affiliation(s)
- Oya Koker
- Department of Pediatric Rheumatology, Faculty of Medicine, Marmara University, Istanbul, Turkey
| | - Sezgin Sahin
- Department of Pediatric Rheumatology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Mehmet Yildiz
- Department of Pediatric Rheumatology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Amra Adrovic
- Department of Pediatric Rheumatology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Ozgur Kasapcopur
- Department of Pediatric Rheumatology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey.
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Bor S, Ciğerim SÇ, Kotan S. Comparison of AI-assisted cephalometric analysis and orthodontist-performed digital tracing analysis. Prog Orthod 2024; 25:41. [PMID: 39428414 PMCID: PMC11491421 DOI: 10.1186/s40510-024-00539-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 09/22/2024] [Indexed: 10/22/2024] Open
Abstract
BACKGROUND The aim of this study was to compare and evaluate three AI-assisted cephalometric analysis platforms-CephX, WeDoCeph, and WebCeph-with the traditional digital tracing method using NemoCeph software. MATERIAL AND METHOD A total of 1500 lateral cephalometric films that met the inclusion criteria were classified as Class I, Class II, and Class III. Subsequently, 40 patients were randomly selected from each class. These selected films were uploaded to 3 AI-assisted cephalometric analysis platforms and analyzed without any manual intervention. The same films were also analyzed by an orthodontist using the NemoCeph program. RESULTS The results revealed significant differences in key angular measurements (ANB, FMA, IMPA, and NLA) across Class I, II, and III patients when comparing the four cephalometric analysis methods (WebCeph, WeDoCeph, CephX, and NemoCeph). Notably, ANB (p < 0.05), FMA (p < 0.001), IMPA (p < 0.001), and NLA (p < 0.001) varied significantly. Linear measurements also differed, with significant differences in U1-NA (p = 0.002) and Co-A (p = 0.002) in certain classes. Repeated measurement analysis revealed variation in SNA (p = 0.011) and FMA (p = 0.030), particularly in the Class II NemoCeph group, suggesting method-dependent variability. CONCLUSION AI-assisted cephalometric analysis platforms such as WebCeph, WeDoCeph, and CephX give rise to notable variation in accuracy and reliability compared to traditional manual digital tracing, specifically in terms of angular and linear measurements. These results emphasize the importance of meticulous selection and assessment of analysis methods in orthodontic diagnostics and treatment planning.
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Affiliation(s)
- Sabahattin Bor
- Faculty of Dentistry, Department of Orthodontics, İnönü University, Malatya, Turkey.
| | | | - Seda Kotan
- Faculty of Dentistry, Department of Orthodontics, Van Yüzüncü Yıl University, Van, Turkey
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Azhideh A, Pooyan A, Alipour E, Haseli S, Hosseini N, Chalian M. The Role of Artificial Intelligence in Osteoarthritis. Semin Roentgenol 2024; 59:518-525. [PMID: 39490044 DOI: 10.1053/j.ro.2024.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 07/11/2024] [Accepted: 07/12/2024] [Indexed: 11/05/2024]
Affiliation(s)
- Arash Azhideh
- Department of Radiology, Division of Muscluskeletal and Intervention, University of Washington, Seattle, WA
| | - Atefe Pooyan
- Department of Radiology, Division of Muscluskeletal and Intervention, University of Washington, Seattle, WA
| | - Ehsan Alipour
- Department of Radiology, Division of Muscluskeletal and Intervention, University of Washington, Seattle, WA
| | - Sara Haseli
- Department of Radiology, Division of Muscluskeletal and Intervention, University of Washington, Seattle, WA
| | - Nastaran Hosseini
- Department of Radiology, Division of Muscluskeletal and Intervention, University of Washington, Seattle, WA
| | - Majid Chalian
- Department of Radiology, Division of Muscluskeletal and Intervention, University of Washington, Seattle, WA.
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Omar M, Naffaa ME, Glicksberg BS, Reuveni H, Nadkarni GN, Klang E. Advancing rheumatology with natural language processing: insights and prospects from a systematic review. Rheumatol Adv Pract 2024; 8:rkae120. [PMID: 39399162 PMCID: PMC11467191 DOI: 10.1093/rap/rkae120] [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: 06/18/2024] [Accepted: 08/14/2024] [Indexed: 10/15/2024] Open
Abstract
Objectives Natural language processing (NLP) and large language models (LLMs) have emerged as powerful tools in healthcare, offering advanced methods for analysing unstructured clinical texts. This systematic review aims to evaluate the current applications of NLP and LLMs in rheumatology, focusing on their potential to improve disease detection, diagnosis and patient management. Methods We screened seven databases. We included original research articles that evaluated the performance of NLP models in rheumatology. Data extraction and risk of bias assessment were performed independently by two reviewers, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies was used to evaluate the risk of bias. Results Of 1491 articles initially identified, 35 studies met the inclusion criteria. These studies utilized various data types, including electronic medical records and clinical notes, and employed models like Bidirectional Encoder Representations from Transformers and Generative Pre-trained Transformers. High accuracy was observed in detecting conditions such as RA, SpAs and gout. The use of NLP also showed promise in managing diseases and predicting flares. Conclusion NLP showed significant potential in enhancing rheumatology by improving diagnostic accuracy and personalizing patient care. While applications in detecting diseases like RA and gout are well developed, further research is needed to extend these technologies to rarer and more complex clinical conditions. Overcoming current limitations through targeted research is essential for fully realizing NLP's potential in clinical practice.
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Affiliation(s)
- Mahmud Omar
- Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | | | - Benjamin S Glicksberg
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Hagar Reuveni
- Division of Diagnostic Imaging, Sheba Medical Center, Affiliated to Tel-Aviv University, Ramat Gan, Israel
| | - Girish N Nadkarni
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Eyal Klang
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Benavent D, Madrid-García A. Large language models and rheumatology: are we there yet? Rheumatol Adv Pract 2024; 9:rkae119. [PMID: 40256630 PMCID: PMC12007598 DOI: 10.1093/rap/rkae119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Accepted: 07/17/2024] [Indexed: 04/22/2025] Open
Abstract
The last 2 years have marked the beginning of a golden age for natural language processing in medicine. The arrival of large language models (LLMs) and multimodal models have raised new opportunities and challenges for research and clinical practice. In rheumatology, a specialty rich in data and requiring complex decision-making, the use of these tools may transform diagnostic procedures, improve patient interaction and simplify data management, leading to more personalized and efficient healthcare outcomes. The objective of this article is to present an overview of the status of LLMs in the field of rheumatology while discussing some of the challenges ahead in this area of great potential.
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Affiliation(s)
- Diego Benavent
- Rheumatology Department, Hospital Universitari de Bellvitge, Barcelona, Spain
- Medical Department, Savana Research SL, Madrid, Spain
| | - Alfredo Madrid-García
- Grupo de Patología Musculoesquelética, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain
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Shahid S, Javaid A, Amjad U, Rasheed J. Investigating artificial intelligence models for predicting joint pain from serum biochemistry. REVISTA DA ASSOCIACAO MEDICA BRASILEIRA (1992) 2024; 70:e20240381. [PMID: 39292083 PMCID: PMC11404989 DOI: 10.1590/1806-9282.20240381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Accepted: 05/31/2024] [Indexed: 09/19/2024]
Abstract
OBJECTIVE The study used machine learning models to predict the clinical outcome with various attributes or when the models chose features based on their algorithms. METHODS Patients who presented to an orthopedic outpatient department with joint swelling or myalgia were included in the study. A proforma collected clinical information on age, gender, uric acid, C-reactive protein, and complete blood count/liver function test/renal function test parameters. Machine learning decision models (Random Forest and Gradient Boosted) were evaluated with the selected features/attributes. To categorize input data into outputs of indications of joint discomfort, multilayer perceptron and radial basis function-neural networks were used. RESULTS The random forest decision model outperformed with 97% accuracy and minimum errors to anticipate joint pain from input attributes. For predicted classifications, the multilayer perceptron fared better with an accuracy of 98% as compared to the radial basis function. Multilayer perceptron achieved the following normalized relevance: 100% (uric acid), 10.3% (creatinine), 9.8% (AST), 5.4% (lymphocytes), and 5% (C-reactive protein) for having joint pain. Uric acid has the highest normalized relevance for predicting joint pain. CONCLUSION The earliest artificial intelligence-based detection of joint pain will aid in the prevention of more serious orthopedic complications.
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Affiliation(s)
- Saman Shahid
- National University of Computer and Emerging Sciences, Foundation for the Advancement of Science and Technology, Department of Sciences and Humanities - Lahore, Pakistan
| | - Aatir Javaid
- The University of Lahore, University College of Medicine, Department of Orthopedic Surgery - Lahore, Pakistan
| | - Usman Amjad
- The University of Lahore, University College of Medicine, Department of Orthopedic Surgery - Lahore, Pakistan
| | - Jawad Rasheed
- Istanbul Sabahattin Zaim University, Department of Computer Engineering - İstanbul, Turkey
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La Bella S, Attanasi M, Porreca A, Di Ludovico A, Maggio MC, Gallizzi R, La Torre F, Rigante D, Soscia F, Ardenti Morini F, Insalaco A, Natale MF, Chiarelli F, Simonini G, De Benedetti F, Gattorno M, Breda L. Reliability of a generative artificial intelligence tool for pediatric familial Mediterranean fever: insights from a multicentre expert survey. Pediatr Rheumatol Online J 2024; 22:78. [PMID: 39180115 PMCID: PMC11342667 DOI: 10.1186/s12969-024-01011-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Accepted: 07/29/2024] [Indexed: 08/26/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has become a popular tool for clinical and research use in the medical field. The aim of this study was to evaluate the accuracy and reliability of a generative AI tool on pediatric familial Mediterranean fever (FMF). METHODS Fifteen questions repeated thrice on pediatric FMF were prompted to the popular generative AI tool Microsoft Copilot with Chat-GPT 4.0. Nine pediatric rheumatology experts rated response accuracy with a blinded mechanism using a Likert-like scale with values from 1 to 5. RESULTS Median values for overall responses at the initial assessment ranged from 2.00 to 5.00. During the second assessment, median values spanned from 2.00 to 4.00, while for the third assessment, they ranged from 3.00 to 4.00. Intra-rater variability showed poor to moderate agreement (intraclass correlation coefficient range: -0.151 to 0.534). A diminishing level of agreement among experts over time was documented, as highlighted by Krippendorff's alpha coefficient values, ranging from 0.136 (at the first response) to 0.132 (at the second response) to 0.089 (at the third response). Lastly, experts displayed varying levels of trust in AI pre- and post-survey. CONCLUSIONS AI has promising implications in pediatric rheumatology, including early diagnosis and management optimization, but challenges persist due to uncertain information reliability and the lack of expert validation. Our survey revealed considerable inaccuracies and incompleteness in AI-generated responses regarding FMF, with poor intra- and extra-rater reliability. Human validation remains crucial in managing AI-generated medical information.
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Affiliation(s)
- Saverio La Bella
- Department of Pediatrics, "G. D'Annunzio" University of Chieti-Pescara, Chieti, Italy.
- Division of Pediatric Rheumatology, "G. D'Annunzio" University of Chieti-Pescara, Chieti, Italy.
- Division of Rheumatology and Autoinflammatory Diseases, IRCCS Istituto Giannina Gaslini, Genova, Italy.
| | - Marina Attanasi
- Department of Pediatrics, "G. D'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Annamaria Porreca
- Laboratory of Biostatistics, Department of Medical, Oral and Biotechnological Sciences, "G. D'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Armando Di Ludovico
- Department of Pediatrics, "G. D'Annunzio" University of Chieti-Pescara, Chieti, Italy
- Division of Pediatric Rheumatology, "G. D'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Maria Cristina Maggio
- University Department PROMISE "G. D'Alessandro", University of Palermo, Palermo, Italy
| | - Romina Gallizzi
- Department of Medical of Health Sciences, Magna Graecia University, Catanzaro, Italy
| | - Francesco La Torre
- Department of Pediatrics, Giovanni XXIII Pediatric Hospital, University of Bari, Bari, Italy
| | - Donato Rigante
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario A. Gemelli, Rome and Università Cattolica Sacro Cuore, Rome, Italy
| | | | | | - Antonella Insalaco
- Division of Rheumatology, Bambino Gesù Children's Hospital, Scientific Institute for Research and Health Care, Rome, Italy
| | - Marco Francesco Natale
- Division of Rheumatology, Bambino Gesù Children's Hospital, Scientific Institute for Research and Health Care, Rome, Italy
| | - Francesco Chiarelli
- Department of Pediatrics, "G. D'Annunzio" University of Chieti-Pescara, Chieti, Italy.
| | | | - Fabrizio De Benedetti
- Division of Rheumatology, Bambino Gesù Children's Hospital, Scientific Institute for Research and Health Care, Rome, Italy
| | - Marco Gattorno
- Division of Rheumatology and Autoinflammatory Diseases, IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - Luciana Breda
- Department of Pediatrics, "G. D'Annunzio" University of Chieti-Pescara, Chieti, Italy
- Division of Pediatric Rheumatology, "G. D'Annunzio" University of Chieti-Pescara, Chieti, Italy
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Caratsch L, Lechtenboehmer C, Caorsi M, Oung K, Zanchi F, Aleman Y, Walker UA, Omoumi P, Hügle T. Detection and Grading of Radiographic Hand Osteoarthritis Using an Automated Machine Learning Platform. ACR Open Rheumatol 2024; 6:388-395. [PMID: 38576187 PMCID: PMC11168904 DOI: 10.1002/acr2.11665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 01/31/2024] [Accepted: 02/09/2024] [Indexed: 04/06/2024] Open
Abstract
OBJECTIVE Automated machine learning (autoML) platforms allow health care professionals to play an active role in the development of machine learning (ML) algorithms according to scientific or clinical needs. The aim of this study was to develop and evaluate such a model for automated detection and grading of distal hand osteoarthritis (OA). METHODS A total of 13,690 hand radiographs from 2,863 patients within the Swiss Cohort of Quality Management (SCQM) and an external control data set of 346 non-SCQM patients were collected and scored for distal interphalangeal OA (DIP-OA) using the modified Kellgren/Lawrence (K/L) score. Giotto (Learn to Forecast [L2F]) was used as an autoML platform for training two convolutional neural networks for DIP joint extraction and subsequent classification according to the K/L scores. A total of 48,892 DIP joints were extracted and then used to train the classification model. Heatmaps were generated independently of the platform. User experience of a web application as a provisional user interface was investigated by rheumatologists and radiologists. RESULTS The sensitivity and specificity of this model for detecting DIP-OA were 79% and 86%, respectively. The accuracy for grading the correct K/L score was 75%, with a κ score of 0.76. The accuracy per DIP-OA class differed, with 86% for no OA (defined as K/L scores 0 and 1), 71% for a K/L score of 2, 46% for a K/L score of 3, and 67% for a K/L score of 4. Similar values were obtained in an independent external test set. Qualitative and quantitative user experience testing of the web application revealed a moderate to high demand for automated DIP-OA scoring among rheumatologists. Conversely, radiologists expressed a low demand, except for the use of heatmaps. CONCLUSION AutoML platforms are an opportunity to develop clinical end-to-end ML algorithms. Here, automated radiographic DIP-OA detection is both feasible and usable, whereas grading among individual K/L scores (eg, for clinical trials) remains challenging.
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Affiliation(s)
- Leo Caratsch
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
- City Hospital WaidZurichSwitzerland
- L2F (Learn to Forecast)LausanneSwitzerland
| | - Christian Lechtenboehmer
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
- City Hospital WaidZurichSwitzerland
- L2F (Learn to Forecast)LausanneSwitzerland
- University Hospital of BaselBaselSwitzerland
| | | | - Karine Oung
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Fabio Zanchi
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Yasser Aleman
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
| | | | - Patrick Omoumi
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Thomas Hügle
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
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15
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Stamate E, Piraianu AI, Ciobotaru OR, Crassas R, Duca O, Fulga A, Grigore I, Vintila V, Fulga I, Ciobotaru OC. Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years. Diagnostics (Basel) 2024; 14:1103. [PMID: 38893630 PMCID: PMC11172021 DOI: 10.3390/diagnostics14111103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/12/2024] [Accepted: 05/23/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. RESULTS We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. CONCLUSIONS The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.
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Affiliation(s)
- Elena Stamate
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Alin-Ionut Piraianu
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Oana Roxana Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
| | - Rodica Crassas
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Oana Duca
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Ionica Grigore
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Vlad Vintila
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Clinical Department of Cardio-Thoracic Pathology, University of Medicine and Pharmacy “Carol Davila” Bucharest, 37 Dionisie Lupu Street, 4192910 Bucharest, Romania
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Octavian Catalin Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
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16
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Gandrup J, Selby DA, Dixon WG. Classifying Self-Reported Rheumatoid Arthritis Flares Using Daily Patient-Generated Data From a Smartphone App: Exploratory Analysis Applying Machine Learning Approaches. JMIR Form Res 2024; 8:e50679. [PMID: 38743480 PMCID: PMC11134244 DOI: 10.2196/50679] [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: 07/09/2023] [Revised: 02/04/2024] [Accepted: 02/26/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND The ability to predict rheumatoid arthritis (RA) flares between clinic visits based on real-time, longitudinal patient-generated data could potentially allow for timely interventions to avoid disease worsening. OBJECTIVE This exploratory study aims to investigate the feasibility of using machine learning methods to classify self-reported RA flares based on a small data set of daily symptom data collected on a smartphone app. METHODS Daily symptoms and weekly flares reported on the Remote Monitoring of Rheumatoid Arthritis (REMORA) smartphone app from 20 patients with RA over 3 months were used. Predictors were several summary features of the daily symptom scores (eg, pain and fatigue) collected in the week leading up to the flare question. We fitted 3 binary classifiers: logistic regression with and without elastic net regularization, a random forest, and naive Bayes. Performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic curve. For the best-performing model, we considered sensitivity and specificity for different thresholds in order to illustrate different ways in which the predictive model could behave in a clinical setting. RESULTS The data comprised an average of 60.6 daily reports and 10.5 weekly reports per participant. Participants reported a median of 2 (IQR 0.75-4.25) flares each over a median follow-up time of 81 (IQR 79-82) days. AUCs were broadly similar between models, but logistic regression with elastic net regularization had the highest AUC of 0.82. At a cutoff requiring specificity to be 0.80, the corresponding sensitivity to detect flares was 0.60 for this model. The positive predictive value (PPV) in this population was 53%, and the negative predictive value (NPV) was 85%. Given the prevalence of flares, the best PPV achieved meant only around 2 of every 3 positive predictions were correct (PPV 0.65). By prioritizing a higher NPV, the model correctly predicted over 9 in every 10 non-flare weeks, but the accuracy of predicted flares fell to only 1 in 2 being correct (NPV and PPV of 0.92 and 0.51, respectively). CONCLUSIONS Predicting self-reported flares based on daily symptom scorings in the preceding week using machine learning methods was feasible. The observed predictive accuracy might improve as we obtain more data, and these exploratory results need to be validated in an external cohort. In the future, analysis of frequently collected patient-generated data may allow us to predict flares before they unfold, opening opportunities for just-in-time adaptative interventions. Depending on the nature and implication of an intervention, different cutoff values for an intervention decision need to be considered, as well as the level of predictive certainty required.
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Affiliation(s)
- Julie Gandrup
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
| | - David A Selby
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
- Department of Computer Science, Technische Universität Kaiserslautern, Kaiserslautern, Germany
| | - William G Dixon
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
- Department of Rheumatology, Northern Care Alliance NHS Foundation Trust, Salford, United Kingdom
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17
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Adams LC, Bressem KK, Ziegeler K, Vahldiek JL, Poddubnyy D. Artificial intelligence to analyze magnetic resonance imaging in rheumatology. Joint Bone Spine 2024; 91:105651. [PMID: 37797827 DOI: 10.1016/j.jbspin.2023.105651] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 08/29/2023] [Accepted: 09/26/2023] [Indexed: 10/07/2023]
Abstract
Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance imaging (MRI) has become indispensable in rheumatology, but interpretation remains laborious and variable. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers a means to improve and advance MRI analysis. This review examines current AI applications in rheumatology MRI analysis, addressing diagnostic support, disease classification, activity assessment, and progression monitoring. AI demonstrates promise, with high sensitivity, specificity, and accuracy, achieving or surpassing expert performance. The review also discusses clinical implementation challenges and future research directions to enhance rheumatic disease diagnosis and management.
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Affiliation(s)
- Lisa C Adams
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Keno K Bressem
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Katharina Ziegeler
- Department of Hematology, Oncology , and Cancer Immunology, Campus Charité Mitte, Charité Universitätsmedizin Berlin, Germany; Evidia Radiologie am Rheumazentrum Ruhrgebiet, Germany
| | - Janis L Vahldiek
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Denis Poddubnyy
- Department of Gastroenterology, Infectious Diseases and Rheumatology (including Nutrition Medicine), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany
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18
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Mickley JP, Grove AF, Rouzrokh P, Yang L, Larson AN, Sanchez-Sotello J, Maradit Kremers H, Wyles CC. A Stepwise Approach to Analyzing Musculoskeletal Imaging Data With Artificial Intelligence. Arthritis Care Res (Hoboken) 2024; 76:590-599. [PMID: 37849415 DOI: 10.1002/acr.25260] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/27/2023] [Accepted: 10/13/2023] [Indexed: 10/19/2023]
Abstract
The digitization of medical records and expanding electronic health records has created an era of "Big Data" with an abundance of available information ranging from clinical notes to imaging studies. In the field of rheumatology, medical imaging is used to guide both diagnosis and treatment of a wide variety of rheumatic conditions. Although there is an abundance of data to analyze, traditional methods of image analysis are human resource intensive. Fortunately, the growth of artificial intelligence (AI) may be a solution to handle large datasets. In particular, computer vision is a field within AI that analyzes images and extracts information. Computer vision has impressive capabilities and can be applied to rheumatologic conditions, necessitating a need to understand how computer vision works. In this article, we provide an overview of AI in rheumatology and conclude with a five step process to plan and conduct research in the field of computer vision. The five steps include (1) project definition, (2) data handling, (3) model development, (4) performance evaluation, and (5) deployment into clinical care.
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19
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Gallardo-Pizarro A, Campos-Rodríguez V, Martín-Iglesias D, Ruiz-Irastorza G. Routine biomarker profile for the prediction of clinical phenotypes of adult-onset Still's disease using unsupervised clustering algorithm. Int J Rheum Dis 2024; 27:e15143. [PMID: 38576108 DOI: 10.1111/1756-185x.15143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 03/12/2024] [Accepted: 03/22/2024] [Indexed: 04/06/2024]
Abstract
AIM This study addresses the challenge of predicting the course of Adult-onset Still's disease (AoSD), a rare systemic autoinflammatory disorder of unknown origin. Precise prediction is crucial for effective clinical management, especially in the absence of specific laboratory indicators. METHODS We assessed the effectiveness of combining traditional biomarkers with the k-medoids unsupervised clustering algorithm in forecasting the various clinical courses of AoSD-monocyclic, polycyclic, or chronic articular. This approach represents an innovative strategy in predicting the disease's course. RESULTS The analysis led to the identification of distinct patient profiles based on accessible biomarkers. Specifically, patients with elevated ferritin levels at diagnosis were more likely to experience a monocyclic disease course, while those with lower erythrocyte sedimentation rate could present with any of the clinical courses, monocyclic, polycyclic, or chronic articular, during follow-up. CONCLUSION The study demonstrates the potential of integrating traditional biomarkers with unsupervised clustering algorithms in understanding the heterogeneity of AoSD. These findings suggest new avenues for developing personalized treatment strategies, though further validation in larger, prospective studies is necessary.
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Affiliation(s)
| | | | - Daniel Martín-Iglesias
- Autoimmune Diseases Research Unit, Department of Internal Medicine, Biocruces Bizkaia Health Research Institute, Hospital Universitario Cruces, Barakaldo, Spain
- University of the Basque Country Faculty of Medicine and Nursing, Medicine, Barakaldo, Spain
| | - Guillermo Ruiz-Irastorza
- Autoimmune Diseases Research Unit, Department of Internal Medicine, Biocruces Bizkaia Health Research Institute, Hospital Universitario Cruces, Barakaldo, Spain
- University of the Basque Country, UPV/EHU, Bilbao, Spain
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20
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Zhan K, Buhler KA, Chen IY, Fritzler MJ, Choi MY. Systemic lupus in the era of machine learning medicine. Lupus Sci Med 2024; 11:e001140. [PMID: 38443092 PMCID: PMC11146397 DOI: 10.1136/lupus-2023-001140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 01/26/2024] [Indexed: 03/07/2024]
Abstract
Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challenges and opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward.
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Affiliation(s)
- Kevin Zhan
- University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - Katherine A Buhler
- University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - Irene Y Chen
- Computational Precision Health, University of California Berkeley and University of California San Francisco, Berkeley, California, USA
- Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, California, USA
| | - Marvin J Fritzler
- University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - May Y Choi
- University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
- McCaig Institute for Bone and Joint Health, Calgary, Alberta, Canada
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21
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Hügle T. Advancing Rheumatology Care Through Machine Learning. Pharmaceut Med 2024; 38:87-96. [PMID: 38421585 PMCID: PMC10948517 DOI: 10.1007/s40290-024-00515-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/31/2024] [Indexed: 03/02/2024]
Abstract
Rheumatologic diseases are marked by their complexity, involving immune-, metabolic- and mechanically mediated processes which can affect different organ systems. Despite a growing arsenal of targeted medications, many rheumatology patients fail to achieve full remission. Assessing disease activity remains challenging, as patients prioritize different symptoms and disease phenotypes vary. This is also reflected in clinical trials where the efficacy of drugs is not necessarily measured in an optimal way with the traditional outcome assessment. The recent COVID-19 pandemic has catalyzed a digital transformation in healthcare, embracing telemonitoring and patient-reported data via apps and wearables. As a further driver of digital medicine, electronic medical record (EMR) providers are actively engaged in developing algorithms for clinical decision support, heralding a shift towards patient-centered, decentralized care. Machine learning algorithms have emerged as valuable tools for handling the increasing volume of patient data, promising to enhance treatment quality and patient well-being. Convolutional neural networks (CNN) are particularly promising for radiological image analysis, aiding in the detection of specific lesions such as erosions, sacroiliitis, or osteoarthritis, with several FDA-approved applications. Clinical predictions, including numerical disease activity forecasts and medication choices, offer the potential to optimize treatment strategies. Numeric predictions can be integrated into clinical workflows, allowing for shared decision making with patients. Clustering patients based on disease characteristics provides a personalized care approach. Digital biomarkers, such as patient-reported outcomes and wearables data, offer insights into disease progression and therapy response more flexibly and outside patient consultations. In association with patient-reported outcomes, disease-specific digital biomarkers via image recognition or single-camera motion capture enables more efficient remote patient monitoring. Digital biomarkers may also play a major role in clinical trials in the future as continuous, disease-specific outcome measurement facilitating decentralized studies. Prediction models can help with patient selection in clinical trials, such as by predicting high disease activity. Efforts are underway to integrate these advancements into clinical workflows using digital pathways and remote patient monitoring platforms. In summary, machine learning, digital biomarkers, and advanced imaging technologies hold immense promise for enhancing clinical decision support and clinical trials in rheumatology. Effective integration will require a multidisciplinary approach and continued validation through prospective studies.
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Affiliation(s)
- Thomas Hügle
- Department of Rheumatology, University Hospital Lausanne (CHUV) and University of Lausanne, Avenue Pierre-Decker 4, 1001, Lausanne, Switzerland.
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22
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Tan TC, Roslan NEB, Li JW, Zou X, Chen X, Santosa A. Patient Acceptability of Symptom Screening and Patient Education Using a Chatbot for Autoimmune Inflammatory Diseases: Survey Study. JMIR Form Res 2023; 7:e49239. [PMID: 37219234 PMCID: PMC11019963 DOI: 10.2196/49239] [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: 05/23/2023] [Revised: 08/27/2023] [Accepted: 11/05/2023] [Indexed: 05/24/2023] Open
Abstract
BACKGROUND Chatbots have the potential to enhance health care interaction, satisfaction, and service delivery. However, data regarding their acceptance across diverse patient populations are limited. In-depth studies on the reception of chatbots by patients with chronic autoimmune inflammatory diseases are lacking, although such studies are vital for facilitating the effective integration of chatbots in rheumatology care. OBJECTIVE We aim to assess patient perceptions and acceptance of a chatbot designed for autoimmune inflammatory rheumatic diseases (AIIRDs). METHODS We administered a comprehensive survey in an outpatient setting at a top-tier rheumatology referral center. The target cohort included patients who interacted with a chatbot explicitly tailored to facilitate diagnosis and obtain information on AIIRDs. Following the RE-AIM (Reach, Effectiveness, Adoption, Implementation and Maintenance) framework, the survey was designed to gauge the effectiveness, user acceptability, and implementation of the chatbot. RESULTS Between June and October 2022, we received survey responses from 200 patients, with an equal number of 100 initial consultations and 100 follow-up (FU) visits. The mean scores on a 5-point acceptability scale ranged from 4.01 (SD 0.63) to 4.41 (SD 0.54), indicating consistently high ratings across the different aspects of chatbot performance. Multivariate regression analysis indicated that having a FU visit was significantly associated with a greater willingness to reuse the chatbot for symptom determination (P=.01). Further, patients' comfort with chatbot diagnosis increased significantly after meeting physicians (P<.001). We observed no significant differences in chatbot acceptance according to sex, education level, or diagnosis category. CONCLUSIONS This study underscores that chatbots tailored to AIIRDs have a favorable reception. The inclination of FU patients to engage with the chatbot signifies the possible influence of past clinical encounters and physician affirmation on its use. Although further exploration is required to refine their integration, the prevalent positive perceptions suggest that chatbots have the potential to strengthen the bridge between patients and health care providers, thus enhancing the delivery of rheumatology care to various cohorts.
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Affiliation(s)
- Tze Chin Tan
- Department of Rheumatology and Immunology, Singapore General Hospital, Singapore, Singapore
- Medicine Academic Clinical Programme, SingHealth-Duke-NUS, Singapore, Singapore
| | - Nur Emillia Binte Roslan
- Medicine Academic Clinical Programme, SingHealth-Duke-NUS, Singapore, Singapore
- Department of General Medicine, Sengkang General Hospital, Singapore, Singapore
| | - James Weiquan Li
- Medicine Academic Clinical Programme, SingHealth-Duke-NUS, Singapore, Singapore
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore, Singapore
| | - Xinying Zou
- Internal Medicine Clinic, Changi General Hospital, Singapore, Singapore
| | - Xiangmei Chen
- Internal Medicine Clinic, Changi General Hospital, Singapore, Singapore
| | - Anindita Santosa
- Medicine Academic Clinical Programme, SingHealth-Duke-NUS, Singapore, Singapore
- Division of Rheumatology and Immunology, Department of Medicine, Changi General Hospital, Singapore, Singapore
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Hügle T, Grek V. Digital transformation of an academic hospital department: A case study on strategic planning using the balanced scorecard. PLOS DIGITAL HEALTH 2023; 2:e0000385. [PMID: 37976272 PMCID: PMC10656018 DOI: 10.1371/journal.pdig.0000385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 10/10/2023] [Indexed: 11/19/2023]
Abstract
Digital transformation has a significant impact on efficiency and quality in hospitals. New solutions can support the management of data overload and the shortage of qualified staff. However, the timely and effective integration of these new digital tools in the healthcare setting poses challenges and requires guidance. The balanced scorecard (BSC) is a managerial method used to translate new strategies into action and measure their impact in an institution, going beyond financial values. This framework enables quicker operational adjustments and enhances awareness of real-time performance from multiple perspectives, including customers, internal procedures, and the learning organization. The aim of this study was to adapt the BSC to the evolving digital healthcare environment, encompassing factors like the recent pandemic, new technologies such as artificial intelligence, legislation, and user preferences. A strategic mapping with identification of corresponding key performance indicators was performed. To achieve this, we employed a qualitative research approach involving retreats, interdisciplinary working groups, and semi-structured interviews with different stakeholders (administrative, clinical, computer scientists) in a rheumatology department. These inputs served as the basis for customizing the BSC according to upcoming or already implemented solutions and to define actionable, cross-level performance indicators for all perspectives. Our defined values include quality of care, patient empowerment, employee satisfaction, sustainability and innovation. We also identified substantial changes in our internal processes, with the electronic medical record (EMR) emerging as a central element for vertical and horizontal digitalization. This includes integrating patient-reported outcomes, disease-specific digital biomarker, prediction algorithms to increase the quality of care as well as advanced language models in order save resources. Gaps in communication and collaboration between medical departments have been identified as a main target for new digital solutions, especially in patients with more than one disorder. From a learning institution's perspective, digital literacy among patients and healthcare professionals emerges as a crucial lever for successful implementation of internal processes. In conclusion, the BSC is a helpful tool for guiding digitalization in hospitals as a horizontally and vertically connected process that affects all stakeholders. Future studies should include empirical analyses and explore correlations between variables and above all input and user experience from patients.
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Affiliation(s)
- Thomas Hügle
- Department of Rheumatology, Departement Appareil Locomoteur (DAL), University Hospital Lausanne (CHUV) and University of Lausanne, Switzerland
| | - Vincent Grek
- Department of Rheumatology, Departement Appareil Locomoteur (DAL), University Hospital Lausanne (CHUV) and University of Lausanne, Switzerland
- Department of Urology,Inselspital and University of Bern,Bern, Switzerland
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24
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Al-Maini M, Maindarkar M, Kitas GD, Khanna NN, Misra DP, Johri AM, Mantella L, Agarwal V, Sharma A, Singh IM, Tsoulfas G, Laird JR, Faa G, Teji J, Turk M, Viskovic K, Ruzsa Z, Mavrogeni S, Rathore V, Miner M, Kalra MK, Isenovic ER, Saba L, Fouda MM, Suri JS. Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review. Rheumatol Int 2023; 43:1965-1982. [PMID: 37648884 DOI: 10.1007/s00296-023-05415-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 07/31/2023] [Indexed: 09/01/2023]
Abstract
The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™-aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.
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Affiliation(s)
- Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, L4Z 4C4, Canada
| | - Mahesh Maindarkar
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
- Asia Pacific Vascular Society, New Delhi, 110001, India
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, DY1 2HQ, UK
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, M13 9PL, UK
| | - Narendra N Khanna
- Asia Pacific Vascular Society, New Delhi, 110001, India
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, 110001, India
| | | | - Amer M Johri
- Division of Cardiology, Department of Medicine, Queen's University, Kingston, Canada
| | - Laura Mantella
- Division of Cardiology, Department of Medicine, University of Toronto, Toronto, Canada
| | - Vikas Agarwal
- Department of Immunology, SGPIMS, Lucknow, 226014, India
| | - Aman Sharma
- Department of Immunology, SGPIMS, Lucknow, 226014, India
| | - Inder M Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124, Thessaloniki, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, 94574, USA
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria, 09124, Cagliari, Italy
| | - Jagjit Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, 60611, USA
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753, Delmenhorst, Germany
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, UHID, 10 000, Zagreb, Croatia
| | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Centre, Athens, Greece
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, 95823, USA
| | - Martin Miner
- Men's Health Centre, Miriam Hospital Providence, Providence, RI, 02906, USA
| | - Manudeep K Kalra
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Esma R Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of the Republic of Serbia, University of Belgrade, 11000, Belgrade, Serbia
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138, Cagliari, Italy
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA.
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25
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Gkantzios A, Kokkotis C, Tsiptsios D, Moustakidis S, Gkartzonika E, Avramidis T, Tripsianis G, Iliopoulos I, Aggelousis N, Vadikolias K. From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning. J Pers Med 2023; 13:1375. [PMID: 37763143 PMCID: PMC10532952 DOI: 10.3390/jpm13091375] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/03/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
As a result of social progress and improved living conditions, which have contributed to a prolonged life expectancy, the prevalence of strokes has increased and has become a significant phenomenon. Despite the available stroke treatment options, patients frequently suffer from significant disability after a stroke. Initial stroke severity is a significant predictor of functional dependence and mortality following an acute stroke. The current study aims to collect and analyze data from the hyperacute and acute phases of stroke, as well as from the medical history of the patients, in order to develop an explainable machine learning model for predicting stroke-related neurological deficits at discharge, as measured by the National Institutes of Health Stroke Scale (NIHSS). More specifically, we approached the data as a binary task problem: improvement of NIHSS progression vs. worsening of NIHSS progression at discharge, using baseline data within the first 72 h. For feature selection, a genetic algorithm was applied. Using various classifiers, we found that the best scores were achieved from the Random Forest (RF) classifier at the 15 most informative biomarkers and parameters for the binary task of the prediction of NIHSS score progression. RF achieved 91.13% accuracy, 91.13% recall, 90.89% precision, 91.00% f1-score, 8.87% FNrate and 4.59% FPrate. Those biomarkers are: age, gender, NIHSS upon admission, intubation, history of hypertension and smoking, the initial diagnosis of hypertension, diabetes, dyslipidemia and atrial fibrillation, high-density lipoprotein (HDL) levels, stroke localization, systolic blood pressure levels, as well as erythrocyte sedimentation rate (ESR) levels upon admission and the onset of respiratory infection. The SHapley Additive exPlanations (SHAP) model interpreted the impact of the selected features on the model output. Our findings suggest that the aforementioned variables may play a significant role in determining stroke patients' NIHSS progression from the time of admission until their discharge.
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Affiliation(s)
- Aimilios Gkantzios
- Department of Neurology, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (D.T.); (I.I.); (K.V.)
- Department of Neurology, Korgialeneio—Benakeio “Hellenic Red Cross” General Hospital of Athens, 11526 Athens, Greece;
| | - Christos Kokkotis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece; (C.K.); (S.M.); (N.A.)
| | - Dimitrios Tsiptsios
- Department of Neurology, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (D.T.); (I.I.); (K.V.)
| | - Serafeim Moustakidis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece; (C.K.); (S.M.); (N.A.)
| | - Elena Gkartzonika
- School of Philosophy, University of Ioannina, 45110 Ioannina, Greece;
| | - Theodoros Avramidis
- Department of Neurology, Korgialeneio—Benakeio “Hellenic Red Cross” General Hospital of Athens, 11526 Athens, Greece;
| | - Gregory Tripsianis
- Laboratory of Medical Statistics, Democritus University of Thrace, 68100 Alexandroupolis, Greece;
| | - Ioannis Iliopoulos
- Department of Neurology, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (D.T.); (I.I.); (K.V.)
| | - Nikolaos Aggelousis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece; (C.K.); (S.M.); (N.A.)
| | - Konstantinos Vadikolias
- Department of Neurology, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (D.T.); (I.I.); (K.V.)
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26
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Lee Y, Choi HJ, Kim H, Kim S, Kim MS, Cha H, Eum YJ, Cho H, Park JE, You SH. Feasibility of artificial intelligence-driven interfractional monitoring of organ changes by mega-voltage computed tomography in intensity-modulated radiotherapy of prostate cancer. Radiat Oncol J 2023; 41:186-198. [PMID: 37793628 PMCID: PMC10556843 DOI: 10.3857/roj.2023.00444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 08/21/2023] [Accepted: 09/04/2023] [Indexed: 10/06/2023] Open
Abstract
PURPOSE High-dose radiotherapy (RT) for localized prostate cancer requires careful consideration of target position changes and adjacent organs-at-risk (OARs), such as the rectum and bladder. Therefore, daily monitoring of target position and OAR changes is crucial in minimizing interfractional dosimetric uncertainties. For efficient monitoring of the internal condition of patients, we assessed the feasibility of an auto-segmentation of OARs on the daily acquired images, such as megavoltage computed tomography (MVCT), via a commercial artificial intelligence (AI)-based solution in this study. MATERIALS AND METHODS We collected MVCT images weekly during the entire course of RT for 100 prostate cancer patients treated with the helical TomoTherapy system. Based on the manually contoured body outline, the bladder including prostate area, and rectal balloon regions for the 100 MVCT images, we trained the commercially available fully convolutional (FC)-DenseNet model and tested its auto-contouring performance. RESULTS Based on the optimally determined hyperparameters, the FC-DenseNet model successfully auto-contoured all regions of interest showing high dice similarity coefficient (DSC) over 0.8 and a small mean surface distance (MSD) within 1.43 mm in reference to the manually contoured data. With this well-trained AI model, we have efficiently monitored the patient's internal condition through six MVCT scans, analyzing DSC, MSD, centroid, and volume differences. CONCLUSION We have verified the feasibility of utilizing a commercial AI-based model for auto-segmentation with low-quality daily MVCT images. In the future, we will establish a fast and accurate auto-segmentation and internal organ monitoring system for efficiently determining the time for adaptive replanning.
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Affiliation(s)
- Yohan Lee
- Department of Radiation Oncology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Hyun Joon Choi
- Department of Radiation Oncology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Hyemi Kim
- Department of Radiation Oncology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Sunghyun Kim
- Department of Radiation Oncology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Mi Sun Kim
- Department of Radiation Oncology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Hyejung Cha
- Department of Radiation Oncology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Young Ju Eum
- Department of Radiation Oncology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Hyosung Cho
- Department of Radiation Convergence Engineering, Yonsei University, Wonju, Korea
| | - Jeong Eun Park
- Department of Radiation Convergence Engineering, Yonsei University, Wonju, Korea
| | - Sei Hwan You
- Department of Radiation Oncology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
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27
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Madrid-García A, Merino-Barbancho B, Rodríguez-González A, Fernández-Gutiérrez B, Rodríguez-Rodríguez L, Menasalvas-Ruiz E. Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature. Semin Arthritis Rheum 2023; 61:152213. [PMID: 37315379 DOI: 10.1016/j.semarthrit.2023.152213] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 06/16/2023]
Abstract
The major and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five- year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease identification and prediction, disease classification, patient stratification and disease subtype identification, disease progression and activity, treatment response, and predictors of outcomes. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed, are highlighted in a discussion. Therefore, the present review aims to characterise how researchers are applying data science techniques in the rheumatology medical field. The most immediate conclusions that can be drawn from this work are: multiple and novel data science techniques have been used in a wide range of rheumatic and musculoskeletal diseases including rare diseases; the sample size and the data type used are heterogeneous, and new technical approaches are expected to arrive in the short-middle term.
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Affiliation(s)
- Alfredo Madrid-García
- Grupo de Patología Musculoesquelética. Hospital Clínico San Carlos, Prof. Martin Lagos s/n, Madrid, 28040, Spain; Escuela Técnica Superior de Ingenieros de Telecomunicación. Universidad Politécnica de Madrid, Avenida Complutense, 30, Madrid, 28040, Spain.
| | - Beatriz Merino-Barbancho
- Escuela Técnica Superior de Ingenieros de Telecomunicación. Universidad Politécnica de Madrid, Avenida Complutense, 30, Madrid, 28040, Spain
| | | | - Benjamín Fernández-Gutiérrez
- Grupo de Patología Musculoesquelética. Hospital Clínico San Carlos, Prof. Martin Lagos s/n, Madrid, 28040, Spain
| | - Luis Rodríguez-Rodríguez
- Grupo de Patología Musculoesquelética. Hospital Clínico San Carlos, Prof. Martin Lagos s/n, Madrid, 28040, Spain
| | - Ernestina Menasalvas-Ruiz
- Centro de Tecnología Biomédica. Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, 28223, Spain
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28
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Wang J, Tian Y, Zhou T, Tong D, Ma J, Li J. A survey of artificial intelligence in rheumatoid arthritis. RHEUMATOLOGY AND IMMUNOLOGY RESEARCH 2023; 4:69-77. [PMID: 37485476 PMCID: PMC10362600 DOI: 10.2478/rir-2023-0011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 06/14/2023] [Indexed: 07/25/2023]
Abstract
The article offers a survey of currently notable artificial intelligence methods (released between 2019-2023), with a particular emphasis on the latest advancements in detecting rheumatoid arthritis (RA) at an early stage, providing early treatment, and managing the disease. We discussed challenges in these areas followed by specific artificial intelligence (AI) techniques and summarized advances, relevant strengths, and obstacles. Overall, the application of AI in the fields of RA has the potential to enable healthcare professionals to detect RA at an earlier stage, thereby facilitating timely intervention and better disease management. However, more research is required to confirm the precision and dependability of AI in RA, and several problems such as technological and ethical concerns related to these approaches must be resolved before their widespread adoption.
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Affiliation(s)
- Jiaqi Wang
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
| | - Yu Tian
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou310027, Zhejiang Province, China
| | - Tianshu Zhou
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
| | - Danyang Tong
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
| | - Jing Ma
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
| | - Jingsong Li
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou310027, Zhejiang Province, China
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29
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Hammam N, Bakhiet A, El-Latif EA, El-Gazzar II, Samy N, Noor RAA, El-Shebeiny E, El-Najjar AR, Eesa NN, Salem MN, Ibrahim SE, El-Essawi DF, Elsaman AM, Fathi HM, Sallam RA, El Shereef RR, Ismail F, Abd-Elazeem MI, Said EA, Khalil NM, Shahin D, El-Saadany HM, ElKhalifa M, Nasef SI, Abdalla AM, Noshy N, Fawzy RM, Saad E, Moshrif A, El-Shanawany AT, Abdel-Fattah YH, Khalil HM, Hammam O, Fathy AA, Gheita TA. Development of machine learning models for detection of vision threatening Behçet's disease (BD) using Egyptian College of Rheumatology (ECR)-BD cohort. BMC Med Inform Decis Mak 2023; 23:37. [PMID: 36803463 PMCID: PMC9938580 DOI: 10.1186/s12911-023-02130-6] [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: 08/07/2022] [Accepted: 02/03/2023] [Indexed: 02/19/2023] Open
Abstract
BACKGROUND Eye lesions, occur in nearly half of patients with Behçet's Disease (BD), can lead to irreversible damage and vision loss; however, limited studies are available on identifying risk factors for the development of vision-threatening BD (VTBD). Using an Egyptian college of rheumatology (ECR)-BD, a national cohort of BD patients, we examined the performance of machine-learning (ML) models in predicting VTBD compared to logistic regression (LR) analysis. We identified the risk factors for the development of VTBD. METHODS Patients with complete ocular data were included. VTBD was determined by the presence of any retinal disease, optic nerve involvement, or occurrence of blindness. Various ML-models were developed and examined for VTBD prediction. The Shapley additive explanation value was used for the interpretability of the predictors. RESULTS A total of 1094 BD patients [71.5% were men, mean ± SD age 36.1 ± 10 years] were included. 549 (50.2%) individuals had VTBD. Extreme Gradient Boosting was the best-performing ML model (AUROC 0.85, 95% CI 0.81, 0.90) compared with logistic regression (AUROC 0.64, 95%CI 0.58, 0.71). Higher disease activity, thrombocytosis, ever smoking, and daily steroid dose were the top factors associated with VTBD. CONCLUSIONS Using information obtained in the clinical settings, the Extreme Gradient Boosting identified patients at higher risk of VTBD better than the conventional statistical method. Further longitudinal studies to evaluate the clinical utility of the proposed prediction model are needed.
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Affiliation(s)
- Nevin Hammam
- Department of Rheumatology and Rehabilitation, Faculty of Medicine, Assiut University, Assiut, Egypt.
| | - Ali Bakhiet
- Computer Science Department, Higher Institute of Computer Science and Information Systems, Culture and Science City, Giza, Egypt
| | - Eiman Abd El-Latif
- grid.7155.60000 0001 2260 6941Ophthalmology Department, Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | - Iman I. El-Gazzar
- grid.7776.10000 0004 0639 9286Rheumatology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Nermeen Samy
- grid.7269.a0000 0004 0621 1570Rheumatology Unit, Internal Medicine Department, Faculty of Medicine, Ain-Shams University, Cairo, Egypt
| | - Rasha A. Abdel Noor
- grid.412258.80000 0000 9477 7793Rheumatology Unit, Internal Medicine Department, Tanta University, Gharbia, Egypt
| | - Emad El-Shebeiny
- grid.411775.10000 0004 0621 4712Rheumatology Unit, Internal Medicine Department, Menoufia University, Menoufia, Egypt
| | - Amany R. El-Najjar
- grid.31451.320000 0001 2158 2757Rheumatology Department, Faculty of Medicine, Zagazig University, Sharkia, Egypt
| | - Nahla N. Eesa
- grid.7776.10000 0004 0639 9286Rheumatology Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Mohamed N. Salem
- grid.411662.60000 0004 0412 4932Rheumatology Unit, Internal Medicine Department, Faculty of Medicine, Beni-Suef University, Beni-Suef, Egypt
| | - Soha E. Ibrahim
- grid.7269.a0000 0004 0621 1570Rheumatology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Dina F. El-Essawi
- grid.429648.50000 0000 9052 0245Internal Medicine Department, Rheumatology and Rehabilitation Clinic, National Centre for Radiation Research and Technology, Egyptian Atomic Energy Authority (AEA), Cairo, Egypt
| | - Ahmed M. Elsaman
- grid.412659.d0000 0004 0621 726XRheumatology Department, Faculty of Medicine, Sohag University, Sohag, Egypt
| | - Hanan M. Fathi
- grid.411170.20000 0004 0412 4537Rheumatology Department, Faculty of Medicine, Fayoum University, Fayoum, Egypt
| | - Rehab A. Sallam
- grid.10251.370000000103426662Rheumatology Department, Faculty of Medicine, Mansoura University, Dakahlia, Egypt
| | - Rawhya R. El Shereef
- grid.411806.a0000 0000 8999 4945Rheumatology Department, Faculty of Medicine, Minia University, Minia, Egypt
| | - Faten Ismail
- grid.411806.a0000 0000 8999 4945Rheumatology Department, Faculty of Medicine, Minia University, Minia, Egypt
| | - Mervat I. Abd-Elazeem
- grid.411662.60000 0004 0412 4932Rheumatology Department, Faculty of Medicine, Beni-Suef University, Beni-Suef, Egypt
| | - Emtethal A. Said
- grid.411660.40000 0004 0621 2741Rheumatology Department, Faculty of Medicine, Benha University, Kalubia, Egypt
| | - Noha M. Khalil
- grid.7776.10000 0004 0639 9286Rheumatology Unit, Internal Medicine Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Dina Shahin
- grid.10251.370000000103426662Rheumatology Unit, Internal Medicine Department, Faculty of Medicine, Mansoura University, Dakahlia, Egypt
| | - Hanan M. El-Saadany
- grid.412258.80000 0000 9477 7793Rheumatology Department, Faculty of Medicine, Tanta University, Tanta, Egypt
| | - Marwa ElKhalifa
- grid.7155.60000 0001 2260 6941Rheumatology Unit, Internal Medicine Department, Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | - Samah I. Nasef
- grid.33003.330000 0000 9889 5690Rheumatology and Rehabilitation Department, Faculty of Medicine, Suez-Canal University, Ismailia, Egypt
| | - Ahmed M. Abdalla
- grid.417764.70000 0004 4699 3028Rheumatology Department, Faculty of Medicine, Aswan University, Aswan, Egypt
| | - Nermeen Noshy
- grid.411660.40000 0004 0621 2741Rheumatology Department, Faculty of Medicine, Benha University, Kalubia, Egypt
| | - Rasha M. Fawzy
- grid.411660.40000 0004 0621 2741Rheumatology Department, Faculty of Medicine, Benha University, Kalubia, Egypt
| | - Ehab Saad
- grid.412707.70000 0004 0621 7833Rheumatology Department, Faculty of Medicine, South Valley University, Qena, Egypt
| | - Abdelhafeez Moshrif
- grid.411303.40000 0001 2155 6022Rheumatology Department, Faculty of Medicine, Al-Azhar University, Assuit, Egypt
| | - Amira T. El-Shanawany
- grid.411775.10000 0004 0621 4712Rheumatology Department, Faculty of Medicine, Menoufia University, Menoufia, Egypt
| | - Yousra H. Abdel-Fattah
- grid.7155.60000 0001 2260 6941Rheumatology Department, Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | - Hossam M. Khalil
- grid.411662.60000 0004 0412 4932Ophthalmology Department, Faculty of Medicine, Beni-Suef University, Beni-Suef, Egypt
| | - Osman Hammam
- grid.252487.e0000 0000 8632 679XDepartment of Rheumatology and Rehabilitation, Faculty of Medicine, New Valley University, New Valley, Egypt
| | - Aly Ahmed Fathy
- grid.252487.e0000 0000 8632 679XOphthalmology Department, Faculty of Medicine, Al-Azhar Assiut University, Assiut, Egypt
| | - Tamer A. Gheita
- grid.7776.10000 0004 0639 9286Rheumatology Department, Kasr Al Ainy School of Medicine, Cairo University, Cairo, Egypt
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30
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Gkantzios A, Kokkotis C, Tsiptsios D, Moustakidis S, Gkartzonika E, Avramidis T, Aggelousis N, Vadikolias K. Evaluation of Blood Biomarkers and Parameters for the Prediction of Stroke Survivors' Functional Outcome upon Discharge Utilizing Explainable Machine Learning. Diagnostics (Basel) 2023; 13:diagnostics13030532. [PMID: 36766637 PMCID: PMC9914778 DOI: 10.3390/diagnostics13030532] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 01/25/2023] [Accepted: 01/29/2023] [Indexed: 02/04/2023] Open
Abstract
Despite therapeutic advancements, stroke remains a leading cause of death and long-term disability. The quality of current stroke prognostic models varies considerably, whereas prediction models of post-stroke disability and mortality are restricted by the sample size, the range of clinical and risk factors and the clinical applicability in general. Accurate prognostication can ease post-stroke discharge planning and help healthcare practitioners individualize aggressive treatment or palliative care, based on projected life expectancy and clinical course. In this study, we aimed to develop an explainable machine learning methodology to predict functional outcomes of stroke patients at discharge, using the Modified Rankin Scale (mRS) as a binary classification problem. We identified 35 parameters from the admission, the first 72 h, as well as the medical history of stroke patients, and used them to train the model. We divided the patients into two classes in two approaches: "Independent" vs. "Non-Independent" and "Non-Disability" vs. "Disability". Using various classifiers, we found that the best models in both approaches had an upward trend, with respect to the selected biomarkers, and achieved a maximum accuracy of 88.57% and 89.29%, respectively. The common features in both approaches included: age, hemispheric stroke localization, stroke localization based on blood supply, development of respiratory infection, National Institutes of Health Stroke Scale (NIHSS) upon admission and systolic blood pressure levels upon admission. Intubation and C-reactive protein (CRP) levels upon admission are additional features for the first approach and Erythrocyte Sedimentation Rate (ESR) levels upon admission for the second. Our results suggest that the said factors may be important predictors of functional outcomes in stroke patients.
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Affiliation(s)
- Aimilios Gkantzios
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
- Department of Neurology, Korgialeneio—Benakeio “Hellenic Red Cross” General Hospital of Athens, 11526 Athens, Greece
- Correspondence:
| | - Christos Kokkotis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Dimitrios Tsiptsios
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Serafeim Moustakidis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
- AIDEAS OÜ, Narva mnt 5, 10117 Tallinn, Estonia
| | - Elena Gkartzonika
- School of Philosophy, University of Ioannina, 45110 Ioannina, Greece
| | - Theodoros Avramidis
- Department of Neurology, Korgialeneio—Benakeio “Hellenic Red Cross” General Hospital of Athens, 11526 Athens, Greece
| | - Nikolaos Aggelousis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Konstantinos Vadikolias
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
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Nelson AE, Arbeeva L. Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions. J Rheumatol 2022; 49:1191-1200. [PMID: 35840150 PMCID: PMC9633365 DOI: 10.3899/jrheum.220326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 11/22/2022]
Abstract
There has been rapid growth in the use of artificial intelligence (AI) analytics in medicine in recent years, including in rheumatic and musculoskeletal diseases (RMDs). Such methods represent a challenge to clinicians, patients, and researchers, given the "black box" nature of most algorithms, the unfamiliarity of the terms, and the lack of awareness of potential issues around these analyses. Therefore, this review aims to introduce this subject area in a way that is relevant and meaningful to clinicians and researchers. We hope to provide some insights into relevant strengths and limitations, reporting guidelines, as well as recent examples of such analyses in key areas, with a focus on lessons learned and future directions in diagnosis, phenotyping, prognosis, and precision medicine in RMDs.
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Affiliation(s)
- Amanda E Nelson
- A.E. Nelson, MD, MSCR, Department of Medicine, Division of Rheumatology, Allergy, and Immunology, University of North Carolina at Chapel Hill;
| | - Liubov Arbeeva
- L. Arbeeva, MS, Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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32
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Unravelling the physicochemical features of US wheat flours over the past two decades by machine learning analysis. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.114036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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33
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Tarakci F, Ozkan IA, Yilmaz S, Tezcan D. Diagnosing rheumatoid arthritis disease using fuzzy expert system and machine learning techniques. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-221582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Rheumatoid Arthritis (RA) is a very common autoimmune disease that causes significant morbidity and mortality, and therefore early diagnosis and treatment are important. Early diagnosis of RA and knowing the severity of the disease are very important for the treatment to be applied. The diagnosis of RA usually requires a physical examination, laboratory tests, and a review of the patient’s medical history. In this study, the diagnosis of RA was made with two different methods using a fuzzy expert system (FES) and machine learning (ML) techniques, which were designed and implemented with the help of a specialist in the field, and the results were compared. For this purpose, blood counts were taken from 286 people, including 91 men and 195 women from various age groups. In the first method, an FES structure that determines the severity of RA disease has been established from blood count using the laboratory test results of CRP, ESR, RF, and ANA. The FES result that determines RA disease severity, the Anti-CCP level that is used to distinguish RA disease, and the patient’s medical history were used to design the Decision Support System (DSS) that diagnoses RA disease. The DSS is web-based and publicly accessible. In the second method, RA disease was diagnosed using kNN, SVM, LR, DT, NB, and MLP algorithms, which are widely used in machine learning. To examine the effect of the patient’s history on RA disease diagnosis, two different models were used in machine learning techniques, one with and one without the patient’s history. The results of the fuzzy-based DSS were also compared with the diagnoses made by the specialist and the diagnoses made according to the 2010 ACR / EULAR RA classification criteria. The performed DSS has achieved a diagnostic success rate of 94.05% on 286 patients. In the study of machine learning techniques, the highest success rate was achieved with the LR model. While the success rate of the model was 91.25 % with only blood count data, the success rate was 97.90% with the addition of the patient’s history. In addition to the high success rate, the results show that the patient’s history is important in diagnosing RA disease.
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Affiliation(s)
- Fatih Tarakci
- Department of Computer Engineering, Faculty of Technology, Selcuk University, Konya, Turkey
| | - Ilker Ali Ozkan
- Department of Computer Engineering, Faculty of Technology, Selcuk University, Konya, Turkey
| | - Sema Yilmaz
- Division of Rheumatology, Selcuk University School of Medicine, Konya, Turkey
| | - Dilek Tezcan
- Division of Rheumatology, Selcuk University School of Medicine, Konya, Turkey
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Busnatu ȘS, Niculescu AG, Bolocan A, Andronic O, Pantea Stoian AM, Scafa-Udriște A, Stănescu AMA, Păduraru DN, Nicolescu MI, Grumezescu AM, Jinga V. A Review of Digital Health and Biotelemetry: Modern Approaches towards Personalized Medicine and Remote Health Assessment. J Pers Med 2022; 12:1656. [PMID: 36294795 PMCID: PMC9604784 DOI: 10.3390/jpm12101656] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 09/28/2022] [Accepted: 09/30/2022] [Indexed: 11/05/2022] Open
Abstract
With the prevalence of digitalization in all aspects of modern society, health assessment is becoming digital too. Taking advantage of the most recent technological advances and approaching medicine from an interdisciplinary perspective has allowed for important progress in healthcare services. Digital health technologies and biotelemetry devices have been more extensively employed for preventing, detecting, diagnosing, monitoring, and predicting the evolution of various diseases, without requiring wires, invasive procedures, or face-to-face interaction with medical personnel. This paper aims to review the concepts correlated to digital health, classify and describe biotelemetry devices, and present the potential of digitalization for remote health assessment, the transition to personalized medicine, and the streamlining of clinical trials.
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Affiliation(s)
- Ștefan Sebastian Busnatu
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | - Adelina-Gabriela Niculescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Politehnica University of Bucharest, 011061 Bucharest, Romania
| | - Alexandra Bolocan
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | - Octavian Andronic
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | | | - Alexandru Scafa-Udriște
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | | | - Dan Nicolae Păduraru
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | - Mihnea Ioan Nicolescu
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | - Alexandru Mihai Grumezescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Politehnica University of Bucharest, 011061 Bucharest, Romania
- Research Institute of the University of Bucharest—ICUB, University of Bucharest, 050657 Bucharest, Romania
- Academy of Romanian Scientists, Ilfov No. 3, 050044 Bucharest, Romania
| | - Viorel Jinga
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
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Kokkotis C, Giarmatzis G, Giannakou E, Moustakidis S, Tsatalas T, Tsiptsios D, Vadikolias K, Aggelousis N. An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data. Diagnostics (Basel) 2022; 12:2392. [PMID: 36292081 PMCID: PMC9600473 DOI: 10.3390/diagnostics12102392] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/26/2022] [Accepted: 09/27/2022] [Indexed: 11/16/2022] Open
Abstract
Stroke is an acute neurological dysfunction attributed to a focal injury of the central nervous system due to reduced blood flow to the brain. Nowadays, stroke is a global threat associated with premature death and huge economic consequences. Hence, there is an urgency to model the effect of several risk factors on stroke occurrence, and artificial intelligence (AI) seems to be the appropriate tool. In the present study, we aimed to (i) develop reliable machine learning (ML) prediction models for stroke disease; (ii) cope with a typical severe class imbalance problem, which is posed due to the stroke patients' class being significantly smaller than the healthy class; and (iii) interpret the model output for understanding the decision-making mechanism. The effectiveness of the proposed ML approach was investigated in a comparative analysis with six well-known classifiers with respect to metrics that are related to both generalization capability and prediction accuracy. The best overall false-negative rate was achieved by the Multi-Layer Perceptron (MLP) classifier (18.60%). Shapley Additive Explanations (SHAP) were employed to investigate the impact of the risk factors on the prediction output. The proposed AI method could lead to the creation of advanced and effective risk stratification strategies for each stroke patient, which would allow for timely diagnosis and the right treatments.
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Affiliation(s)
- Christos Kokkotis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Georgios Giarmatzis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Erasmia Giannakou
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | | | - Themistoklis Tsatalas
- Department of Physical Education and Sport Science, University of Thessaly, 38221 Trikala, Greece
| | - Dimitrios Tsiptsios
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Konstantinos Vadikolias
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Nikolaos Aggelousis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
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Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus. Curr Opin Rheumatol 2022; 34:374-381. [PMID: 36001343 DOI: 10.1097/bor.0000000000000902] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Machine learning is a computational tool that is increasingly used for the analysis of medical data and has provided the promise of more personalized care. RECENT FINDINGS The frequency with which machine learning analytics are reported in lupus research is comparable with that of rheumatoid arthritis and cancer, yet the clinical application of these computational tools has yet to be translated into better care. Considerable work has been applied to the development of machine learning models for lupus diagnosis, flare prediction, and classification of disease using histology or other medical images, yet few models have been tested in external datasets and independent centers. Application of machine learning has yet to be reported for lupus clinical trial enrichment and automated identification of eligible patients. Integration of machine learning into lupus clinical care and clinical trials would benefit from collaborative development between clinicians and data scientists. SUMMARY Although the application of machine learning to lupus data is at a nascent stage, initial results suggest a promising future.
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Hügle T. Learning from chess engines: how reinforcement learning could redefine clinical decision-making in rheumatology. Ann Rheum Dis 2022; 81:1072-1075. [PMID: 35135830 PMCID: PMC9279752 DOI: 10.1136/annrheumdis-2022-222141] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 01/27/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Thomas Hügle
- Department of Rheumatology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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38
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Oliosi E, Guede-Fernández F, Londral A. Machine Learning Approaches for the Frailty Screening: A Narrative Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19148825. [PMID: 35886674 PMCID: PMC9320589 DOI: 10.3390/ijerph19148825] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/18/2022] [Accepted: 07/19/2022] [Indexed: 12/16/2022]
Abstract
Frailty characterizes a state of impairments that increases the risk of adverse health outcomes such as physical limitation, lower quality of life, and premature death. Frailty prevention, early screening, and management of potential existing conditions are essential and impact the elderly population positively and on society. Advanced machine learning (ML) processing methods are one of healthcare’s fastest developing scientific and technical areas. Although research studies are being conducted in a controlled environment, their translation into the real world (clinical setting, which is often dynamic) is challenging. This paper presents a narrative review of the procedures for the frailty screening applied to the innovative tools, focusing on indicators and ML approaches. It results in six selected studies. Support vector machine was the most often used ML method. These methods apparently can identify several risk factors to predict pre-frail or frailty. Even so, there are some limitations (e.g., quality data), but they have enormous potential to detect frailty early.
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Affiliation(s)
- Eduarda Oliosi
- Value for Health CoLAB, 1150-190 Lisboa, Portugal; (E.O.); (F.G.-F.)
- LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal
| | - Federico Guede-Fernández
- Value for Health CoLAB, 1150-190 Lisboa, Portugal; (E.O.); (F.G.-F.)
- LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal
| | - Ana Londral
- Value for Health CoLAB, 1150-190 Lisboa, Portugal; (E.O.); (F.G.-F.)
- Comprehensive Health Research Center, NOVA Medical School, NOVA University of Lisbon, 1150-082 Lisboa, Portugal
- Correspondence:
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Knevel R, Knitza J, Hensvold A, Circiumaru A, Bruce T, Evans S, Maarseveen T, Maurits M, Beaart-van de Voorde L, Simon D, Kleyer A, Johannesson M, Schett G, Huizinga T, Svanteson S, Lindfors A, Klareskog L, Catrina A. Rheumatic?-A Digital Diagnostic Decision Support Tool for Individuals Suspecting Rheumatic Diseases: A Multicenter Pilot Validation Study. Front Med (Lausanne) 2022; 9:774945. [PMID: 35547229 PMCID: PMC9083190 DOI: 10.3389/fmed.2022.774945] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 03/15/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Digital diagnostic decision support tools promise to accelerate diagnosis and increase health care efficiency in rheumatology. Rheumatic? is an online tool developed by specialists in rheumatology and general medicine together with patients and patient organizations. It calculates a risk score for several rheumatic diseases. We ran a pilot study retrospectively testing Rheumatic? for its ability to differentiate symptoms from existing or emerging immune-mediated rheumatic diseases from other rheumatic and musculoskeletal complaints and disorders in patients visiting rheumatology clinics. Materials and Methods The performance of Rheumatic? was tested using in three university rheumatology centers: (A) patients at Risk for RA (Karolinska Institutet, n = 50 individuals with musculoskeletal complaints and anti-citrullinated protein antibody positivity) (B) patients with early joint swelling [dataset B (Erlangen) n = 52]. (C) Patients with early arthritis where the clinician considered it likely to be of auto-immune origin [dataset C (Leiden) n = 73]. In dataset A we tested whether Rheumatic? could predict the development of arthritis. In dataset B and C we tested whether Rheumatic? could predict the development of an immune-mediated rheumatic diseases. We examined the discriminative power of the total score with the Wilcoxon rank test and the area-under-the-receiver-operating-characteristic curve (AUC-ROC). Next, we calculated the test characteristics for these patients passing the first or second expert-based Rheumatic? scoring threshold. Results The total test scores differentiated between: (A) Individuals developing arthritis or not, median 245 vs. 163, P < 0.0001, AUC-ROC = 75.3; (B) patients with an immune-mediated arthritic disease or not median 191 vs. 107, P < 0.0001, AUC-ROC = 79.0; but less patients with an immune-mediated arthritic disease or not amongst those where the clinician already considered an immune mediated disease most likely (median 262 vs. 212, P < 0.0001, AUC-ROC = 53.6). Threshold-1 (advising to visit primary care doctor) was highly specific in dataset A and B (0.72, 0.87, and 0.23, respectively) and sensitive (0.67, 0.61, and 0.67). Threshold-2 (advising to visit rheumatologic care) was very specific in all three centers but not very sensitive: specificity of 1.0, 0.96, and 0.91, sensitivity 0.05, 0.07, 0.14 in dataset A, B, and C, respectively. Conclusion Rheumatic? is a web-based patient-centered multilingual diagnostic tool capable of differentiating immune-mediated rheumatic conditions from other musculoskeletal problems. The current scoring system needs to be further optimized.
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Affiliation(s)
- Rachel Knevel
- Leiden University Medical Center, Leiden, Netherlands
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Johannes Knitza
- Department of Internal Medicine 3, Friedrich-Alexander-Universität Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander-Universität Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Université Grenoble Alpe, Autonomie, Gérontologie, E-santé, Imagerie et Société, Grenoble, France
| | - Aase Hensvold
- Division of Rheumatology, Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
- Center for Rheumatology, Academic Specialist Center, Stockholm, Sweden
| | - Alexandra Circiumaru
- Division of Rheumatology, Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
- Center for Rheumatology, Academic Specialist Center, Stockholm, Sweden
| | - Tor Bruce
- Ocean Observations AB, Design Consultancy, Stockholm, Sweden
| | | | | | - Marc Maurits
- Leiden University Medical Center, Leiden, Netherlands
| | - Liesbeth Beaart-van de Voorde
- Leiden University Medical Center, Leiden, Netherlands
- Master Advanced Nursing Practice, University of Applied Sciences Leiden, Leiden, Netherlands
| | - David Simon
- Department of Internal Medicine 3, Friedrich-Alexander-Universität Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander-Universität Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Arnd Kleyer
- Department of Internal Medicine 3, Friedrich-Alexander-Universität Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander-Universität Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Martina Johannesson
- Division of Rheumatology, Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Georg Schett
- Department of Internal Medicine 3, Friedrich-Alexander-Universität Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie, Friedrich-Alexander-Universität Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Tom Huizinga
- Leiden University Medical Center, Leiden, Netherlands
| | | | | | - Lars Klareskog
- Division of Rheumatology, Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Anca Catrina
- Division of Rheumatology, Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
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Busnatu Ș, Niculescu AG, Bolocan A, Petrescu GED, Păduraru DN, Năstasă I, Lupușoru M, Geantă M, Andronic O, Grumezescu AM, Martins H. Clinical Applications of Artificial Intelligence-An Updated Overview. J Clin Med 2022; 11:jcm11082265. [PMID: 35456357 PMCID: PMC9031863 DOI: 10.3390/jcm11082265] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.
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Affiliation(s)
- Ștefan Busnatu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Adelina-Gabriela Niculescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
| | - Alexandra Bolocan
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - George E. D. Petrescu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Dan Nicolae Păduraru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Iulian Năstasă
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Mircea Lupușoru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Marius Geantă
- Centre for Innovation in Medicine, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania;
| | - Octavian Andronic
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Alexandru Mihai Grumezescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
- Research Institute of the University of Bucharest—ICUB, University of Bucharest, 050657 Bucharest, Romania
- Academy of Romanian Scientists, Ilfov No. 3, 50044 Bucharest, Romania
- Correspondence:
| | - Henrique Martins
- Faculty of Health Sciences, Universidade da Beira Interior, 6200-506 Covilha, Portugal;
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Hung SJ, Tsai HP, Wang YF, Ko WC, Wang JR, Huang SW. Assessment of the Risk of Severe Dengue Using Intrahost Viral Population in Dengue Virus Serotype 2 Patients via Machine Learning. Front Cell Infect Microbiol 2022; 12:831281. [PMID: 35223554 PMCID: PMC8866709 DOI: 10.3389/fcimb.2022.831281] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
Dengue virus, a positive-sense single-stranded RNA virus, continuously threatens human health. Although several criteria for evaluation of severe dengue have been recently established, the ability to prognose the risk of severe outcomes for dengue patients remains limited. Mutant spectra of RNA viruses, including single nucleotide variants (SNVs) and defective virus genomes (DVGs), contribute to viral virulence and growth. Here, we determine the potency of intrahost viral population in dengue patients with primary infection that progresses into severe dengue. A total of 65 dengue virus serotype 2 infected patients in primary infection including 17 severe cases were enrolled. We utilized deep sequencing to directly define the frequency of SNVs and detection times of DVGs in sera of dengue patients and analyzed their associations with severe dengue. Among the detected SNVs and DVGs, the frequencies of 9 SNVs and the detection time of 1 DVG exhibited statistically significant differences between patients with dengue fever and those with severe dengue. By utilizing the detected frequencies/times of the selected SNVs/DVG as features, the machine learning model showed high average with a value of area under the receiver operating characteristic curve (AUROC, 0.966 ± 0.064). The elevation of the frequency of SNVs at E (nucleotide position 995 and 2216), NS2A (nucleotide position 4105), NS3 (nucleotide position 4536, 4606), and NS5 protein (nucleotide position 7643 and 10067) and the detection times of the selected DVG that had a deletion junction in the E protein region (nucleotide positions of the junction: between 969 and 1022) increased the possibility of dengue patients for severe dengue. In summary, we demonstrated the detected frequencies/times of SNVs/DVG in dengue patients associated with severe disease and successfully utilized them to discriminate severe patients using machine learning algorithm. The identified SNVs and DVGs that are associated with severe dengue will expand our understanding of intrahost viral population in dengue pathogenesis.
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Affiliation(s)
- Su-Jhen Hung
- National Mosquito-Borne Diseases Control Research Center, National Health Research Institutes, Tainan, Taiwan
| | - Huey-Pin Tsai
- Department of Pathology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Medical Laboratory Science and Biotechnology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ya-Fang Wang
- National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Tainan, Taiwan
| | - Wen-Chien Ko
- Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Jen-Ren Wang
- Department of Pathology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Medical Laboratory Science and Biotechnology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Tainan, Taiwan
- Center of Infectious Disease and Signaling Research, National Cheng Kung University, Tainan, Taiwan
| | - Sheng-Wen Huang
- National Mosquito-Borne Diseases Control Research Center, National Health Research Institutes, Tainan, Taiwan
- *Correspondence: Sheng-Wen Huang,
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Joseph GB, McCulloch CE, Sohn JH, Pedoia V, Majumdar S, Link TM. AI MSK clinical applications: cartilage and osteoarthritis. Skeletal Radiol 2022; 51:331-343. [PMID: 34735607 DOI: 10.1007/s00256-021-03909-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 09/08/2021] [Accepted: 09/12/2021] [Indexed: 02/02/2023]
Abstract
The advancements of artificial intelligence (AI) for osteoarthritis (OA) applications have been rapid in recent years, particularly innovations of deep learning for image classification, lesion detection, cartilage segmentation, and prediction modeling of future knee OA development. This review article focuses on AI applications in OA research, first describing machine learning (ML) techniques and workflow, followed by how these algorithms are used for OA classification tasks through imaging and non-imaging-based ML models. Deep learning applications for OA research, including analysis of both radiographs for automatic detection of OA severity, and MR images for detection of cartilage/meniscus lesions and cartilage segmentation for automatic T2 quantification will be described. In addition, information on ML models that identify individuals at high risk of OA development will be provided. The future vision of machine learning applications in imaging of OA and cartilage hinges on implementation of AI for optimizing imaging protocols, quantitative assessment of cartilage, and automated analysis of disease burden yielding a faster and more efficient workflow for a radiologist with a higher level of reproducibility and precision. It may also provide risk assessment tools for individual patients, which is an integral part of precision medicine.
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Affiliation(s)
- Gabby B Joseph
- Department of Radiology and Biomedical Imaging, University of California, 185 Berry St, Suite 350, San Francisco, CA, 94158, USA.
| | - Charles E McCulloch
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Jae Ho Sohn
- Department of Radiology and Biomedical Imaging, University of California, 185 Berry St, Suite 350, San Francisco, CA, 94158, USA
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging, University of California, 185 Berry St, Suite 350, San Francisco, CA, 94158, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California, 185 Berry St, Suite 350, San Francisco, CA, 94158, USA
| | - Thomas M Link
- Department of Radiology and Biomedical Imaging, University of California, 185 Berry St, Suite 350, San Francisco, CA, 94158, USA
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Chang CC, Yeh JH, Chiu HC, Chen YM, Jhou MJ, Liu TC, Lu CJ. Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach. J Pers Med 2022; 12:32. [PMID: 35055347 PMCID: PMC8778268 DOI: 10.3390/jpm12010032] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/24/2021] [Accepted: 12/28/2021] [Indexed: 12/23/2022] Open
Abstract
Myasthenia gravis (MG), an acquired autoimmune-related neuromuscular disorder that causes muscle weakness, presents with varying severity, including myasthenic crisis (MC). Although MC can cause significant morbidity and mortality, specialized neuro-intensive care can produce a good long-term prognosis. Considering the outcomes of MG during hospitalization, it is critical to conduct risk assessments to predict the need for intensive care. Evidence and valid tools for the screening of critical patients with MG are lacking. We used three machine learning-based decision tree algorithms, including a classification and regression tree, C4.5, and C5.0, for predicting intensive care unit (ICU) admission of patients with MG. We included 228 MG patients admitted between 2015 and 2018. Among them, 88.2% were anti-acetylcholine receptors antibody positive and 4.7% were anti-muscle-specific kinase antibody positive. Twenty clinical variables were used as predictive variables. The C5.0 decision tree outperformed the other two decision tree and logistic regression models. The decision rules constructed by the best C5.0 model showed that the Myasthenia Gravis Foundation of America clinical classification at admission, thymoma history, azathioprine treatment history, disease duration, sex, and onset age were significant risk factors for the development of decision rules for ICU admission prediction. The developed machine learning-based decision tree can be a supportive tool for alerting clinicians regarding patients with MG who require intensive care, thereby improving the quality of care.
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Affiliation(s)
- Che-Cheng Chang
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan; (C.-C.C.); (Y.-M.C.)
- Ph.D. Program in Nutrition and Food Sciences, Human Ecology College, Fu Jen Catholic University, New Taipei City 242062, Taiwan
| | - Jiann-Horng Yeh
- School of Medicine, Fu Jen Catholic University, New Taipei City 24205, Taiwan; (J.-H.Y.); (H.-C.C.)
- Department of Neurology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 11101, Taiwan
- Department of Neurology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Hou-Chang Chiu
- School of Medicine, Fu Jen Catholic University, New Taipei City 24205, Taiwan; (J.-H.Y.); (H.-C.C.)
- Department of Neurology, Shuang-Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
| | - Yen-Ming Chen
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan; (C.-C.C.); (Y.-M.C.)
| | - Mao-Jhen Jhou
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan;
| | - Tzu-Chi Liu
- Department of Business Administration, Fu Jen Catholic University, New Taipei City, 242062, Taiwan;
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan;
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City 242062, Taiwan
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Davids J, Ashrafian H. AIM and mHealth, Smartphones and Apps. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Grainger R, Ung N. Digital technologies in rheumatology: new tools, new skills, and new care. INDIAN JOURNAL OF RHEUMATOLOGY 2022. [DOI: 10.4103/injr.injr_150_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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Lim AJW, Lim LJ, Ooi BNS, Koh ET, Tan JWL, Chong SS, Khor CC, Tucker-Kellogg L, Leong KP, Lee CG. Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients. EBioMedicine 2022; 75:103800. [PMID: 35022146 PMCID: PMC8808170 DOI: 10.1016/j.ebiom.2021.103800] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 12/19/2021] [Accepted: 12/20/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Major challenges in large scale genetic association studies include not only the identification of causative single nucleotide polymorphisms (SNPs), but also accounting for SNP-SNP interactions. This study thus proposes a novel feature engineering approach integrating potentially functional coding haplotypes (pfcHap) with machine-learning (ML) feature selection to identify biologically meaningful, possibly causative genetic factors, that take into consideration potential SNP-SNP interactions within the pfcHap, to best predict for methotrexate (MTX) response in rheumatoid arthritis (RA) patients. METHODS Exome sequencing from 349 RA patients were analysed, of which they were split into training and unseen test set. Inferred pfcHaps were combined with 30 non-genetic features to undergo ML recursive feature elimination with cross-validation using the training set. Predictive capacity and robustness of the selected features were assessed using six popular machine learning models through a train set cross-validation and evaluated in an unseen test set. FINDINGS Significantly, 100 features (95 pfcHaps, 5 non-genetic factors) were identified to have good predictive performance (AUC: 0.776-0.828; Sensitivity: 0.656-0.813; Specificity: 0.684-0.868) across all six ML models in an unseen test dataset for the prediction of MTX response in RA patients. INTERPRETATION Majority of the predictive pfcHap SNPs were predicted to be potentially functional and some of the genes in which the pfcHap resides in were identified to be associated with previously reported MTX/RA pathways. FUNDING Singapore Ministry of Health's National Medical Research Council (NMRC) [NMRC/CBRG/0095/2015; CG12Aug17; CGAug16M012; NMRC/CG/017/2013]; National Cancer Center Research Fund and block funding Duke-NUS Medical School.; Singapore Ministry of Education Academic Research Fund Tier 2 grant MOE2019-T2-1-138.
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Affiliation(s)
- Ashley J W Lim
- Dept of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Lee Jin Lim
- Dept of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Brandon N S Ooi
- Dept of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ee Tzun Koh
- Department of Rheumatology, Allergy and Immunology, Tan Tock Seng Hospital, Singapore
| | - Justina Wei Lynn Tan
- Department of Rheumatology, Allergy and Immunology, Tan Tock Seng Hospital, Singapore
| | - Samuel S Chong
- Dept of Pediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Chiea Chuen Khor
- Division of Human Genetics, Genome Institute of Singapore, Singapore
| | - Lisa Tucker-Kellogg
- Centre for Computational Biology, and Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore
| | - Khai Pang Leong
- Department of Rheumatology, Allergy and Immunology, Tan Tock Seng Hospital, Singapore; Clinical Research & Innovation Office, Tan Tock Seng Hospital, Singapore.
| | - Caroline G Lee
- Dept of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Div of Cellular & Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre Singapore, Singapore; Duke-NUS Medical School, Singapore; NUS Graduate School, National University of Singapore, Singapore.
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More S, Singla J. A generalized deep learning framework for automatic rheumatoid arthritis severity grading. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-212015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Knee rheumatoid arthritis (RA) is the highly prevalent, chronic, progressive condition in the world. To diagnose this disease in the early stage in detail analysis with magnetic resonance (MR) image is possible. The imaging modality feature allows unbiased assessment of joint space narrowing (JSN), cartilage volume, and other vital features. This provides a fine-grained RA severity evaluation of the knee, contrasted to the benchmark, and generally used Kellgren Lawrence (KL) assessment. In this research, an intelligent system is developed to predict KL grade from the knee dataset. Our approach is based on hybrid deep learning of 50 layers (ResNet50) with skip connections. The proposed approach also uses Adam optimizer to provide learning linearity in the training stage. Our approach yields KL grade and JSN for femoral and tibial tissue with lateral and medial compartments. Furthermore, the approach also yields area under curve (AUC) of 0.98, accuracy 96.85%, mean absolute error (MAE) 0.015, precision 98.31%, and other commonly used parameters for the existence of radiographic RA progression which is improved than the existing state-of-the-art.
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Affiliation(s)
- Sujeet More
- School of Computer Science and Engineering, Lovely Professional University, Jalandhar, Punjab, India
| | - Jimmy Singla
- School of Computer Science and Engineering, Lovely Professional University, Jalandhar, Punjab, India
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Garner AJ, Saatchi R, Ward O, Hawley DP. Juvenile Idiopathic Arthritis: A Review of Novel Diagnostic and Monitoring Technologies. Healthcare (Basel) 2021; 9:1683. [PMID: 34946409 PMCID: PMC8700900 DOI: 10.3390/healthcare9121683] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 12/29/2022] Open
Abstract
Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease of childhood and is characterized by an often insidious onset and a chronic relapsing-remitting course, once diagnosed. With successive flares of joint inflammation, joint damage accrues, often associated with pain and functional disability. The progressive nature and potential for chronic damage and disability caused by JIA emphasizes the critical need for a prompt and accurate diagnosis. This article provides a review of recent studies related to diagnosis, monitoring and management of JIA and outlines recent novel tools and techniques (infrared thermal imaging, three-dimensional imaging, accelerometry, artificial neural networks and fuzzy logic) which have demonstrated potential value in assessment and monitoring of JIA. The emergence of novel techniques to assist clinicians' assessments for diagnosis and monitoring of JIA has demonstrated promise; however, further research is required to confirm their clinical utility.
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Affiliation(s)
- Amelia J. Garner
- The Medical School, University of Sheffield, Sheffield S10 2TN, UK
| | - Reza Saatchi
- Industry and Innovation Research Institute, Sheffield Hallam University, Sheffield S1 1WB, UK;
| | - Oliver Ward
- Department of Paediatric Rheumatology, Sheffield Children’s Hospital, Sheffield S10 2TH, UK; (O.W.); (D.P.H.)
| | - Daniel P. Hawley
- Department of Paediatric Rheumatology, Sheffield Children’s Hospital, Sheffield S10 2TH, UK; (O.W.); (D.P.H.)
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Rehberg M, Giegerich C, Praestgaard A, van Hoogstraten H, Iglesias-Rodriguez M, Curtis JR, Gottenberg JE, Schwarting A, Castañeda S, Rubbert-Roth A, Choy EHS, the MOBILITY, MONARCH, TARGET, and ASCERTAIN investigators. Identification of a Rule to Predict Response to Sarilumab in Patients with Rheumatoid Arthritis Using Machine Learning and Clinical Trial Data. Rheumatol Ther 2021; 8:1661-1675. [PMID: 34519964 PMCID: PMC8572308 DOI: 10.1007/s40744-021-00361-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 08/11/2021] [Indexed: 11/22/2022] Open
Abstract
INTRODUCTION In rheumatoid arthritis, time spent using ineffective medications may lead to irreversible disease progression. Despite availability of targeted treatments, only a minority of patients achieve sustained remission, and little evidence exists to direct the choice of biologic disease-modifying antirheumatic drugs in individual patients. Machine learning was used to identify a rule to predict the response to sarilumab and discriminate between responses to sarilumab versus adalimumab, with a focus on clinically feasible blood biomarkers. METHODS The decision tree model GUIDE was trained using a data subset from the sarilumab trial with the most biomarker data, MOBILITY, to identify a rule to predict disease activity after sarilumab 200 mg. The training set comprised 18 categorical and 24 continuous baseline variables; some data were omitted from training and used for validation by the algorithm (cross-validation). The rule was tested using full datasets from four trials (MOBILITY, MONARCH, TARGET, and ASCERTAIN), focusing on the recommended sarilumab dose of 200 mg. RESULTS In the training set, the presence of anti-cyclic citrullinated peptide antibodies, combined with C-reactive protein > 12.3 mg/l, was identified as the "rule" that predicts American College of Rheumatology 20% response (ACR20) to sarilumab. In testing, the rule reliably predicted response to sarilumab in MOBILITY, MONARCH, and ASCERTAIN for many efficacy parameters (e.g., ACR70 and the 28-joint disease activity score using CRP [DAS28-CRP] remission). The rule applied less to TARGET, which recruited individuals refractory to tumor necrosis factor inhibitors. The potential clinical benefit of the rule was highlighted in a clinical scenario based on MONARCH data, which found that increased ACR70 rates could be achieved by treating either rule-positive patients with sarilumab or rule-negative patients with adalimumab. CONCLUSIONS Well-established and clinically feasible blood biomarkers can guide individual treatment choice. Real-world validation of the rule identified in this post hoc analysis is merited. CLINICAL TRIAL REGISTRATION NCT01061736, NCT02332590, NCT01709578, NCT01768572.
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Affiliation(s)
| | | | | | | | | | - Jeffrey R. Curtis
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL USA
| | | | - Andreas Schwarting
- Acura Kliniken Rheinland-Pfalz AG, Bad Kreuznach, Germany
- University Center of Autoimmunity, University Medical Center Mainz, Mainz, Germany
| | - Santos Castañeda
- Rheumatology Division, Hospital Universitario de La Princesa, IIS-IP and EPID-Future Cátedra, Autónoma University of Madrid (UAM), Madrid, Spain
| | | | - Ernest H. S. Choy
- Section of Rheumatology and Translational Research, Division of Infection and Immunity, Arthritis Research UK CREATE Centre and Welsh Arthritis Research Network (WARN), Cardiff University School of Medicine, Tenovus Building, Heath Park Campus, Cardiff, CF14 4XN UK
| | - the MOBILITY, MONARCH, TARGET, and ASCERTAIN investigators
- Sanofi, Frankfurt, Germany
- Sanofi, Cambridge, MA USA
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL USA
- Strasbourg University Hospital, Strasbourg, France
- Acura Kliniken Rheinland-Pfalz AG, Bad Kreuznach, Germany
- University Center of Autoimmunity, University Medical Center Mainz, Mainz, Germany
- Rheumatology Division, Hospital Universitario de La Princesa, IIS-IP and EPID-Future Cátedra, Autónoma University of Madrid (UAM), Madrid, Spain
- Kantonsspital St Gallen, St Gallen, Switzerland
- Section of Rheumatology and Translational Research, Division of Infection and Immunity, Arthritis Research UK CREATE Centre and Welsh Arthritis Research Network (WARN), Cardiff University School of Medicine, Tenovus Building, Heath Park Campus, Cardiff, CF14 4XN UK
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Spencer AK, Bandaria J, Leavy MB, Gliklich B, Su Z, Curhan G, Boussios C. Validation of a machine learning approach to estimate Clinical Disease Activity Index Scores for rheumatoid arthritis. RMD Open 2021; 7:rmdopen-2021-001781. [PMID: 34819386 PMCID: PMC8614150 DOI: 10.1136/rmdopen-2021-001781] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 10/29/2021] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE Disease activity measures, such as the Clinical Disease Activity Index (CDAI), are important tools for informing treatment decisions and monitoring patient outcomes in rheumatoid arthritis (RA). Yet, documentation of CDAI scores in electronic medical records and other real-world data sources is inconsistent, making it challenging to use these data for research. The purpose of this study was to validate a machine learning model to estimate CDAI scores for patients with RA using clinical notes. METHODS A machine learning model was developed to estimate CDAI score values using clinical notes from a specific rheumatology visit. Data from the OM1 RA Registry were used to create a training cohort of 56 177 encounters and a separate validation cohort of 18 726 encounters, 11 985 of which passed a model-derived confidence filter; all included encounters had both a clinician-recorded CDAI score and a clinical note. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), positive predictive value (PPV) and negative predictive value (NPV), calculated using a binarised version of the outcome. The Spearman's R and Pearson's R values were also calculated. RESULTS The model had a PPV of 0.80, NPV of 0.84 and AUC of 0.88 when evaluating performance using the binarised version of the outcome. The model had a Spearman's R value of 0.72 and a Pearson's R value of 0.69 when evaluating performance using the continuous CDAI numeric scores. CONCLUSION A machine learning model estimates CDAI scores from clinical notes with good performance. Application of the model to real-world data sets may allow estimated CDAI scores to be used for research purposes.
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Affiliation(s)
| | | | | | | | - Zhaohui Su
- Biostatistics, OM1 Inc, Boston, Massachusetts, USA
| | - Gary Curhan
- Research, OM1 Inc, Boston, Massachusetts, USA
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