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Zhou X, Cai F, Li S, Li G, Zhang C, Xie J, Yang Y. Machine learning techniques for prediction in pregnancy complicated by autoimmune rheumatic diseases: Applications and challenges. Int Immunopharmacol 2024; 134:112238. [PMID: 38735259 DOI: 10.1016/j.intimp.2024.112238] [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: 03/05/2024] [Revised: 05/01/2024] [Accepted: 05/08/2024] [Indexed: 05/14/2024]
Abstract
Autoimmune rheumatic diseases are chronic conditions affecting multiple systems and often occurring in young women of childbearing age. The diseases and the physiological characteristics of pregnancy significantly impact maternal-fetal health and pregnancy outcomes. Currently, the integration of big data with healthcare has led to the increasing popularity of using machine learning (ML) to mine clinical data for studying pregnancy complications. In this review, we introduce the basics of ML and the recent advances and trends of ML in different prediction applications for common pregnancy complications by autoimmune rheumatic diseases. Finally, the challenges and future for enhancing the accuracy, reliability, and clinical applicability of ML in prediction have been discussed. This review will provide insights into the utilization of ML in identifying and assisting clinical decision-making for pregnancy complications, while also establishing a foundation for exploring comprehensive management strategies for pregnancy and enhancing maternal and child health.
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Affiliation(s)
- Xiaoshi Zhou
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Feifei Cai
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Shiran Li
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Guolin Li
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Changji Zhang
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Jingxian Xie
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; College of Pharmacy, Southwest Medical University, Luzhou, China
| | - Yong Yang
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
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Trottet C, Allam A, Horvath AN, Finckh A, Hügle T, Adler S, Kyburz D, Micheroli R, Krauthammer M, Ospelt C. Explainable deep learning for disease activity prediction in chronic inflammatory joint diseases. PLOS DIGITAL HEALTH 2024; 3:e0000422. [PMID: 38935600 PMCID: PMC11210792 DOI: 10.1371/journal.pdig.0000422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 05/27/2024] [Indexed: 06/29/2024]
Abstract
Analysing complex diseases such as chronic inflammatory joint diseases (CIJDs), where many factors influence the disease evolution over time, is a challenging task. CIJDs are rheumatic diseases that cause the immune system to attack healthy organs, mainly the joints. Different environmental, genetic and demographic factors affect disease development and progression. The Swiss Clinical Quality Management in Rheumatic Diseases (SCQM) Foundation maintains a national database of CIJDs documenting the disease management over time for 19'267 patients. We propose the Disease Activity Score Network (DAS-Net), an explainable multi-task learning model trained on patients' data with different arthritis subtypes, transforming longitudinal patient journeys into comparable representations and predicting multiple disease activity scores. First, we built a modular model composed of feed-forward neural networks, long short-term memory networks and attention layers to process the heterogeneous patient histories and predict future disease activity. Second, we investigated the utility of the model's computed patient representations (latent embeddings) to identify patients with similar disease progression. Third, we enhanced the explainability of our model by analysing the impact of different patient characteristics on disease progression and contrasted our model outcomes with medical expert knowledge. To this end, we explored multiple feature attribution methods including SHAP, attention attribution and feature weighting using case-based similarity. Our model outperforms temporal and non-temporal neural network, tree-based, and naive static baselines in predicting future disease activity scores. To identify similar patients, a k-nearest neighbours regression algorithm applied to the model's computed latent representations outperforms baseline strategies that use raw input features representation.
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Affiliation(s)
- Cécile Trottet
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Ahmed Allam
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Aron N. Horvath
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Axel Finckh
- Division of Rheumatology, Department of Medicine, Faculty of Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Thomas Hügle
- Department of Rheumatology, Lausanne University Hospital, Lausanne, Switzerland
| | - Sabine Adler
- Department of Rheumatology and Immunology, Kantonsspital Aarau, Aarau, Switzerland
- Department of Rheumatology and Immunology, Inselspital - University Hospital Bern, Bern, Switzerland
| | - Diego Kyburz
- Department of Rheumatology, University Hospital Basel, Basel, Switzerland
| | - Raphael Micheroli
- Center of Experimental Rheumatology, Department of Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Michael Krauthammer
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Biomedical Informatics DFL, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Caroline Ospelt
- Center of Experimental Rheumatology, Department of Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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3
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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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4
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Stoel BC, Staring M, Reijnierse M, van der Helm-van Mil AHM. Deep learning in rheumatological image interpretation. Nat Rev Rheumatol 2024; 20:182-195. [PMID: 38332242 DOI: 10.1038/s41584-023-01074-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/20/2023] [Indexed: 02/10/2024]
Abstract
Artificial intelligence techniques, specifically deep learning, have already affected daily life in a wide range of areas. Likewise, initial applications have been explored in rheumatology. Deep learning might not easily surpass the accuracy of classic techniques when performing classification or regression on low-dimensional numerical data. With images as input, however, deep learning has become so successful that it has already outperformed the majority of conventional image-processing techniques developed during the past 50 years. As with any new imaging technology, rheumatologists and radiologists need to consider adapting their arsenal of diagnostic, prognostic and monitoring tools, and even their clinical role and collaborations. This adaptation requires a basic understanding of the technical background of deep learning, to efficiently utilize its benefits but also to recognize its drawbacks and pitfalls, as blindly relying on deep learning might be at odds with its capabilities. To facilitate such an understanding, it is necessary to provide an overview of deep-learning techniques for automatic image analysis in detecting, quantifying, predicting and monitoring rheumatic diseases, and of currently published deep-learning applications in radiological imaging for rheumatology, with critical assessment of possible limitations, errors and confounders, and conceivable consequences for rheumatologists and radiologists in clinical practice.
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Affiliation(s)
- Berend C Stoel
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Marius Staring
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Monique Reijnierse
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
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Xie Q, Li N, Lu Y, Chen J, Qu W, Geng L, Sun L. Prediction of Treatment Effect of SLE-ITP Patients Based on Cost-Sensitive Neural Network and Variational Autoencoder. J Clin Rheumatol 2024:00124743-990000000-00191. [PMID: 38427830 DOI: 10.1097/rhu.0000000000002078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2024]
Abstract
OBJECTIVE The aim of the study was to examine the factors influencing the therapeutic effect of patients with systemic lupus erythematosus combined with immune thrombocytopenia (SLE-ITP) and develop a prediction model to predict the therapeutic effect of SLE-ITP. METHODS Three hundred twenty-four SLE-ITP patients were retrieved from the electronic health record database of SLE patients in Jiangsu Province according to the latest treatment response criteria for ITP. We adopted the Cox model based on the least absolute shrinkage and selection operator to explore the impact factors affecting patient therapeutic effect, and we developed neural network model to predict therapeutic effect, and in prediction model, cost-sensitivity was introduced to address data category imbalance, and variational autoencoder was used to achieve data augmentation. The performance of each model was evaluated by accuracy and the area under the receiver operator curve. RESULTS The results showed that B-lymphocyte count, H-cholesterol level, complement-3 level, anticardiolipin antibody, and so on could be used as predictors of SLE-ITP curative effect, and abnormal levels of alanine transaminase, immunoglobulin A, and apolipoprotein B predicted adverse treatment response. The neural network treatment effect prediction model based on cost-sensitivity and variational autoencoder was better than the traditional classifiers, with an overall accuracy rate closed to 0.9 and a specificity of more than 0.9, which was useful for clinical practice to identify patients at risk of ineffective treatment response and to achieve better individualized management. CONCLUSIONS By predicting the curative effect of SLE-ITP, the severity of patients can be determined, and then the best treatment strategy can be planned to avoid ineffective treatment.
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Affiliation(s)
| | - Na Li
- From the Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Youbei Lu
- School of Computer and Information, Hohai University, Nanjing, China
| | - Jiaqi Chen
- School of Computer and Information, Hohai University, Nanjing, China
| | - Wenqiang Qu
- Intelligent Software Research Center, Nanjing Institute of Software Technology, China
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Gleber C, Fear K. Diagnostic reasoning in the age of artificial intelligence: Synergy or opposition? J Hosp Med 2024. [PMID: 38340350 DOI: 10.1002/jhm.13295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 01/21/2024] [Accepted: 01/28/2024] [Indexed: 02/12/2024]
Affiliation(s)
- Conrad Gleber
- University of Rochester Medical Center, Rochester, New York, USA
| | - Kathleen Fear
- UR Health Lab, University of Rochester Medical Center, Rochester, New York, USA
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7
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Wang Y, Wei W, Ouyang R, Chen R, Wang T, Yuan X, Wang F, Hou H, Wu S. Novel multiclass classification machine learning approach for the early-stage classification of systemic autoimmune rheumatic diseases. Lupus Sci Med 2024; 11:e001125. [PMID: 38302133 PMCID: PMC10831448 DOI: 10.1136/lupus-2023-001125] [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: 12/06/2023] [Accepted: 01/19/2024] [Indexed: 02/03/2024]
Abstract
OBJECTIVE Systemic autoimmune rheumatic diseases (SARDs) encompass a diverse group of complex conditions with overlapping clinical features, making accurate diagnosis challenging. This study aims to develop a multiclass machine learning (ML) model for early-stage SARDs classification using accessible laboratory indicators. METHODS A total of 925 SARDs patients were included, categorised into SLE, Sjögren's syndrome (SS) and inflammatory myositis (IM). Clinical characteristics and laboratory markers were collected and nine key indicators, including anti-dsDNA, anti-SS-A60, anti-Sm/nRNP, antichromatin, anti-dsDNA (indirect immunofluorescence assay), haemoglobin (Hb), platelet, neutrophil percentage and cytoplasmic patterns (AC-19, AC-20), were selected for model building. Various ML algorithms were used to construct a tripartite classification ML model. RESULTS Patients were divided into two cohorts, cohort 1 was used to construct a tripartite classification model. Among models assessed, the random forest (RF) model demonstrated superior performance in distinguishing SLE, IM and SS (with area under curve=0.953, 0.903 and 0.836; accuracy= 0.892, 0.869 and 0.857; sensitivity= 0.890, 0.868 and 0.795; specificity= 0.910, 0.836 and 0.748; positive predictive value=0.922, 0.727 and 0.663; and negative predictive value= 0.854, 0.915 and 0.879). The RF model excelled in classifying SLE (precision=0.930, recall=0.985, F1 score=0.957). For IM and SS, RF model outcomes were (precision=0.793, 0.950; recall=0.920, 0.679; F1 score=0.852, 0.792). Cohort 2 served as an external validation set, achieving an overall accuracy of 87.3%. Individual classification performances for SLE, SS and IM were excellent, with precision, recall and F1 scores specified. SHAP analysis highlighted significant contributions from antibody profiles. CONCLUSION This pioneering multiclass ML model, using basic laboratory indicators, enhances clinical feasibility and demonstrates promising potential for SARDs classification. The collaboration of clinical expertise and ML offers a nuanced approach to SARDs classification, with potential for enhanced patient care.
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Affiliation(s)
- Yun Wang
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wei Wei
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Renren Ouyang
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Rujia Chen
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ting Wang
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xu Yuan
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Feng Wang
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hongyan Hou
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shiji Wu
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
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Moon SJ, Lee S, Hwang J, Lee J, Kang S, Cha HS. Performances of machine learning algorithms in discriminating sacroiliitis features on MRI: a systematic review. RMD Open 2023; 9:e003783. [PMID: 37996126 PMCID: PMC10668284 DOI: 10.1136/rmdopen-2023-003783] [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: 10/03/2023] [Accepted: 11/08/2023] [Indexed: 11/25/2023] Open
Abstract
OBJECTIVES Summarise the evidence of the performance of the machine learning algorithm in discriminating sacroiliitis features on MRI and compare it with the accuracy of human physicians. METHODS MEDLINE, EMBASE, CIHNAL, Web of Science, IEEE, American College of Rheumatology and European Alliance of Associations for Rheumatology abstract archives were searched for studies published between 2008 and 4 June 2023. Two authors independently screened and extracted the variables, and the results are presented using tables and forest plots. RESULTS Ten studies were selected from 2381. Over half of the studies used deep learning models, using Assessment of Spondyloarthritis International Society sacroiliitis criteria as the ground truth, and manually extracted the regions of interest. All studies reported the area under the curve as a performance index, ranging from 0.76 to 0.99. Sensitivity and specificity were the second-most commonly reported indices, with sensitivity ranging from 0.56 to 1.00 and specificity ranging from 0.67 to 1.00; these results are comparable to a radiologist's sensitivity of 0.67-1.00 and specificity of 0.78-1.00 in the same cohort. More than half of the studies showed a high risk of bias in the analysis domain of quality appraisal owing to the small sample size or overfitting issues. CONCLUSION The performance of machine learning algorithms in discriminating sacroiliitis features on MRI varied owing to the high heterogeneity between studies and the small sample sizes, overfitting, and under-reporting issues of individual studies. Further well-designed and transparent studies are required.
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Affiliation(s)
- Sun Jae Moon
- Department of Medicine, Santa Marie 24 Clinic, Seongnam-si, Korea (the Republic of)
| | - Seulkee Lee
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (the Republic of)
| | - Jinseub Hwang
- Department of Data Science, Daegu University, Gyeongsan-si, Korea (the Republic of)
| | - Jaejoon Lee
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (the Republic of)
| | - Seonyoung Kang
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (the Republic of)
| | - Hoon-Suk Cha
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (the Republic of)
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Hughes BK, Wallis R, Bishop CL. Yearning for machine learning: applications for the classification and characterisation of senescence. Cell Tissue Res 2023; 394:1-16. [PMID: 37016180 PMCID: PMC10558380 DOI: 10.1007/s00441-023-03768-4] [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: 11/15/2022] [Accepted: 03/05/2023] [Indexed: 04/06/2023]
Abstract
Senescence is a widely appreciated tumour suppressive mechanism, which acts as a barrier to cancer development by arresting cell cycle progression in response to harmful stimuli. However, senescent cell accumulation becomes deleterious in aging and contributes to a wide range of age-related pathologies. Furthermore, senescence has beneficial roles and is associated with a growing list of normal physiological processes including wound healing and embryonic development. Therefore, the biological role of senescent cells has become increasingly nuanced and complex. The emergence of sophisticated, next-generation profiling technologies, such as single-cell RNA sequencing, has accelerated our understanding of the heterogeneity of senescence, with distinct final cell states emerging within models as well as between cell types and tissues. In order to explore data sets of increasing size and complexity, the senescence field has begun to employ machine learning (ML) methodologies to probe these intricacies. Most notably, ML has been used to aid the classification of cells as senescent, as well as to characterise the final senescence phenotypes. Here, we provide a background to the principles of ML tasks, as well as some of the most commonly used methodologies from both traditional and deep ML. We focus on the application of these within the context of senescence research, by addressing the utility of ML for the analysis of data from different laboratory technologies (microscopy, transcriptomics, proteomics, methylomics), as well as the potential within senolytic drug discovery. Together, we aim to highlight both the progress and potential for the application of ML within senescence research.
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Affiliation(s)
- Bethany K Hughes
- Blizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK
| | - Ryan Wallis
- Blizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK
| | - Cleo L Bishop
- Blizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK.
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Rothe F, Berger J, Welker P, Fiebelkorn R, Kupper S, Kiesel D, Gedat E, Ohrndorf S. Fluorescence optical imaging feature selection with machine learning for differential diagnosis of selected rheumatic diseases. Front Med (Lausanne) 2023; 10:1228833. [PMID: 37671403 PMCID: PMC10475553 DOI: 10.3389/fmed.2023.1228833] [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: 05/25/2023] [Accepted: 07/28/2023] [Indexed: 09/07/2023] Open
Abstract
Background and objective Accurate and fast diagnosis of rheumatic diseases affecting the hands is essential for further treatment decisions. Fluorescence optical imaging (FOI) visualizes inflammation-induced impaired microcirculation by increasing signal intensity, resulting in different image features. This analysis aimed to find specific image features in FOI that might be important for accurately diagnosing different rheumatic diseases. Patients and methods FOI images of the hands of patients with different types of rheumatic diseases, such as rheumatoid arthritis (RA), osteoarthritis (OA), and connective tissue diseases (CTD), were assessed in a reading of 20 different image features in three phases of the contrast agent dynamics, yielding 60 different features for each patient. The readings were analyzed for mutual differential diagnosis of the three diseases (One-vs-One) and each disease in all data (One-vs-Rest). In the first step, statistical tools and machine-learning-based methods were applied to reveal the importance rankings of the features, that is, to find features that contribute most to the model-based classification. In the second step machine learning with a stepwise increasing number of features was applied, sequentially adding at each step the most crucial remaining feature to extract a minimized subset that yields the highest diagnostic accuracy. Results In total, n = 605 FOI of both hands were analyzed (n = 235 with RA, n = 229 with OA, and n = 141 with CTD). All classification problems showed maximum accuracy with a reduced set of image features. For RA-vs.-OA, five features were needed for high accuracy. For RA-vs.-CTD ten, OA-vs.-CTD sixteen, RA-vs.-Rest five, OA-vs.-Rest eleven, and CTD-vs-Rest fifteen, features were needed, respectively. For all problems, the final importance ranking of the features with respect to the contrast agent dynamics was determined. Conclusions With the presented investigations, the set of features in FOI examinations relevant to the differential diagnosis of the selected rheumatic diseases could be remarkably reduced, providing helpful information for the physician.
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Affiliation(s)
- Felix Rothe
- Telematics Research Group, Wildau Technical University of Applied Sciences, Wildau, Germany
| | | | - Pia Welker
- Institute of Functional Anatomy, Charité—Universitätsmedizin Berlin, Berlin, Germany
| | - Richard Fiebelkorn
- Telematics Research Group, Wildau Technical University of Applied Sciences, Wildau, Germany
| | - Stefan Kupper
- Telematics Research Group, Wildau Technical University of Applied Sciences, Wildau, Germany
| | - Denise Kiesel
- Institute of Functional Anatomy, Charité—Universitätsmedizin Berlin, Berlin, Germany
| | - Egbert Gedat
- Telematics Research Group, Wildau Technical University of Applied Sciences, Wildau, Germany
- Xiralite GmbH, Berlin, Germany
| | - Sarah Ohrndorf
- Department of Rheumatology and Clinical Immunology, Charité—Universitätsmedizin Berlin, Berlin, Germany
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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: 4.0] [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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12
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Dou J, Dawuti W, Li J, Zhao H, Zhou R, Zhou J, Lin R, Lü G. Rapid detection of serological biomarkers in gallbladder carcinoma using fourier transform infrared spectroscopy combined with machine learning. Talanta 2023; 259:124457. [PMID: 36989965 DOI: 10.1016/j.talanta.2023.124457] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 03/10/2023] [Accepted: 03/12/2023] [Indexed: 03/29/2023]
Abstract
Gallbladder cancer (GBC) is the most common malignant tumour of the biliary tract. GBC is difficult to diagnose and treat at an early stage because of the lack of effective serum markers and typical symptoms, resulting in low survival rates. This study aimed to investigate the applicability of dried serum Fourier-transform infrared (FTIR) spectroscopy combined with machine learning algorithms to correctly differentiate patients with GBC from patients with gallbladder disease (GBD), cholangiocarcinoma (CCA), hepatocellular carcinoma (HCC) and healthy individuals. The differentiation between healthy individuals and GBC serum was better using principal component analysis (PCA) and linear discriminant analysis (LDA) for six spectral regions, especially in the protein (1710-1475 cm-1) and combined (1710-1475 + 1354-980 cm-1) region. However, the PCA-LDA model poorly differentiated GBC from GBD, CCA, and HCC in serum spectra. We evaluated the PCA- LDA, PCA-support vector machine (SVM), and radial basis kernel function support vector machine (RBF-SVM) models for GBC diagnosis and found that the RBF-SVM model performed the best, with 88.24-95% accuracy, 95.83% sensitivity, and 78.38-94.44% specificity in the 1710-1475 + 1354-980 cm-1 region. This study demonstrated that serum FTIR spectroscopy combined with the RBF-SVM algorithm has great clinical potential for GBC screening.
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Motta F, Morandini P, Maffia F, Vecellio M, Tonutti A, De Santis M, Costanzo A, Puggioni F, Savevski V, Selmi C. Connecting the use of innovative treatments and glucocorticoids with the multidisciplinary evaluation through rule-based natural-language processing: a real-world study on patients with rheumatoid arthritis, psoriatic arthritis, and psoriasis. Front Med (Lausanne) 2023; 10:1179240. [PMID: 37387783 PMCID: PMC10301822 DOI: 10.3389/fmed.2023.1179240] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 05/15/2023] [Indexed: 07/01/2023] Open
Abstract
Background The impact of a multidisciplinary management of rheumatoid arthritis (RA), psoriatic arthritis (PsA), and psoriasis on systemic glucocorticoids or innovative treatments remains unknown. Rule-based natural language processing and text extraction help to manage large datasets of unstructured information and provide insights into the profile of treatment choices. Methods We obtained structured information from text data of outpatient visits between 2017 and 2022 using regular expressions (RegEx) to define elastic search patterns and to consider only affirmative citation of diseases or prescribed therapy by detecting negations. Care processes were described by binary flags which express the presence of RA, PsA and psoriasis and the prescription of glucocorticoids and biologics or small molecules in each cases. Logistic regression analyses were used to train the classifier to predict outcomes using the number of visits and the other specialist visits as the main variables. Results We identified 1743 patients with RA, 1359 with PsA and 2,287 with psoriasis, accounting for 5,677, 4,468 and 7,770 outpatient visits, respectively. Among these, 25% of RA, 32% of PsA and 25% of psoriasis cases received biologics or small molecules, while 49% of RA, 28% of PsA, and 40% of psoriasis cases received glucocorticoids. Patients evaluated also by other specialists were treated more frequently with glucocorticoids (70% vs. 49% for RA, 60% vs. 28% for PsA, 51% vs. 40% for psoriasis; p < 0.001) as well as with biologics/small molecules (49% vs. 25% for RA, 64% vs. 32% in PsA; 51% vs. 25% for psoriasis; p < 0.001) compared to cases seen only by the main specialist. Conclusion Patients with RA, PsA, or psoriasis undergoing multiple evaluations are more likely to receive innovative treatments or glucocorticoids, possibly reflecting more complex cases.
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Affiliation(s)
- Francesca Motta
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Division of Rheumatology and Clinical Immunology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Pierandrea Morandini
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Fiore Maffia
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Matteo Vecellio
- Division of Rheumatology and Clinical Immunology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Centro Ricerche Fondazione Italiana Ricerca Sull'Artrite (FIRA), Fondazione Pisana per la Scienza ONLUS, San Giuliano Terme (Pisa), Milan, Italy
| | - Antonio Tonutti
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Division of Rheumatology and Clinical Immunology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Maria De Santis
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Division of Rheumatology and Clinical Immunology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Antonio Costanzo
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Division of Dermatology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Francesca Puggioni
- Division of Asthma, Allergy and Personalized Medicine, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Carlo Selmi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Division of Rheumatology and Clinical Immunology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
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14
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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: 3.0] [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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15
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Galozzi P, Basso D, Plebani M, Padoan A. Artificial Intelligence and laboratory data in rheumatic diseases. Clin Chim Acta 2023; 546:117388. [PMID: 37187221 DOI: 10.1016/j.cca.2023.117388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 05/17/2023]
Abstract
Artificial intelligence (AI)-based medical technologies are rapidly evolving into actionable solutions for clinical practice. Machine learning (ML) algorithms can process increasing amounts of laboratory data such as gene expression immunophenotyping data and biomarkers. In recent years, the analysis of ML has become particularly useful for the study of complex chronic diseases, such as rheumatic diseases, heterogenous conditions with multiple triggers. Numerous studies have used ML to classify patients and improve diagnosis, to stratify the risk and determine disease subtypes, as well as to discover biomarkers and gene signatures. This review aims to provide examples of ML models for specific rheumatic diseases using laboratory data and some insights into relevant strengths and limitations. A better understanding and future application of these analytical strategies could facilitate the development of precision medicine for rheumatic patients.
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Affiliation(s)
- Paola Galozzi
- Department of Medicine-DIMED, University of Padova, Padova, Italy.
| | - Daniela Basso
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Mario Plebani
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Andrea Padoan
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy
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16
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Forrest IS, Petrazzini BO, Duffy Á, Park JK, O'Neal AJ, Jordan DM, Rocheleau G, Nadkarni GN, Cho JH, Blazer AD, Do R. A machine learning model identifies patients in need of autoimmune disease testing using electronic health records. Nat Commun 2023; 14:2385. [PMID: 37169741 PMCID: PMC10130143 DOI: 10.1038/s41467-023-37996-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 04/05/2023] [Indexed: 05/13/2023] Open
Abstract
Systemic autoimmune rheumatic diseases (SARDs) can lead to irreversible damage if left untreated, yet these patients often endure long diagnostic journeys before being diagnosed and treated. Machine learning may help overcome the challenges of diagnosing SARDs and inform clinical decision-making. Here, we developed and tested a machine learning model to identify patients who should receive rheumatological evaluation for SARDs using longitudinal electronic health records of 161,584 individuals from two institutions. The model demonstrated high performance for predicting cases of autoantibody-tested individuals in a validation set, an external test set, and an independent cohort with a broader case definition. This approach identified more individuals for autoantibody testing compared with current clinical standards and a greater proportion of autoantibody carriers among those tested. Diagnoses of SARDs and other autoimmune conditions increased with higher model probabilities. The model detected a need for autoantibody testing and rheumatology encounters up to five years before the test date and assessment date, respectively. Altogether, these findings illustrate that the clinical manifestations of a diverse array of autoimmune conditions are detectable in electronic health records using machine learning, which may help systematize and accelerate autoimmune testing.
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Affiliation(s)
- Iain S Forrest
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ben O Petrazzini
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Áine Duffy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joshua K Park
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Anya J O'Neal
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Daniel M Jordan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ghislain Rocheleau
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Judy H Cho
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ashira D Blazer
- Division of Rheumatology, Hospital for Special Surgery, New York, NY, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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17
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Baek IW, Jung SM, Park YJ, Park KS, Kim KJ. Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting. Arthritis Res Ther 2023; 25:65. [PMID: 37081563 PMCID: PMC10116698 DOI: 10.1186/s13075-023-03050-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 04/12/2023] [Indexed: 04/22/2023] Open
Abstract
BACKGROUND Predicting radiographic progression in axial spondyloarthritis (axSpA) remains limited because of the complex interaction between multiple associated factors and individual variability in real-world settings. Hence, we tested the feasibility of artificial neural network (ANN) models to predict radiographic progression in axSpA. METHODS In total, 555 patients with axSpA were split into training and testing datasets at a 3:1 ratio. A generalized linear model (GLM) and ANN models were fitted based on the baseline clinical characteristics and treatment-dependent variables for the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS) of the radiographs at follow-up time points. The mSASSS prediction was evaluated, and explainable machine learning methods were used to provide insights into the model outcome or prediction. RESULTS The R2 values of the fitted models were in the range of 0.90-0.95 and ANN with an input of mSASSS as the number of each score performed better (root mean squared error (RMSE) = 2.83) than GLM or input of mSASSS as a total score (RMSE = 2.99-3.57). The ANN also effectively captured complex interactions among variables and their contributions to the transition of mSASSS over time in the fitted models. Structural changes constituting the mSASSS scoring systems were the most important contributing factors, and no detectable structural abnormalities at baseline were the most significant factors suppressing mSASSS change. CONCLUSIONS Clinical and radiographic data-driven ANN allows precise mSASSS prediction in real-world settings. Correct evaluation and prediction of spinal structural changes could be beneficial for monitoring patients with axSpA and developing a treatment plan.
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Affiliation(s)
- In-Woon Baek
- Division of Rheumatology, Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul, Republic of Korea
| | - Seung Min Jung
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, 93 Jungbu-Daero, Paldal-Gu, Suwon, Gyeonggi-Do, 16247, Republic of Korea
| | - Yune-Jung Park
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, 93 Jungbu-Daero, Paldal-Gu, Suwon, Gyeonggi-Do, 16247, Republic of Korea
| | - Kyung-Su Park
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, 93 Jungbu-Daero, Paldal-Gu, Suwon, Gyeonggi-Do, 16247, Republic of Korea
| | - Ki-Jo Kim
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, 93 Jungbu-Daero, Paldal-Gu, Suwon, Gyeonggi-Do, 16247, Republic of Korea.
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18
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Rischke S, Hahnefeld L, Burla B, Behrens F, Gurke R, Garrett TJ. Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects. J Mass Spectrom Adv Clin Lab 2023; 28:47-55. [PMID: 36872952 PMCID: PMC9982001 DOI: 10.1016/j.jmsacl.2023.02.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 02/14/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023] Open
Abstract
Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.
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Key Words
- (U)HPLC (Ultra-), High pressure liquid chromatography
- Biomarker Discovery Study
- HILIC, Hydrophilic interaction liquid chromatography
- HRMS, High resolution mass spectrometry
- LC-MS, Liquid chromatography – mass spectrometry
- LC-MS-Based Clinical Research
- Lipidomics
- MRM, Multiple reaction monitoring
- Metabolomics
- PCA, Principal component analysis
- QA, Quality assurance
- QC, Quality control
- RF, Random Forest
- RP, Reversed phase
- SVA, Support vector machine
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Affiliation(s)
- S Rischke
- pharmazentrum frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - L Hahnefeld
- pharmazentrum frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - B Burla
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - F Behrens
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany.,Division of Rheumatology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - R Gurke
- pharmazentrum frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - T J Garrett
- Department of Pathology, Immunology and Laboratory Medicine and Southeast Center for Integrated Metabolomics, University of Florida, Gainesville, FL 32611, USA
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Classification of COVID-19 Patients into Clinically Relevant Subsets by a Novel Machine Learning Pipeline Using Transcriptomic Features. Int J Mol Sci 2023; 24:ijms24054905. [PMID: 36902333 PMCID: PMC10002748 DOI: 10.3390/ijms24054905] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 02/24/2023] [Accepted: 03/01/2023] [Indexed: 03/06/2023] Open
Abstract
The persistent impact of the COVID-19 pandemic and heterogeneity in disease manifestations point to a need for innovative approaches to identify drivers of immune pathology and predict whether infected patients will present with mild/moderate or severe disease. We have developed a novel iterative machine learning pipeline that utilizes gene enrichment profiles from blood transcriptome data to stratify COVID-19 patients based on disease severity and differentiate severe COVID cases from other patients with acute hypoxic respiratory failure. The pattern of gene module enrichment in COVID-19 patients overall reflected broad cellular expansion and metabolic dysfunction, whereas increased neutrophils, activated B cells, T-cell lymphopenia, and proinflammatory cytokine production were specific to severe COVID patients. Using this pipeline, we also identified small blood gene signatures indicative of COVID-19 diagnosis and severity that could be used as biomarker panels in the clinical setting.
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20
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Yaung KN, Yeo JG, Kumar P, Wasser M, Chew M, Ravelli A, Law AHN, Arkachaisri T, Martini A, Pisetsky DS, Albani S. Artificial intelligence and high-dimensional technologies in the theragnosis of systemic lupus erythematosus. THE LANCET. RHEUMATOLOGY 2023; 5:e151-e165. [PMID: 38251610 DOI: 10.1016/s2665-9913(23)00010-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 12/14/2022] [Accepted: 01/04/2023] [Indexed: 02/22/2023]
Abstract
Systemic lupus erythematosus is a complex, systemic autoimmune disease characterised by immune dysregulation. Pathogenesis is multifactorial, contributing to clinical heterogeneity and posing challenges for diagnosis and treatment. Although strides in treatment options have been made in the past 15 years, with the US Food and Drug Administration approval of belimumab in 2011, there are still many patients who have inadequate responses to therapy. A better understanding of underlying disease mechanisms with a holistic and multiparametric approach is required to improve clinical assessment and treatment. This Review discusses the evolution of genomics, epigenomics, transcriptomics, and proteomics in the study of systemic lupus erythematosus and ways to amalgamate these silos of data with a systems-based approach while also discussing ways to strengthen the overall process. These mechanistic insights will facilitate the discovery of functionally relevant biomarkers to guide rational therapeutic selection to improve patient outcomes.
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Affiliation(s)
- Katherine Nay Yaung
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore; Duke-NUS Medical School, Singapore.
| | - Joo Guan Yeo
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore; Duke-NUS Medical School, Singapore; Rheumatology and Immunology Service, KK Women's and Children's Hospital, Singapore
| | - Pavanish Kumar
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore
| | - Martin Wasser
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore
| | - Marvin Chew
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore
| | - Angelo Ravelli
- Direzione Scientifica, IRCCS Istituto Giannina Gaslini, Genoa, Italy; Dipartimento di Neuroscienze, Riabilitazione, Oftalmologia, Genetica e Scienze Materno-Infantili, Università degli Studi di Genova, Genoa, Italy
| | - Annie Hui Nee Law
- Duke-NUS Medical School, Singapore; Department of Rheumatology and Immunology, Singapore General Hospital, Singapore
| | - Thaschawee Arkachaisri
- Duke-NUS Medical School, Singapore; Rheumatology and Immunology Service, KK Women's and Children's Hospital, Singapore
| | | | - David S Pisetsky
- Department of Medicine and Department of Immunology, Duke University Medical Center, Durham, NC, USA; Medical Research Service, Veterans Administration Medical Center, Durham, NC, USA
| | - Salvatore Albani
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore; Duke-NUS Medical School, Singapore; Rheumatology and Immunology Service, KK Women's and Children's Hospital, Singapore
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21
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Application of Machine Learning Models in Systemic Lupus Erythematosus. Int J Mol Sci 2023; 24:ijms24054514. [PMID: 36901945 PMCID: PMC10003088 DOI: 10.3390/ijms24054514] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/14/2023] [Accepted: 02/22/2023] [Indexed: 03/02/2023] Open
Abstract
Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease and is extremely heterogeneous in terms of immunological features and clinical manifestations. This complexity could result in a delay in the diagnosis and treatment introduction, with impacts on long-term outcomes. In this view, the application of innovative tools, such as machine learning models (MLMs), could be useful. Thus, the purpose of the present review is to provide the reader with information about the possible application of artificial intelligence in SLE patients from a medical perspective. To summarize, several studies have applied MLMs in large cohorts in different disease-related fields. In particular, the majority of studies focused on diagnosis and pathogenesis, disease-related manifestations, in particular Lupus Nephritis, outcomes and treatment. Nonetheless, some studies focused on peculiar features, such as pregnancy and quality of life. The review of published data demonstrated the proposal of several models with good performance, suggesting the possible application of MLMs in the SLE scenario.
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22
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Munguía-Realpozo P, Etchegaray-Morales I, Mendoza-Pinto C, Méndez-Martínez S, Osorio-Peña ÁD, Ayón-Aguilar J, García-Carrasco M. Current state and completeness of reporting clinical prediction models using machine learning in systemic lupus erythematosus: A systematic review. Autoimmun Rev 2023; 22:103294. [PMID: 36791873 DOI: 10.1016/j.autrev.2023.103294] [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: 01/24/2023] [Accepted: 02/09/2023] [Indexed: 02/17/2023]
Abstract
OBJECTIVE We carried out a systematic review (SR) of adherence in diagnostic and prognostic applications of ML in SLE using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. METHODS A SR employing five databases was conducted from its inception until December 2021. We identified articles that evaluated the utilization of ML for prognostic and/or diagnostic purposes. This SR was reported based on the PRISMA guidelines. The TRIPOD statement assessed adherence to reporting standards. Assessment for risk of bias was done using PROBAST tool. RESULTS We included 45 studies: 29 (64.4%) diagnostic and 16 (35.5%) prognostic prediction- model studies. Overall, articles adhered by between 17% and 67% (median 43%, IQR 37-49%) to TRIPOD items. Only few articles reported the model's predictive performance (2.3%, 95% CI 0.06-12.0), testing of interaction terms (2.3%, 95% CI 0.06-12.0), flow of participants (50%, 95% CI; 34.6-65.4), blinding of predictors (2.3%, 95% CI 0.06-12.0), handling of missing data (36.4%, 95% CI 22.4-52.2), and appropriate title (20.5%, 95% CI 9.8-35.3). Some items were almost completely reported: the source of data (88.6%, 95% CI 75.4-96.2), eligibility criteria (86.4%, 95% CI 76.2-96.5), and interpretation of findings (88.6%, 95% CI 75.4-96.2). In addition, most of model studies had high risk of bias. CONCLUSIONS The reporting adherence of ML-based model developed for SLE, is currently inadequate. Several items deemed crucial for transparent reporting were not fully reported in studies on ML-based prediction models. REVIEW REGISTRATION PROSPERO ID# CRD42021284881. (Amended to limit the scope).
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Affiliation(s)
- Pamela Munguía-Realpozo
- Systemic Autoimmune Diseases Research Unit, Specialties Hospital UMAE- CIBIOR, Mexican Institute for Social Security, Puebla, Mexico; Department of Rheumatology, Medicine School, Meritorious Autonomous University of Puebla, Mexico
| | - Ivet Etchegaray-Morales
- Department of Rheumatology, Medicine School, Meritorious Autonomous University of Puebla, Mexico.
| | - Claudia Mendoza-Pinto
- Systemic Autoimmune Diseases Research Unit, Specialties Hospital UMAE- CIBIOR, Mexican Institute for Social Security, Puebla, Mexico; Department of Rheumatology, Medicine School, Meritorious Autonomous University of Puebla, Mexico.
| | | | - Ángel David Osorio-Peña
- Department of Rheumatology, Medicine School, Meritorious Autonomous University of Puebla, Mexico
| | - Jorge Ayón-Aguilar
- Coordination of Health Research, Mexican Social Security Institute, Puebla, Mexico.
| | - Mario García-Carrasco
- Department of Rheumatology, Medicine School, Meritorious Autonomous University of Puebla, Mexico
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Li H, Guan Y. Multilevel Modeling of Joint Damage in Rheumatoid Arthritis. ADVANCED INTELLIGENT SYSTEMS (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 4:2200184. [PMID: 37808948 PMCID: PMC10557461 DOI: 10.1002/aisy.202200184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Indexed: 10/10/2023]
Abstract
While most deep learning approaches are developed for single images, in real world applications, images are often obtained as a series to inform decision making. Due to hardware (memory) and software (algorithm) limitations, few methods have been developed to integrate multiple images so far. In this study, we present an approach that seamlessly integrates deep learning and traditional machine learning models, to study multiple images and score joint damages in rheumatoid arthritis. This method allows the quantification of joining space narrowing to approach the clinical upper limit. Beyond predictive performance, we integrate the multilevel interconnections across joints and damage types into the machine learning model and reveal the cross-regulation map of joint damages in rheumatoid arthritis.
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Affiliation(s)
- Hongyang Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
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24
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Huang M, Xu H. Genetic susceptibility to autoimmunity-Current status and challenges. Adv Immunol 2022; 156:25-54. [PMID: 36410874 DOI: 10.1016/bs.ai.2022.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Autoimmune diseases (ADs) often arise from a combination of genetic and environmental triggers that disrupt the immune system's capability to properly tolerate body self-antigens. Familial studies provided the earliest insights into the risk loci of such diseases, while genome-wide association studies (GWAS) significantly broadened the horizons. A drug targeting a prominent pathological pathway can be applied to multiple indications sharing overlapping mechanisms. Advances in genomic technologies used in genetic studies provide critical insights into future research on gene-environment interactions in autoimmunity. This Review summarizes the history and recent advances in the understanding of genetic susceptibility to ADs and related immune disorders, including coronavirus disease 2019 (COVID-19), and their indications for the development of diagnostic or prognostic markers for translational applications.
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Affiliation(s)
| | - Huji Xu
- School of Medicine, Tsinghua University, Beijing, China; Department of Rheumatology and Immunology, Shanghai Changzheng Hospital, The Navel Medical University, Shanghai, China; Peking-Tsinghua Center for Life Sciences, Tsinghua University, Beijing, China.
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25
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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: 0] [Impact Index Per Article: 0] [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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26
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Bouget V, Duquesne J, Hassler S, Cournède PH, Fautrel B, Guillemin F, Pallardy M, Broët P, Mariette X, Bitoun S. Machine learning predicts response to TNF inhibitors in rheumatoid arthritis: results on the ESPOIR and ABIRISK cohorts. RMD Open 2022; 8:rmdopen-2022-002442. [PMID: 35999028 PMCID: PMC9403109 DOI: 10.1136/rmdopen-2022-002442] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 08/05/2022] [Indexed: 11/05/2022] Open
Abstract
Objectives Around 30% of patients with rheumatoid arthritis (RA) do not respond to tumour necrosis factor inhibitors (TNFi). We aimed to predict patient response to TNFi using machine learning on simple clinical and biological data. Methods We used data from the RA ESPOIR cohort to train our models. The endpoints were the EULAR response and the change in Disease Activity Score (DAS28). We compared the performances of multiple models (linear regression, random forest, XGBoost and CatBoost) on the training set and cross-validated them using the area under the receiver operating characteristic curve (AUROC) or the mean squared error. The best model was then evaluated on a replication cohort (ABIRISK). Results We included 161 patients from ESPOIR and 118 patients from ABIRISK. The key selected features were DAS28, lymphocytes, ALT (aspartate aminotransferase), neutrophils, age, weight, and smoking status. When predicting EULAR response, CatBoost achieved the best performances of the four tested models. It reached an AUROC of 0.72 (0.68–0.73) on the train set (ESPOIR). Better results were obtained on the train set when etanercept and monoclonal antibodies were analysed separately. On the test set (ABIRISK), these models respectively achieved on AUROC of 0.70 (0.57–0.82) and 0.71 (0.55–0.86). Two decision thresholds were tested. The first prioritised a high confidence in identifying responders and yielded a confidence up to 90% for predicting response. The second prioritised a high confidence in identifying inadequate responders and yielded a confidence up to 70% for predicting non-response. The change in DAS28 was predicted with an average error of 1.1 DAS28 points. Conclusion The machine learning models developed allowed predicting patient response to TNFi exclusively using data available in clinical routine.
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Affiliation(s)
| | | | - Signe Hassler
- Sorbonne Université, INSERM UMR 959, Immunology-Immunopathology-Immunotherapy (i3), Assistance Publique Hôpitaux de Paris, Hôpital Pitié Salpêtrière, Paris, France.,CESP, INSERM UMR 1018, Paris-Saclay University, France, Villejuif, France
| | - Paul-Henry Cournède
- CentraleSupélec Laboratory of Mathematics and Informatics for Systems Complexity, Gif-sur-Yvette, France
| | - Bruno Fautrel
- Rheumatology Departement, Assistance Publique Hôpitaux de Paris, Groupe Hospitalier Pitié Salpêtrière, Paris, France.,Institut Pierre Louis d'épidémiologie et santé publique, Inserm UMRS 1136, équipe PEPITES (Pharmaco-épidémiologie et Évaluation des Soins), Paris, France
| | | | - Marc Pallardy
- INSERM UMR 996, Faculty of Pharmacy, Paris-Saclay University, Châtenay-Malabry, France.,ABIRISK (Anti-Biopharmaceutical Immunization: prediction and analysis of clinical relevance to minimize the RISK consortium), Innovative Medicines Initiative, Brussels, Belgium
| | - Philippe Broët
- Sorbonne Université, INSERM UMR 959, Immunology-Immunopathology-Immunotherapy (i3), Assistance Publique Hôpitaux de Paris, Hôpital Pitié Salpêtrière, Paris, France.,CESP, INSERM UMR 1018, Paris-Saclay University, France, Villejuif, France
| | - Xavier Mariette
- Rheumatology departement, Université Paris Saclay, Assistance Publique-Hôpitaux de Paris, Hôpital Bicêtre, INSERM UMR 1184, FHU CARE, Le Kremlin Bicêtre, France
| | - Samuel Bitoun
- Rheumatology departement, Université Paris Saclay, Assistance Publique-Hôpitaux de Paris, Hôpital Bicêtre, INSERM UMR 1184, FHU CARE, Le Kremlin Bicêtre, France
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27
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Personalized Medicine and Machine Learning: A Roadmap for the Future. J Clin Med 2022; 11:jcm11144110. [PMID: 35887873 PMCID: PMC9317385 DOI: 10.3390/jcm11144110] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 07/14/2022] [Indexed: 12/10/2022] Open
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28
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De Cock D, Myasoedova E, Aletaha D, Studenic P. Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs). Ther Adv Musculoskelet Dis 2022; 14:1759720X221105978. [PMID: 35794905 PMCID: PMC9251966 DOI: 10.1177/1759720x221105978] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 05/22/2022] [Indexed: 11/17/2022] Open
Abstract
Health care processes are under constant development and will need to embrace advances in technology and health science aiming to provide optimal care. Considering the perspective of increasing treatment options for people with rheumatic and musculoskeletal diseases, but in many cases not reaching all treatment targets that matter to patients, care systems bare potential to improve on a holistic level. This review provides an overview of systems and technologies under evaluation over the past years that show potential to impact diagnosis and treatment of rheumatic diseases in about 10 years from now. We summarize initiatives and studies from the field of electronic health records, biobanking, remote monitoring, and artificial intelligence. The combination and implementation of these opportunities in daily clinical care will be key for a new era in care of our patients. This aims to inform rheumatologists and healthcare providers concerned with chronic inflammatory musculoskeletal conditions about current important and promising developments in science that might substantially impact the management processes of rheumatic diseases in the 2030s.
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Affiliation(s)
- Diederik De Cock
- Clinical and Experimental Endocrinology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium
| | - Elena Myasoedova
- Division of Rheumatology, Department of Internal Medicine and Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Daniel Aletaha
- Division of Rheumatology, Department of Internal Medicine 3, Medical University Vienna, Vienna, Austria
| | - Paul Studenic
- Division of Rheumatology, Department of Internal Medicine 3, Medical University Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
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29
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Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test. Sci Rep 2022; 12:7224. [PMID: 35508670 PMCID: PMC9068780 DOI: 10.1038/s41598-022-11361-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/22/2022] [Indexed: 11/08/2022] Open
Abstract
Recent effective therapies enable most rheumatoid arthritis (RA) patients to achieve remission; however, some patients experience relapse. We aimed to predict relapse in RA patients through machine learning (ML) using data on ultrasound (US) examination and blood test. Overall, 210 patients with RA in remission at baseline were dichotomized into remission (n = 150) and relapse (n = 60) based on the disease activity at 2-year follow-up. Three ML classifiers [Logistic Regression, Random Forest, and extreme gradient boosting (XGBoost)] and data on 73 features (14 US examination data, 54 blood test data, and five data on patient information) at baseline were used for predicting relapse. The best performance was obtained using the XGBoost classifier (area under the receiver operator characteristic curve (AUC) = 0.747), compared with Random Forest and Logistic Regression (AUC = 0.719 and 0.701, respectively). In the XGBoost classifier prediction, ten important features, including wrist/metatarsophalangeal superb microvascular imaging scores, were selected using the recursive feature elimination method. The performance was superior to that predicted by researcher-selected features, which are conventional prognostic markers. These results suggest that ML can provide an accurate prediction of relapse in RA patients, and the use of predictive algorithms may facilitate personalized treatment options.
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30
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Konnaris MA, Brendel M, Fontana MA, Otero M, Ivashkiv LB, Wang F, Bell RD. Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges. Arthritis Res Ther 2022; 24:68. [PMID: 35277196 PMCID: PMC8915507 DOI: 10.1186/s13075-021-02716-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 12/29/2021] [Indexed: 11/21/2022] Open
Abstract
Histopathology is widely used to analyze clinical biopsy specimens and tissues from pre-clinical models of a variety of musculoskeletal conditions. Histological assessment relies on scoring systems that require expertise, time, and resources, which can lead to an analysis bottleneck. Recent advancements in digital imaging and image processing provide an opportunity to automate histological analyses by implementing advanced statistical models such as machine learning and deep learning, which would greatly benefit the musculoskeletal field. This review provides a high-level overview of machine learning applications, a general pipeline of tissue collection to model selection, and highlights the development of image analysis methods, including some machine learning applications, to solve musculoskeletal problems. We discuss the optimization steps for tissue processing, sectioning, staining, and imaging that are critical for the successful generalizability of an automated image analysis model. We also commenting on the considerations that should be taken into account during model selection and the considerable advances in the field of computer vision outside of histopathology, which can be leveraged for image analysis. Finally, we provide a historic perspective of the previously used histopathological image analysis applications for musculoskeletal diseases, and we contrast it with the advantages of implementing state-of-the-art computational pathology approaches. While some deep learning approaches have been used, there is a significant opportunity to expand the use of such approaches to solve musculoskeletal problems.
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Affiliation(s)
- Maxwell A Konnaris
- Research Institute, Hospital for Special Surgery, New York, USA.,Orthopedic Soft Tissue Research Program, Hospital for Special Surgery, New York, USA
| | - Matthew Brendel
- Department of Population Health Sciences, Weill Cornell Medical College, New York, USA.,Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Mark Alan Fontana
- Department of Population Health Sciences, Weill Cornell Medical College, New York, USA.,Center for Analytics, Modeling, & Performance, Hospital for Special Surgery, New York, USA
| | - Miguel Otero
- Research Institute, Hospital for Special Surgery, New York, USA.,Orthopedic Soft Tissue Research Program, Hospital for Special Surgery, New York, USA
| | - Lionel B Ivashkiv
- Research Institute, Hospital for Special Surgery, New York, USA.,Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, USA.,Rosenweig Genomics Center, Hospital for Special Surgery, New York, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medical College, New York, USA
| | - Richard D Bell
- Research Institute, Hospital for Special Surgery, New York, USA. .,Center for Analytics, Modeling, & Performance, Hospital for Special Surgery, New York, USA. .,Rosenweig Genomics Center, Hospital for Special Surgery, New York, USA.
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31
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The Past, Present, and Future in Antinuclear Antibodies (ANA). Diagnostics (Basel) 2022; 12:diagnostics12030647. [PMID: 35328200 PMCID: PMC8946865 DOI: 10.3390/diagnostics12030647] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 02/28/2022] [Accepted: 03/05/2022] [Indexed: 12/21/2022] Open
Abstract
Autoantibodies are a hallmark of autoimmunity and, specifically, antinuclear antibodies (ANAs) are the most relevant autoantibodies present in systemic autoimmune rheumatic diseases (SARDs). Over the years, different methods from LE cell to HEp-2 indirect immunofluorescence (IIF), solid-phase assays (SPAs), and finally multianalyte technologies have been developed to study ANA-associated SARDs. All of them provide complementary information that is important to provide the most clinically valuable information. The identification of new biomarkers together with multianalyte platforms will help close the so-called “seronegative gap” and to correctly classify and diagnose patients with SARDs. Finally, artificial intelligence and machine learning is an area still to be exploited but in a next future will help to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management.
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Bertsias G. Dialogue: High-throughput studies in rheumatology: time for unsupervised clustering? Lupus Sci Med 2021; 8:8/1/e000643. [PMID: 34952891 PMCID: PMC8710894 DOI: 10.1136/lupus-2021-000643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 12/15/2021] [Indexed: 11/24/2022]
Affiliation(s)
- George Bertsias
- Rheumatology, Clinical Immunology and Allergy, University of Crete School of Medicine, Heraklion, Crete, Greece .,Laboratory of Rheumatology, Autoimmunity and Inflammation, Institute of Molecular Biology and Biotechnology (IMBB-FORTH), Heraklion, Greece
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