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Cipolletta E, Fiorentino MC, Vreju FA, Moccia S, Filippucci E. Editorial: Artificial intelligence in rheumatology and musculoskeletal diseases. Front Med (Lausanne) 2024; 11:1402871. [PMID: 38646556 PMCID: PMC11026684 DOI: 10.3389/fmed.2024.1402871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 03/25/2024] [Indexed: 04/23/2024] Open
Affiliation(s)
- Edoardo Cipolletta
- Rheumatology Unit, Department of Clinical and Molecular Sciences, Polytechnic University of Marche, Ancona, Italy
- Academic Rheumatology, University of Nottingham, Nottingham, United Kingdom
| | | | - Florentin Ananu Vreju
- Department of Rheumatology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Sara Moccia
- Department of Excellence in Robotics and AI, The Biorobotics Institute, Scuola Superiore Sant'anna, Pisa, Italy
| | - Emilio Filippucci
- Rheumatology Unit, Department of Clinical and Molecular Sciences, Polytechnic University of Marche, Ancona, Italy
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Zeng J, Gao X, Gao L, Yu Y, Shen L, Pan X. Recognition of rare antinuclear antibody patterns based on a novel attention-based enhancement framework. Brief Bioinform 2024; 25:bbad531. [PMID: 38279651 PMCID: PMC10818137 DOI: 10.1093/bib/bbad531] [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: 09/28/2023] [Revised: 12/17/2023] [Accepted: 12/19/2023] [Indexed: 01/28/2024] Open
Abstract
Rare antinuclear antibody (ANA) pattern recognition has been a widely applied technology for routine ANA screening in clinical laboratories. In recent years, the application of deep learning methods in recognizing ANA patterns has witnessed remarkable advancements. However, the majority of studies in this field have primarily focused on the classification of the most common ANA patterns, while another subset has concentrated on the detection of mitotic metaphase cells. To date, no prior research has been specifically dedicated to the identification of rare ANA patterns. In the present paper, we introduce a novel attention-based enhancement framework, which was designed for the recognition of rare ANA patterns in ANA-indirect immunofluorescence images. More specifically, we selected the algorithm with the best performance as our target detection network by conducting comparative experiments. We then further developed and enhanced the chosen algorithm through a series of optimizations. Then, attention mechanism was introduced to facilitate neural networks in expediting the learning process, extracting more essential and distinctive features for the target features that belong to the specific patterns. The proposed approach has helped to obtained high precision rate of 86.40%, 82.75% recall, 84.24% F1 score and 84.64% mean average precision for a 9-category rare ANA pattern detection task on our dataset. Finally, we evaluated the potential of the model as medical technologist assistant and observed that the technologist's performance improved after referring to the results of the model prediction. These promising results highlighted its potential as an efficient and reliable tool to assist medical technologists in their clinical practice.
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Affiliation(s)
- Junxiang Zeng
- Department of Clinical Laboratory, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Faculty of Medical Laboratory Science, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Artificial Intelligence Medicine, Shanghai Academy of Experimental Medicine, Shanghai, China
| | - Xiupan Gao
- Department of Clinical Laboratory, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Limei Gao
- Department of Immunology and Rheumatology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Youyou Yu
- Department of Clinical Laboratory, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lisong Shen
- Department of Clinical Laboratory, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Faculty of Medical Laboratory Science, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Artificial Intelligence Medicine, Shanghai Academy of Experimental Medicine, Shanghai, China
| | - Xiujun Pan
- Department of Clinical Laboratory, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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Chung CW, Chou SC, Hsiao TH, Zhang GJ, Chung YF, Chen YM. Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records. BioData Min 2024; 17:1. [PMID: 38183082 PMCID: PMC10770905 DOI: 10.1186/s13040-023-00352-y] [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: 08/14/2023] [Accepted: 12/19/2023] [Indexed: 01/07/2024] Open
Abstract
BACKGROUND Although the 2019 EULAR/ACR classification criteria for systemic lupus erythematosus (SLE) has required at least a positive anti-nuclear antibody (ANA) titer (≥ 1:80), it remains challenging for clinicians to identify patients with SLE. This study aimed to develop a machine learning (ML) approach to assist in the detection of SLE patients using genomic data and electronic health records. METHODS Participants with a positive ANA (≥ 1:80) were enrolled from the Taiwan Precision Medicine Initiative cohort. The Taiwan Biobank version 2 array was used to detect single nucleotide polymorphism (SNP) data. Six ML models, Logistic Regression, Random Forest (RF), Support Vector Machine, Light Gradient Boosting Machine, Gradient Tree Boosting, and Extreme Gradient Boosting (XGB), were used to identify SLE patients. The importance of the clinical and genetic features was determined by Shapley Additive Explanation (SHAP) values. A logistic regression model was applied to identify genetic variations associated with SLE in the subset of patients with an ANA equal to or exceeding 1:640. RESULTS A total of 946 SLE and 1,892 non-SLE controls were included in this analysis. Among the six ML models, RF and XGB demonstrated superior performance in the differentiation of SLE from non-SLE. The leading features in the SHAP diagram were anti-double strand DNA antibodies, ANA titers, AC4 ANA pattern, polygenic risk scores, complement levels, and SNPs. Additionally, in the subgroup with a high ANA titer (≥ 1:640), six SNPs positively associated with SLE and five SNPs negatively correlated with SLE were discovered. CONCLUSIONS ML approaches offer the potential to assist in diagnosing SLE and uncovering novel SNPs in a group of patients with autoimmunity.
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Affiliation(s)
- Chih-Wei Chung
- Department of Information Management, National Taiwan University, Taipei, Taiwan
| | - Seng-Cho Chou
- Department of Information Management, National Taiwan University, Taipei, Taiwan
| | - Tzu-Hung Hsiao
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Public Health, Fu Jen Catholic University, New Taipei City, Taiwan
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan
| | - Grace Joyce Zhang
- Department of Cellular and Physiological Sciences, The University of British Columbia, Vancouver, BC, Canada
| | - Yu-Fang Chung
- Department of Electrical Engineering, Tunghai University, Taichung, Taiwan
| | - Yi-Ming Chen
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.
- Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Taichung Veterans General Hospital, 1650, Section 4, Taiwan Boulevard, Xitun Dist., Taichung City, 407, Taiwan.
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Rong Hsing Research Center for Translational Medicine & Ph.D. Program in Translational Medicine, National Chung Hsing University, Taichung, Taiwan.
- Precision Medicine Research Center, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
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Di Maggio G, Confalonieri P, Salton F, Trotta L, Ruggero L, Kodric M, Geri P, Hughes M, Bellan M, Gilio M, Lerda S, Baratella E, Confalonieri M, Mondini L, Ruaro B. Biomarkers in Systemic Sclerosis: An Overview. Curr Issues Mol Biol 2023; 45:7775-7802. [PMID: 37886934 PMCID: PMC10604992 DOI: 10.3390/cimb45100490] [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: 09/02/2023] [Revised: 09/19/2023] [Accepted: 09/22/2023] [Indexed: 10/28/2023] Open
Abstract
Systemic sclerosis (SSc) is a complex autoimmune disease characterized by significant fibrosis of the skin and internal organs, with the main involvement of the lungs, kidneys, heart, esophagus, and intestines. SSc is also characterized by macro- and microvascular damage with reduced peripheral blood perfusion. Several studies have reported more than 240 pathways and numerous dysregulation proteins, giving insight into how the field of biomarkers in SSc is still extremely complex and evolving. Antinuclear antibodies (ANA) are present in more than 90% of SSc patients, and anti-centromere and anti-topoisomerase I antibodies are considered classic biomarkers with precise clinical features. Recent studies have reported that trans-forming growth factor β (TGF-β) plays a central role in the fibrotic process. In addition, interferon regulatory factor 5 (IRF5), interleukin receptor-associated kinase-1 (IRAK-1), connective tissue growth factor (CTGF), transducer and activator of transcription signal 4 (STAT4), pyrin-containing domain 1 (NLRP1), as well as genetic factors, including DRB1 alleles, are implicated in SSc damage. Several interleukins (e.g., IL-1, IL-6, IL-10, IL-17, IL-22, and IL-35) and chemokines (e.g., CCL 2, 5, 23, and CXC 9, 10, 16) are elevated in SSc. While adiponectin and maresin 1 are reduced in patients with SSc, biomarkers are important in research but will be increasingly so in the diagnosis and therapeutic approach to SSc. This review aims to present and highlight the various biomarker molecules, pathways, and receptors involved in the pathology of SSc.
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Affiliation(s)
- Giuseppe Di Maggio
- Pulmonology Unit, Department of Medical Surgical and Healt Sciencies, Hospital of Cattinara, University of Trieste, 34149 Trieste, Italy; (G.D.M.); (M.K.); (P.G.); (L.M.)
| | - Paola Confalonieri
- Pulmonology Unit, Department of Medical Surgical and Healt Sciencies, Hospital of Cattinara, University of Trieste, 34149 Trieste, Italy; (G.D.M.); (M.K.); (P.G.); (L.M.)
| | - Francesco Salton
- Pulmonology Unit, Department of Medical Surgical and Healt Sciencies, Hospital of Cattinara, University of Trieste, 34149 Trieste, Italy; (G.D.M.); (M.K.); (P.G.); (L.M.)
| | - Liliana Trotta
- Pulmonology Unit, Department of Medical Surgical and Healt Sciencies, Hospital of Cattinara, University of Trieste, 34149 Trieste, Italy; (G.D.M.); (M.K.); (P.G.); (L.M.)
| | - Luca Ruggero
- Pulmonology Unit, Department of Medical Surgical and Healt Sciencies, Hospital of Cattinara, University of Trieste, 34149 Trieste, Italy; (G.D.M.); (M.K.); (P.G.); (L.M.)
| | - Metka Kodric
- Pulmonology Unit, Department of Medical Surgical and Healt Sciencies, Hospital of Cattinara, University of Trieste, 34149 Trieste, Italy; (G.D.M.); (M.K.); (P.G.); (L.M.)
| | - Pietro Geri
- Pulmonology Unit, Department of Medical Surgical and Healt Sciencies, Hospital of Cattinara, University of Trieste, 34149 Trieste, Italy; (G.D.M.); (M.K.); (P.G.); (L.M.)
| | - Michael Hughes
- Division of Musculoskeletal and Dermatological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester & Salford Royal NHS Foundation Trust, Manchester M6 8HD, UK;
| | - Mattia Bellan
- Department of Translational Medicine, Università del Piemonte Orientale (UPO), 28100 Novara, Italy
- Center for Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale (UPO), 28100 Novara, Italy
- Department of Medicine, Azienda Ospedaliero–Universitaria, Maggiore della Carità, 28100 Novara, Italy
| | - Michele Gilio
- Infectious Disease Unit, San Carlo Hospital, 85100 Potenza, Italy
| | - Selene Lerda
- Graduate School, University of Milan, 20149 Milano, Italy
| | - Elisa Baratella
- Department of Radiology, Cattinara Hospital, University of Trieste, 34149 Trieste, Italy
| | - Marco Confalonieri
- Pulmonology Unit, Department of Medical Surgical and Healt Sciencies, Hospital of Cattinara, University of Trieste, 34149 Trieste, Italy; (G.D.M.); (M.K.); (P.G.); (L.M.)
| | - Lucrezia Mondini
- Pulmonology Unit, Department of Medical Surgical and Healt Sciencies, Hospital of Cattinara, University of Trieste, 34149 Trieste, Italy; (G.D.M.); (M.K.); (P.G.); (L.M.)
| | - Barbara Ruaro
- Pulmonology Unit, Department of Medical Surgical and Healt Sciencies, Hospital of Cattinara, University of Trieste, 34149 Trieste, Italy; (G.D.M.); (M.K.); (P.G.); (L.M.)
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Boral B, Togay A. Automatic Classification of Antinuclear Antibody Patterns With Machine Learning. Cureus 2023; 15:e45008. [PMID: 37829973 PMCID: PMC10565522 DOI: 10.7759/cureus.45008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2023] [Indexed: 10/14/2023] Open
Abstract
Antinuclear antibodies (ANA) are important diagnostic markers in many autoimmune rheumatological diseases. The indirect immunofluorescence assay applied on human epithelial cells generates images that are used in the detection of ANA. The classification of these images for different ANA patterns requires human experts. It is time-consuming and subjective as different experts may label the same image differently. Therefore, there is an interest in machine learning-based automatic classification of ANA patterns. In our study, to build an application for the automatic classification of ANA patterns, we construct a dataset and learn a deep neural network with a transfer learning approach. We show that even in the existence of a limited number of labeled data, high accuracies can be achieved on the unseen test samples. Our study shows that deep learning-based software can be built for this task to save expert time.
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Affiliation(s)
- Baris Boral
- Immunology, University of Health Sciences, Dr. Abdurrahman Yurtarslan Oncology Training and Research Hospital, Ankara, TUR
| | - Alper Togay
- Medical Microbiology and Immunology, Health Science University İzmir Tepecik Training and Research Hospital, İzmir, TUR
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Automatic Segmentation and Classification for Antinuclear Antibody Images Based on Deep Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:1353965. [PMID: 36818578 PMCID: PMC9931452 DOI: 10.1155/2023/1353965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 11/01/2022] [Accepted: 01/20/2023] [Indexed: 02/10/2023]
Abstract
Antinuclear antibodies (ANAs) testing is the main serological diagnosis screening test for autoimmune diseases. ANAs testing is conducted principally by the indirect immunofluorescence (IIF) on human epithelial cell-substrate (HEp-2) protocol. However, due to its high variability and human subjectivity, there is an insistent need to develop an efficient method for automatic image segmentation and classification. This article develops an automatic segmentation and classification framework based on artificial intelligence (AI) on the ANA images. The Otsu thresholding method and watershed segmentation algorithm are adopted to segment IIF images of cells. Moreover, multiple texture features such as scale-invariant feature transform (SIFT), local binary pattern (LBP), cooccurrence among adjacent LBPs (CoALBP), and rotation invariant cooccurrence among adjacent LBPs (RIC-LBP) are utilized. Firstly, this article adopts traditional machine learning methods such as support vector machine (SVM), k-nearest neighbor algorithm (KNN), and random forest (RF) and then uses ensemble classifier (ECLF) combined with soft voting rules to merge these machine learning methods for classification. The deep learning method InceptionResNetV2 is also utilized to train on the classification of cell images. Eventually, the best accuracy of 0.9269 on the Changsha dataset and 0.9635 on the ICPR 2016 dataset for the traditional methods is obtained by a combination of SIFT and RIC-LBP with the ECLF classifier, and the best accuracy obtained by the InceptionResNetV2 is 0.9465 and 0.9836 separately, which outperforms other schemes.
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