Effectiveness of artificial intelligence in classification of connective tissue diseases in patients with anti-nuclear antibody (ANA) positivity

IF 3.1 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry Pub Date : 2025-09-09 DOI:10.1016/j.compbiolchem.2025.108679

Burcu Bosnalı , Erdinç Türk , Tahir Saygın Öğüt , Mert Ünal , Taner Danışman , Hatice Yazısız , Funda Erbasan , Mustafa Ender Terzioğlu , Veli Yazisiz

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引用次数: 0

Abstract

Objectives

The study aimed to investigate the classification performance of artificial intelligence (AI) in diagnosing connective tissue diseases(CTD). This was done by analyzing laboratory data, including additional markers, in patients who tested positive for antinuclear antibody(ANA).

Material/Methods

The research included 663 ANA-positive patients. An automated machine learning approach, specifically Auto-Weka, was used to classify these patients based on 75 features, including age, sex, and various laboratory tests.

Results

The Bayes Network achieved the highest overall performance with 93.1 % accuracy, 77.7 % sensitivity, and 96.0 % specificity in the classification of all patients. The most successful models were Locally Weighted Learning for systemic lupus erythematosus(SLE), with an accuracy of 93.4 %; Logistic Model Trees for primary Sjogren's syndrome(pSS), with an accuracy of 91.4 %; AdaBoostM for rheumatoid arthritis(RA), with an accuracy of 95.2 %; and Sequential Minimal Optimization for systemic sclerosis(SSc), with an accuracy of 92.0 %. Sensitivity and specificity rates for SLE, pSS, RA and SSc were found to be 69.4 %, 72.0 %, 78.5 %, 75.3 % and 98.7 %, 96.2 %, 98.9 %, 94.9 %, respectively. The area under the ROC curve in the general distribution of the groups was 95.6 %, the highest value in distinguishing was 99.1 % for RA and the lowest was 85.1 % for SSc. The most predictive markers identified were hematocrit for SLE, anti-SSA for pSS, rheumatoid factor for RA, and anti-centromere B positivity for SSc.

Conclusion

AI models are highly successful in classifying ANA-positive patients with great accuracy. AI-based approaches have the potential to assist clinicians in diagnosing autoimmune diseases by providing more accurate and faster results.

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人工智能在抗核抗体（ANA）阳性结缔组织疾病分类中的应用

目的探讨人工智能（AI）在结缔组织病（CTD）诊断中的分类性能。这是通过分析抗核抗体（ANA）检测呈阳性的患者的实验室数据，包括其他标记物来完成的。材料/方法纳入663例ana阳性患者。使用自动机器学习方法，特别是Auto-Weka，根据75个特征（包括年龄、性别和各种实验室测试）对这些患者进行分类。结果贝叶斯网络对所有患者的分类准确率为93.1 %，灵敏度为77.7 %，特异性为96.0 %，总体表现最高。最成功的模型是系统性红斑狼疮（SLE）的局部加权学习，准确率为93.4 %；原发性干燥综合征（pSS）的Logistic模型树，准确率为91.4 %；AdaBoostM用于类风湿性关节炎（RA），准确率为95.2% %；系统性硬化症（SSc）的顺序最小优化，准确率为92.0 %。SLE、pSS、RA和SSc的敏感性和特异性分别为69.4% %、72.0 %、78.5 %、75.3 %和98.7 %、96.2 %、98.9 %、94.9 %。各组总体分布的ROC曲线下面积为95.6% %，RA的区分值最高为99.1 %，SSc的区分值最低为85.1 %。最具预测性的标志物是SLE的红细胞压积、pSS的抗ssa、RA的类风湿因子和SSc的抗着丝粒B阳性。结论人工智能模型对ana阳性患者的分类具有较高的准确性。基于人工智能的方法有可能通过提供更准确和更快的结果来帮助临床医生诊断自身免疫性疾病。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Biology and Chemistry 生物-计算机：跨学科应用

CiteScore

6.10

自引率

3.20%

发文量

142

审稿时长

24 days

期刊介绍： Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.