{"title":"人工智能和机器学习在轴性脊柱关节炎中的应用。","authors":"Lisa C Adams, Keno K Bressem, Denis Poddubnyy","doi":"10.1097/BOR.0000000000001015","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose of review: </strong>To evaluate the current applications and prospects of artificial intelligence and machine learning in diagnosing and managing axial spondyloarthritis (axSpA), focusing on their role in medical imaging, predictive modelling, and patient monitoring.</p><p><strong>Recent findings: </strong>Artificial intelligence, particularly deep learning, is showing promise in diagnosing axSpA assisting with X-ray, computed tomography (CT) and MRI analyses, with some models matching or outperforming radiologists in detecting sacroiliitis and markers. Moreover, it is increasingly being used in predictive modelling of disease progression and personalized treatment, and could aid risk assessment, treatment response and clinical subtype identification. Variable study designs, sample sizes and the predominance of retrospective, single-centre studies still limit the generalizability of results.</p><p><strong>Summary: </strong>Artificial intelligence technologies have significant potential to advance the diagnosis and treatment of axSpA, providing more accurate, efficient and personalized healthcare solutions. However, their integration into clinical practice requires rigorous validation, ethical and legal considerations, and comprehensive training for healthcare professionals. Future advances in artificial intelligence could complement clinical expertise and improve patient care through improved diagnostic accuracy and tailored therapeutic strategies, but the challenge remains to ensure that these technologies are validated in prospective multicentre trials and ethically integrated into patient care.</p>","PeriodicalId":11145,"journal":{"name":"Current opinion in rheumatology","volume":null,"pages":null},"PeriodicalIF":5.2000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence and machine learning in axial spondyloarthritis.\",\"authors\":\"Lisa C Adams, Keno K Bressem, Denis Poddubnyy\",\"doi\":\"10.1097/BOR.0000000000001015\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose of review: </strong>To evaluate the current applications and prospects of artificial intelligence and machine learning in diagnosing and managing axial spondyloarthritis (axSpA), focusing on their role in medical imaging, predictive modelling, and patient monitoring.</p><p><strong>Recent findings: </strong>Artificial intelligence, particularly deep learning, is showing promise in diagnosing axSpA assisting with X-ray, computed tomography (CT) and MRI analyses, with some models matching or outperforming radiologists in detecting sacroiliitis and markers. Moreover, it is increasingly being used in predictive modelling of disease progression and personalized treatment, and could aid risk assessment, treatment response and clinical subtype identification. Variable study designs, sample sizes and the predominance of retrospective, single-centre studies still limit the generalizability of results.</p><p><strong>Summary: </strong>Artificial intelligence technologies have significant potential to advance the diagnosis and treatment of axSpA, providing more accurate, efficient and personalized healthcare solutions. However, their integration into clinical practice requires rigorous validation, ethical and legal considerations, and comprehensive training for healthcare professionals. Future advances in artificial intelligence could complement clinical expertise and improve patient care through improved diagnostic accuracy and tailored therapeutic strategies, but the challenge remains to ensure that these technologies are validated in prospective multicentre trials and ethically integrated into patient care.</p>\",\"PeriodicalId\":11145,\"journal\":{\"name\":\"Current opinion in rheumatology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current opinion in rheumatology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/BOR.0000000000001015\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/3/27 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"RHEUMATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current opinion in rheumatology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/BOR.0000000000001015","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/3/27 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"RHEUMATOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
综述的目的:评估人工智能和机器学习在诊断和管理轴性脊柱关节炎(axSpA)方面的当前应用和前景,重点关注它们在医学成像、预测建模和患者监测方面的作用:人工智能,尤其是深度学习,在辅助 X 光、计算机断层扫描(CT)和核磁共振成像分析诊断 axSpA 方面大有可为,一些模型在检测骶髂关节炎和标记物方面与放射科医生不相上下,甚至更胜一筹。此外,它还越来越多地用于疾病进展和个性化治疗的预测建模,并有助于风险评估、治疗反应和临床亚型识别。小结:人工智能技术在推动轴索硬化症的诊断和治疗方面具有巨大潜力,可提供更准确、高效和个性化的医疗解决方案。然而,将其融入临床实践需要严格的验证、伦理和法律方面的考虑,以及对医疗保健专业人员的全面培训。人工智能的未来发展可以补充临床专业知识,并通过提高诊断准确性和量身定制的治疗策略来改善患者护理,但如何确保这些技术在前瞻性多中心试验中得到验证,并符合伦理要求地融入患者护理中,仍然是一项挑战。
Artificial intelligence and machine learning in axial spondyloarthritis.
Purpose of review: To evaluate the current applications and prospects of artificial intelligence and machine learning in diagnosing and managing axial spondyloarthritis (axSpA), focusing on their role in medical imaging, predictive modelling, and patient monitoring.
Recent findings: Artificial intelligence, particularly deep learning, is showing promise in diagnosing axSpA assisting with X-ray, computed tomography (CT) and MRI analyses, with some models matching or outperforming radiologists in detecting sacroiliitis and markers. Moreover, it is increasingly being used in predictive modelling of disease progression and personalized treatment, and could aid risk assessment, treatment response and clinical subtype identification. Variable study designs, sample sizes and the predominance of retrospective, single-centre studies still limit the generalizability of results.
Summary: Artificial intelligence technologies have significant potential to advance the diagnosis and treatment of axSpA, providing more accurate, efficient and personalized healthcare solutions. However, their integration into clinical practice requires rigorous validation, ethical and legal considerations, and comprehensive training for healthcare professionals. Future advances in artificial intelligence could complement clinical expertise and improve patient care through improved diagnostic accuracy and tailored therapeutic strategies, but the challenge remains to ensure that these technologies are validated in prospective multicentre trials and ethically integrated into patient care.
期刊介绍:
A high impact review journal which boasts an international readership, Current Opinion in Rheumatology offers a broad-based perspective on the most recent and exciting developments within the field of rheumatology. Published bimonthly, each issue features insightful editorials and high quality invited reviews covering two or three key disciplines which include vasculitis syndromes, medical physiology and rheumatic diseases, crystal deposition diseases and rheumatoid arthritis. Each discipline introduces world renowned guest editors to ensure the journal is at the forefront of knowledge development and delivers balanced, expert assessments of advances from the previous year.