Department of Diagnostic and Interventional Radiology, Faculty of Medicine.
Institute for Radiology and Nuclear Medicine, German Heart Centre Munich, Technical University of Munich, Munich.
Curr Opin Rheumatol. 2024 Jul 1;36(4):267-273. doi: 10.1097/BOR.0000000000001015. Epub 2024 Mar 27.
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.
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.
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.
评估人工智能和机器学习在诊断和管理中轴型脊柱关节炎（axSpA）中的当前应用和前景，重点关注它们在医学影像学、预测建模和患者监测中的作用。
人工智能，特别是深度学习，在 axSpA 的诊断中显示出了很大的潜力，可辅助 X 射线、计算机断层扫描（CT）和磁共振成像（MRI）分析，一些模型在检测骶髂关节炎和标志物方面与放射科医生的表现相当，甚至更好。此外，它越来越多地用于疾病进展和个性化治疗的预测建模，并且可以帮助进行风险评估、治疗反应和临床亚型识别。不同的研究设计、样本量以及以回顾性、单中心研究为主，仍然限制了结果的推广性。
人工智能技术在 axSpA 的诊断和治疗方面具有很大的潜力，可以提供更准确、高效和个性化的医疗保健解决方案。然而，将其整合到临床实践中需要严格的验证、伦理和法律考虑，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通过提高诊断准确性和制定量身定制的治疗策略来补充临床专业知识并改善患者护理，但仍需确保这些技术在前瞻性多中心试验中得到验证，并在伦理上融入患者护理。
