牡丹江医科大学研究生院;黑龙江省齐齐哈尔市第一医院CT放射科;
痛风是一种由单尿酸钠（MSU）晶体沉积于体内引起的急、慢性疾病，作为一种炎症性、代谢性疾病在全球发病率逐年升高，且有年轻化的趋势，如不及时干预，一旦延误治疗，就会导致严重的代谢紊乱，MSU的沉积，甚至会造成骨组织和软骨组织的侵蚀，带来不可逆转的损害。近年来，人工智能（AI）作为一种新的智能诊断手段，对痛风快速检测、精准诊断及预后评估具有重要意义。本文对AI在痛风中的最新研究成果进行综述，旨在探讨AI方法性能、优点和局限性，以及在痛风检出、精准诊断、预后评估中的应用研究现状，并与传统的医学诊断方法进行比较，展望其未来应用前景。
| 57 | 0 | 10 |
| 下载次数 | 被引频次 | 阅读次数 |
[1] DALBETH N, GOSLING A L, GAFFO A, et al. Gout[J]. Lancet, 2021,397(10287):1843-1855.
[2] DEHLIN M, JACOBSSON L, RODDY E. Global epidemiology of gout:prevalence, incidence, treatment patterns and risk factors[J]. Nat Rev Rheumatol, 2020,16(7):380-390.
[3]郑婉仪，江桂华，詹文峰，等.影像学检查在痛风性关节炎临床中的价值[J].医学影像学杂志，2024,34(5):151-154.
[4]马俪文，刘健，党万太.不同影像学检查方式在痛风性关节炎中应用的研究进展[J].重庆医学，2023,52(17):2662-2666.
[5] ZOU Z, YANG M, WANG Y, et al. Gout of ankle and foot:DECT versus US for crystal detection[J]. Clin Rheumatol,2021,40(4):1533-1537.
[6]李越，邹月芬，徐磊，等.痛风性关节炎急性发作期DECT影像特征与临床表现的相关性研究[J].临床放射学杂志，2023,42(4):651-655.
[7]陆少范，林波淼，黄裕存，等.膝关节痛风性关节炎MRI征象及其与血尿酸水平的相关性[J].实用放射学杂志，2022,37(6):968-971.
[8] RANI V, NABI S T, KUMAR M, et al. Self-supervised learning:a succinct review[J]. Arch Comput Methods Eng, 2023;30(4):2761-2775.
[9] RODRIGUEZ V J, PAN Y, SALAZAR A S, et al.Using unsupervised machine learning to classify behavioral risk markers of bacterial vaginosis[J]. Arch Gynecol Obstet, 2024,309(3):1053-1063.
[10] LI X,JIA L,LIN F,et al. Semi-supervised auto-segmentation method for pelvic organ-at-risk in magnetic resonance images based on deep-learning.[J].Journal of applied clinical medical physics,2024,25(3):e14296-e14296.
[11]王春杰，袁慧书.卷积神经网络在骨骼肌肉放射学中的研究进展[J].中国医学影像技术，2020,36(9):1375-1378..
[12] FAGHANI S, BAFFOUR F I, RINGLER M D, et al. A deep learning algorithm for detecting lytic bone lesions of multiple myeloma on CT[J]. Skeletal Radiol, 2023,52(1):91-98.
[13] FRITZ B, YI P H, KIJOWSKI R, et al. Radiomics and deep learning for disease detection in musculoskeletal radiology:an overview of novel MRI-and CT-based approaches[J]. Invest Radiol, 2023,58(1):3-13.
[14] FAGHANI S,NICHOLAS G R,PATEL S, et al. Development of a deep learning model for the automated detection of green pixels indicative of gout on dual energy CT scan[J].Research in Diagnostic and Interventional Imaging,2024,9(10):444-450.
[15] FAGHANI S,PATEL S,RHODES G N, et al.Deep-learning for automated detection of MSU deposits on DECT:evaluating impact on efficiency and reader confidence[J].Frontiers in Radiology,2024,41(3):30399-30405.
[16]叶勇军，卢陈英，周宝鹤，等.痛风性踝关节炎关节软骨T2值与软骨损伤的相关性研究[J].医学影像学杂志，2023,33(4):628-631.
[17]刘欣，杨海涛，王琪琪，等.MR T2WI单序列纹理分析对类风湿性关节炎和痛风性关节炎的鉴别诊断价值[J].磁共振成像，2021,12(5):50-54.
[18] WANG Q, YAO M, SONG X, et al. Automated segmentation and classification of knee synovitis based on MRI using deep learning[J]. Acad Radiol, 2024,31(4):1518-1527.
[19] NEVIN H,SAMAR T,AHMED E M, et al. Unsupervised cluster analysis of clinical and ultrasound features reveals unique gout subtypes:results from the egyptian college of rheumatology(ECR).[J]. Diabetes&metabolic syndrome,2023,17(12):102897-102897.
[20] HENSON M A. Interrogation of the perturbed gut microbiota in gouty arthritis patients through in silico metabolic modeling[J].Eng Life Sci, 2021,21(7):489-501.
[21] YOKOSE C, DALBETH N, WEI J, et al. Radiologic evidence of symmetric and polyarticular monosodium urate crystal deposition in gout-A cluster pattern analysis of dual-energy CT[J].Semin Arthritis Rheum, 2020,50(1):54-58.
[22]王飞跃，李长贵，国元元，等.平行高特：基于ACP的平行痛风诊疗系统框架[J].模式识别与人工智能，2017,30(12):1057-1068.
[23]刘士远.医学影像人工智能发展趋势与挑战[J].中华放射学杂志，2021,55(7):700-702.
[24] JIAN D,TIANGE L,A. D T, et al. GA-Net:a geographical attention neural network for the segmentation of body torso tissue composition[J]. Medical Image Analysis, 2024,91(8),102987-102993.
[25]李小雷.人工智能在医学影像图像处理中的研究进展[J].中国医学计算机成像杂志，2023,29(4):454-457.
基本信息:
DOI：10.20258/j.cnki.1006-9011.2025.03.033
中图分类号:TP18;R589.7
引用信息:
[1]马宏洋,刘力,钟威等.人工智能在痛风评估中的应用研究进展[J].医学影像学杂志,2025,35(03):142-146.DOI:10.20258/j.cnki.1006-9011.2025.03.033.
基金信息:
黑龙江省自然科学基金项目（编号：LH2022H106）