Neural networks for automatic scoring of arthritis disease activity on ultrasound images
Neural networks for automatic scoring of arthritis disease activity on ultrasound images
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DOI：
10.1136/rmdopen-2018-000891
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发表时间：
2019-02-01
期刊：
影响因子：
6.2
通讯作者：
Just, Soren Andreas
中科院分区：
文献类型：
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作者：
Andersen, Jakob Kristian Holm;Pedersen, Jannik Skyttegaard;Just, Soren Andreas
基于OMERACT-EULAR滑膜炎评分（OESS）系统的超声（US）扫描和评估滑膜炎活动性的标准化方法的开发，是将US应用于炎性关节炎患者诊断和监测的重要一步。超声图像对疾病活动性的不同解释会影响临床试验的诊断、治疗和结果。因此，我们开始研究我们是否可以利用神经网络架构来解释多普勒超声图像上的疾病活动，使用OESS评分系统。方法采用两种最先进的神经网络对1342张类风湿性关节炎（RA）患者的多普勒超声图像进行信息提取。一个神经网络将图像分为健康（多普勒OESS评分0或1）或病变（多普勒OESS评分2或3）。另一个是对所有四个OESS系统的图像进行多普勒US评分（0-3）。神经网络随后在一组新的RA多普勒US图像（n=176）上进行测试。风湿病学家评分和网络评分之间的一致性用kappa统计量来衡量。结果与风湿病专家相比，神经网络评估健康/病变评分的准确率最高，分别为86.4%和86.9%，灵敏度分别为0.864和0.875，特异性分别为0.864和0.864。另一种神经网络发展为四类多普勒OESS评分，平均每类准确率为75.0%，二次加权kappa评分为0.84。结论本研究首次展示了基于OESS系统的神经网络技术可用于多普勒超声图像的疾病活动性评分。
Background The development of standardised methods for ultrasound (US) scanning and evaluation of synovitis activity by the OMERACT-EULAR Synovitis Scoring (OESS) system is a major step forward in the use of US in the diagnosis and monitoring of patients with inflammatory arthritis. The variation in interpretation of disease activity on US images can affect diagnosis, treatment and outcomes in clinical trials. We, therefore, set out to investigate if we could utilise neural network architecture for the interpretation of disease activity on Doppler US images, using the OESS scoring system.Methods Two state-of-the-art neural networks were used to extract information from 1342 Doppler US images from patients with rheumatoid arthritis (RA). One neural network divided images as either healthy (Doppler OESS score 0 or 1) or diseased (Doppler OESS score 2 or 3). The other to score images across all four of the OESS systems Doppler US scores (0-3). The neural networks were hereafter tested on a new set of RA Doppler US images (n=176). Agreement between rheumatologist's scores and network scores was measured with the kappa statistic.Results For the neural network assessing healthy/diseased score, the highest accuracies compared with an expert rheumatologist were 86.4% and 86.9% with a sensitivity of 0.864 and 0.875 and specificity of 0.864 and 0.864, respectively. The other neural network developed to four class Doppler OESS scoring achieved an average per class accuracy of 75.0% and a quadratically weighted kappa score of 0.84.Conclusion T his study is the first to show that neural network technology can be used in the scoring of disease activity on Doppler US images according to the OESS system.