探讨基于APP的教考结合自主学习模式在小关节超声培训中的应用价值。

13名超声医师通过APP中设置的电子课程自主学习“小关节超声评分”，随后进入APP超声图文考核平台，对140个类风湿关节炎手部小关节［第2/3掌指关节（MCP2/3）、腕关节及第2/3远端指间关节（PIP2/3）］以选择题方式进行自主评分，采用超声诊断符合率及组内相关系数（ICC）对培训效果进行分析。

在小关节有无滑膜炎、腱鞘炎及骨侵蚀判断方面，观察者间一致性中等~极好（ICC=0.49~0.97），且超声诊断符合率较好（76.4%~90.4%），而滑膜炎能量多普勒超声（PDUS）较灰阶超声评分培训效果好（75.7%/0.88 vs 45.2%/0.65）。其中MCP2滑膜炎PDUS及骨侵蚀培训效果最好（80.8%/0.77、88.5%/0.95）。MCP2/3滑膜炎灰阶超声评分一致性良好，但超声误判较高（0.63、0.75；44.4%、46.2%）。腕关节骨侵蚀及PIP2/3滑膜炎灰阶超声评分一致性较差、超声诊断符合率较低（27.4%~53.8%/0.14~0.37）。腕关节滑膜炎灰阶超声评分、PIP2/3及MCP3骨侵蚀判断超声诊断符合率高，但一致性差（74.40%~81.2%；0.24~0.38）。MCP2与MCP3关节，PIP2与PIP3关节整体超声培训效果相似（71.8%/0.83 vs 64.7%/0.83；58.1%/0.60 vs 59.3%/0.45）。

基于APP的自主学习及考核可作为关节超声的有效教学及考核手段，在精细分析考核结果的基础上，针对误判率较高、一致性较差的病变类型进行图像规范化、诊断标准化的递进强化培训，将有助于进一步优化教学效果并提高超声医师临床诊断能力。

To explore the application value of APP-based self-learning and teaching mode in small joint ultrasound training.

Thirteen sonographers were included in this study. First, they were allowed to learn "the joint ultrasound scoring system" independently through the electronic course set in the APP. Following the course, they were admitted to enter "the APP ultrasonographic image assessment platform" to perform ultrasound scoring for 140 small joints with inflammatory rheumatic diseases in the form of multiple choices. The ultrasound score included the second and third metacarpophalangeal joints (MCP2/3), wrist joints, and the second and third proximal interphalangeal joints (PIP2/3), which were evaluated for synovitis, tenosynovitis/paratenonitis, and erosions from the dorsal side by gray-scale ultrasound (GSUS) and power Doppler ultrasound (PDUS). The overall agreement rate (%) and intraclass correlation coefficient (ICC) values were calculated to analyze the ultrasound training effect.

A moderate to good interobserver agreement was observed for the detection of synovitis, tenosynovitis, and erosions (ICC=0.49-0.97, overall agreement 76.4%~90.4%). In detail, the ICC value for GSUS in evaluating synovitis was only 0.65 (overall agreement 45.2%), and for PDUS it was 0.88 (overall agreement 75.7%). The best training results were found for the detection of synovitis and erosions by PDUS in the MCP2 joint (80.8%/0.77; 88.5%/0.95). Good ICC values were observed for GSUS in the detection of synovitis in the MCP2/3 joints; however, the overall agreement rates were lower (0.63, 0.75; 44.4%, 46.2%). Poor agreement values were observed for GSUS in detecting erosions in both the wrist region and synovitis in PIP2/3 joints (27.4%-53.8%; 0.14-0.37). Good overall agreement rates were observed for GSUS in the detection of synovitis in the wrist, and erosions in PIP2/3 and MCP3 joints, while the ICC values were lower (74.40%-81.2%; 0.24-0.38). The overall ultrasound training effect was similar between MCP2 and MCP3 joints (71.8%/0.83 vs 64.7%/0.83), as well as between PIP2 and PIP3 joints (58.1%/0.60 vs 59.3%/0.45).

APP-based self-learning and testing mode has the potential to be an effective tool in joint ultrasound training. More training and standardization of image acquisition, and adequate interpretation for multiple joints with poor agreement values will help to further optimize the teaching effect and improve the clinical diagnostic ability.