摘要目的 探讨经会阴盆底超声智能识别及自动测量软件量化评价膀胱后壁脱垂的可行性.方法 采集170例受试者有效Valsalva动作的前盆腔超声动态图,并随机分为训练组和测试组各85例,离线标记训练组图像相关结构.运用机器学习算法对训练组图像上述标记进行分析得出识别规律,获得盆底超声智能识别及自动测量软件,应用该软件识别测试组图像,得出脱垂距离及相应分度.三位医生分别离线标记测试组图像相应结构2次,间隔时间至少2周.比较自动测量与手动测量测试组的结果差异.结果 通过学习训练组图像获得的软件能够识别出测试组图像相应标记,并且同一医生两次间脱垂分度结果一致性较高(κ : 0 .72～0 .78 ; ICC : 0 .980～0 .990) ; 3位医生两两间脱垂分度结果一致性较高(κ : 0 .65～0 .75 ; ICC : 0 .985～0 .992) ;盆底自动测量与手动测量的脱垂分度一致性较高(κ : 0 .63～0 .67 ;ICC :0 .967～0 .969 ;r :0 .936 ,0 .943 ,0 .936 ,均 P ＜0 .01).结论 盆底超声智能识别及自动测量软件可实现对图像结构的识别且量化评价膀胱后壁脱垂的可信度较高.

abstractsObjective To investigate the feasibility of the automatic cystocele severity grading software for quantitative evaluation of prolapse of bladder posterior wall by transperineal ultrasound . Methods One hundred and seventy transperineal ultrasound video clips were recorded when the female patients performing the Valsalva maneuver and those clips were divided into training group ( 85 cases) and test group ( 85 cases) randomly ,then the ralated structures of the images from the training group offline were marked . Through machine learning algorithm ,the computer had learned and was able to analyzed the marking information ,then the automatic cystocele severity grading software was obtained . And later the software was ran to mark the structures and get the cystocele severity grading in the images from the test group . Meanwhile , the same structures of the same images manually were marked and after an interval of more than two weeks the process were repeated by 3 doctors . Finally the grading results obtained from the software and the measurers of the 3 doctors were compared . Results The intelligent identification and automatic measurement software obtained from the machine learning algorithm was able to identify the related structures . The grading results of each measurer were of good consistency ( κ :0 .72 －0 .78 ;ICC :0 .980－0 .990) . The grading results between different measurers were of good consistency ( κ :0 .65－0 .75 ;ICC :0 .985－0 .992) . The grading results between automatic software and three different measurers were of good consistency ( κ :0 .63－0 .67 ;ICC :0 .967－0 .969 ; r ＝0 .936 ,0 .943 ,0 .936 ,all P ＜0 .01) . Conclusions The automatic cystocele severity grading software is able to identify the related structures in the images and reliable to apply the software in pelvic floor ultrasound .