Abstract
Objective 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. Key words: Ultrasonography, transperineal; Posterior bladder wall prolapse; Intelligent identification; Automatic measurement; Machine learning algorithm
Published Version
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