Automatic anal sphincter integrity detection from ultrasound images via convolutional neural networks.

Abstract

Background: The anal sphincter complex comprises the anal sphincter and the U-shaped deep and superficial puborectalis muscle. As an important supporting structure of the posterior pelvic floor, together with its surrounding tissues and muscles, the anal sphincter complex maintains the normal physiological functions of defecation and continence.

Objective: The plane required for diagnosing anal sphincter injury and the diagnosis of anal sphincter integrity through pelvic floor ultrasound are highly dependent on sonographers' experience. We developed a deep learning (DL) tool for the automatic diagnosis of anal sphincter integrity via pelvic floor ultrasound.

Methods: A 2D detection network was trained to detect the bounding box of the anal sphincter. The pelvic floor ultrasound image and its corresponding oval mask were input into a 2D classification network to determine the integrity of the anal sphincter. The average precision (AP) and intersection over union (IoU) were used to evaluate the performance of anal sphincter detection. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the classification model.

Results: The Pearson correlation coefficients (r values) of the topmost and bottommost layers detected by the CNN and sonographers were 0.932 and 0.978, respectively. The best DL model yielded the highest area under the curve (AUC) of 0.808 (95% CI: 0.698-0.921) in the test cohort. The results from the CNN agreed well with the diagnostic results of experienced sonographers.

Conclusions: We proposed, for the first time, a CNN to obtain the plane required for diagnosing anal sphincter injury on the basis of pelvic floor ultrasound and for preliminarily diagnosing anal sphincter injury.