Automatic classification of levator ani muscle avulsion in 3D transperineal ultrasound images

Levator ani muscle (LAM) avulsion is a pelvic floor injury often resulting from vaginal childbirth and is linked to the development of pelvic organ prolapse. A previous publication has indicated that up to 40% of women worldwide will experience this pelvic floor disorder in their lifetime. Diagnosis of LAM avulsion is commonly done through ultrasound imaging and requires trained experts, often leading to weeks-long delays in receiving diagnostic results and treatment. This study developed a deep learning system to automatically classify the degree of LAM avulsion from 3D transperineal ultrasound images, aiming to expedite the diagnostic process. By analyzing these images, our model accurately identifies the presence and severity of LAM avulsion in each patient. This automated approach addresses the challenges of manual ultrasound diagnosis and enhances screening accessibility, benefiting areas with limited healthcare resources, by reducing the need and time for expert review.