Year: 2025
Type: article
Abstract: OBJECTIVES: To evaluate the performance of a CNN-based (convolutional neural networks-based) AI software for automatic recognition and measurement of minimal levator hiatus in transperineal ultrasound volumes from postpartum women, and to assess its agreement with manual measurements in different functional states. METHODS: We conducted a retrospective analysis of 100 transperineal ultrasound volumes measured independently by two sonographers (one junior, one senior) using a SonoScape S60 ultrasound system. Manual measurements included anteroposterior diameter (AP), left-to-right diameter (LR), levator hiatus area (HA), which were assessed at rest, during maximum pelvic floor contraction maneuver, and during maximum Valsalva maneuver. The levator-urethra gap (LUG) was additionally measured during contraction. The same volumes were identified and measured using automated CNN-based software (Auto PF software, SonoScape). When automatic identification failed, manual slice adjustment was permitted before reattempting automated measurement. The automatic recognition rate was recorded in different functional states. Inter-rater reliability between manual and automated measurements was evaluated using intraclass correlation coefficient (ICC) and Bland-Altman analysis. RESULTS: The overall automatic recognition rate was 86.69%, varying by functional state: 94% at rest, 86.17% during contraction, 79.80% during Valsalva. Automated measurements showed good agreement with manual measurements for HA, AP and LR (ICC > 0.75), excellent agreement with the senior sonographer's measurement for HA, AP (ICC > 0.90). The senior sonographer's measurements demonstrated higher concordance with automated results. Furthermore, the agreement was poorest for LUG measurements. CONCLUSIONS: Automated software is feasible for the recognition and measurement of LH in postpartum women, which can reduce the inter-observer variability, standardize pelvic floor assessment and improve workflow efficiency. Our study supports the clinical utility of CNN-based AI ultrasound software, but optimization is needed for LUG measurements to enhance clinical applicability.
DOI: 10.1002/jum.70139
Authors: Fanhua Xu, Qi Lin, Wei Zeng, Jinfeng Xu, Yujuan Zhang
Language: English
Subfield: Rheumatology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4416683561