Is It Possible to Diagnose Surgical Uterine Prolapse With Transperineal Ultrasound? Multicenter Validation of Diagnostic Software

Applicability of Deep Learning to Dynamically Identify the Different Organs of the Pelvic Floor in the Midsagittal Plane

INTRODUCTION AND HYPOTHESIS

The objective was to create and validate the usefulness of a convolutional neural network (CNN) for identifying different organs of the pelvic floor in the midsagittal plane via dynamic ultrasound.

METHODS

This observational and prospective study included 110 patients. Transperineal ultrasound scans were performed by an expert sonographer of the pelvic floor. A video of each patient was made that captured the midsagittal plane of the pelvic floor at rest and the change in the pelvic structures during the Valsalva maneuver. After saving the captured videos, we manually labeled the different organs in each video. Three different architectures were tested-UNet, FPN, and LinkNet-to determine which CNN model best recognized anatomical structures. The best model was trained with the 86 cases for the number of epochs determined by the stop criterion via cross-validation. The Dice Similarity Index (DSI) was used for CNN validation.

RESULTS

Eighty-six patients were included to train the CNN and 24 to test the CNN. After applying the trained CNN to the 24 test videos, we did not observe any failed segmentation. In fact, we obtained a DSI of 0.79 (95% CI: 0.73 - 0.82) as the median of the 24 test videos. When we studied the organs independently, we observed differences in the DSI of each organ. The poorest DSIs were obtained in the bladder (0.71 [95% CI: 0.70 - 0.73]) and uterus (0.70 [95% CI: 0.68 - 0.74]), whereas the highest DSIs were obtained in the anus (0.81 [95% CI: 0.80 - 0.86]) and levator ani muscle (0.83 [95% CI: 0.82 - 0.83]).

CONCLUSIONS

Our results show that it is possible to apply deep learning using a trained CNN to identify different pelvic floor organs in the midsagittal plane via dynamic ultrasound.

The Application of Shear Wave Elastography to Determine the Elasticity of the Levator Ani Muscle and Vaginal Tissue in Patients With Pelvic Organ Prolapse

OBJECTIVES

The changes of the extracellular matrix of the connective tissue have significantly contributed to the incidence of pelvic organ prolapse (POP). It seems reasonable that sonoelastography could be a useful tool to evaluate the elasticity of pelvic floor tissue in patients with POP and compare it to those without POP. The main aim of this pilot study was to determine if there are differences in the elasticity of the levator ani muscle (LAM) and vaginal tissue between patients with and without POP.

METHODS

Prospective observation study, including 60 patients (30 with POP and 30 without POP). Sonoelastography was performed to evaluate the elasticity (in kilopascals, kPa) of the following regions of interest: vagina at the level of middle third of the urethra; vagina at the level of the bladder trigone; vagina in the anterior and posterior fornix; vagina at the level of middle third of the anorectal canal; posterior third of the LAM.

RESULTS

A total of 60 patients completed the study (30 with POP, 30 without POP). In the POP group, 18/30 (60%) had an anterior vaginal wall prolapse, 3/30 (10%) a uterine prolapse, 15/30 (50%) a rectocele, and 6/30 (20%) a enterocele. Patients with POP had higher elasticity in all anatomical study areas, with statistically significant differences in the anterior fornix (13.6 vs 11.2 kPa; P: .012). A multiple regression (controlling age, menopausal stage, and parity) allowed to detect statistically significant differences in the elasticity of the middle third of the urethra (P: .03) and the middle third of the anorectal canal (P: .019).

CONCLUSION

It is possible to evaluate the elasticity of the LAM and vaginal tissue using sonoelastography, detecting a higher elasticity in patients with POP than in those without POP.

Is the Presence of Levator Ani Muscle Avulsion Relevant for the Diagnosis of Uterine Prolapse?

OBJECTIVE

To determine if the addition of the assessment of levator ani muscle (LAM) avulsion to the measurement of the difference in the pubis-uterine fundus distance between rest and with the Valsalva maneuver could increase the diagnostic capacity of ultrasound for uterine prolapse (UP).

METHODS

This multicenter, observational and prospective study included 145 patients. Ultrasound assessment was performed, establishing the diagnosis of UP as a difference between the pubic-uterine fundus distance at rest and during the Valsalva maneuver ≥15 mm (standard technique), while LAM avulsion was defined as an abnormal LAM insertion in three central slices using multislice ultrasound. A binary multivariate logistic regression model was made using nonautomated methods to predict surgical UP (general population, premenopausal, and postmenopausal patients), including the difference between the pubis-uterine fundus distance at rest and with the Valsalva maneuver as well as LAM avulsion.

RESULTS

A total of 143 patients completed the study. The addition of LAM avulsion criteria to the standard dynamic distance-based protocol for the diagnosis of UP resulted in a higher sensitivity for the general population (79.7 vs 68.1%) as well as for premenopausal (89.3 vs 79.9%) and postmenopausal patients (76 vs 66.1%). In contrast, the standard technique showed a higher specificity than the model based on the standard technique associated with LAM avulsion for the general population (89.2 vs 74.3%) and premenopausal women (91.7 vs 63.2%). For postmenopausal patients, the model based on the standard technique associated with LAM avulsion had a higher sensitivity (76 vs 66.1%) and specificity (91.7 vs 86.8%) than the ultrasound diagnosis of UP.

CONCLUSION

The implementation of the assessment of LAM avulsion in the ultrasound diagnosis of UP is useful in postmenopausal patients, increasing sensitivity and specificity relative to the ultrasound assessment based only on the difference between the pubis-uterine fundus distance at rest and with the Valsalva maneuver.