Pelvic floor muscle contraction automatic evaluation algorithm for pelvic floor muscle training biofeedback using self-performed ultrasound.

INTRODUCTION: Non-invasive biofeedback of pelvic floor muscle training (PFMT) is required for continuous training in home care. Therefore, we considered self-performed ultrasound (US) in adult women with a handheld US device applied to the bladder. However, US images are difficult to read and require assistance when using US at home. In this study, we aimed to develop an algorithm for the automatic evaluation of pelvic floor muscle (PFM) contraction using self-performed bladder US videos to verify whether it is possible to automatically determine PFM contraction from US videos. METHODS: Women aged ≥ 20 years were recruited from the outpatient Urology and Gynecology departments of a general hospital or through snowball sampling. The researcher supported the participants in their self-performed bladder US and videos were obtained several times during PFMT. The US videos obtained were used to develop an automatic evaluation algorithm. Supervised machine learning was then performed using expert PFM contraction classifications as ground truth data. Time-series features were generated from the x- and y-coordinate values of the bladder area including the bladder base. The final model was evaluated for accuracy, area under the curve (AUC), recall, precision, and F1. The contribution of each feature variable to the classification ability of the model was estimated. RESULTS: The 1144 videos obtained from 56 participants were analyzed. We split the data into training and test sets with 7894 time series features. A light gradient boosting machine model (Light GBM) was selected, and the final model resulted in an accuracy of 0.73, AUC = 0.91, recall = 0.66, precision = 0.73, and F1 = 0.73. Movement of the y-coordinate of the bladder base was shown as the most important. CONCLUSION: This study showed that automated classification of PFM contraction from self-performed US videos is possible with high accuracy.

Expert Consensus Document: An Algorithm for the Care and Treatment of Patients with Constipation Based on Ultrasonographic Findings in the Rectum.

Chronic constipation is a common gastrointestinal disorder, and its management is critical. However, it is extremely difficult to assess its subjective symptoms when patients are unable to report them due to cognitive or physical disabilities, especially in cases of patients with incurable geriatric, pediatric, palliative, psychiatric, or neurological diseases. We had previously established a protocol for observing and assessing rectal fecal retention using ultrasonography and for classifying cases into three categories based on the rectal findings: no fecal retention, fecal retention without hard stools, and fecal retention with hard stools. However, although the detection of rectal fecal retention using ultrasonography would be expected to lead to better therapeutic management, there is no standard algorithm for selecting specific treatments and defecation care options based on ultrasonographic findings. Therefore, we organized an expert consensus meeting of multidisciplinary professionals to develop such an algorithm based on rectal ultrasonography findings for patients with constipation in both residential and hospital settings.