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

• Published in: BMC Women's Health Vol. 24(1); no. 1; pp. 219 - 8
• Main Authors: 高橋 聡明, 玉井 奈緒, 真田 弘美, 仲上 豪二朗
• Format: Journal Article
• Language: English
• Published: London Springer Science and Business Media LLC 04.04.2024; BioMed Central; BioMed Central Ltd; BMC
• Subjects: Accuracy; Activities of daily living; Algorithms; Biofeedback; Biofeedback training; Bladder; Care and treatment; Diagnosis; Gynecology; Gynecology and obstetrics; Health aspects; Home care; Hospitals; Machine learning; Maternal and Child Health; Medical examination; Medicine; Medicine & Public Health; Methods; Pelvis; Public aspects of medicine; Questionnaires; RA1-1270; Reproductive Medicine; RG1-991; Time series; Ultrasonic imaging; Urinary incontinence; Urine; Urology; Womens health; Japan
• ISSN: 1472-6874; 1472-6874
• DOI: 10.1186/s12905-024-03041-y

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.