CR-UNET-BASED ULTRASONIC FOLLICLE MONITORING TO REDUCE DIAMETER VARIABILITY AND GENERATE AREA AUTOMATICALLY AS A NOVEL BIOMARKER FOR FOLLICULAR MATURITY

Follicle size is closely related to ovarian function and is an important biomarker in transvaginal ultrasound examinations for assessing follicular maturity during an assisted reproduction cycle. However, manual measurement is time consuming and subject to high inter- and intra-observer variability. Based on the deep learning model CR-Unet described in our previous study, the aim of our present study was to investigate further the feasibility of using this model in clinical practice by validating its performance in reducing the inter- and intra-observer variability of follicle diameter measurement. This study also investigated whether follicular area is a better biomarker than diameter in assessing follicular maturity. Data on 106 ovaries and 230 follicles collected from 80 cases of single follicular cycles and 26 cases of multiple follicular cycles constituted the validation set. Intra-observer variability was 0.973 and 0.982 for the senior sonographer and junior sonographer in single follicular cycles and 0.979 (0.971, 0.985) and 0.920 (0.892, 0.943) in multiple follicular cycles, respectively, while CR-Unet had no intra-group variation. Bland-Altman plot analysis indicated that the 95% limits of agreement between senior sonographer and CR-Unet (-2.1 to 1.1 mm, -2.02 to 0.75 mm) were smaller than those between senior sonographer and junior sonographer (-1.51 to 1.15 mm, -2.1 to 1.56 mm) in single and multiple follicular cycles. The average operating times of diameter measurement taken by the junior sonographer, senior sonographer and CR-Unet were 7.54 +/- 1.8, 4.87 +/- 0.84 and 1.66 +/- 0.76 s, respectively (p < 0.001). Correlation analysis indicated that both manual and automated follicular area correlated better with follicular volume than diameter. The deep learning algorithm and the new biomarker of follicular area hold potential for clinical application of ultrasonic follicular monitoring. (E-mail: zhiyi_chen@gzhmu.edu.cn) (C) 2020 The Author(s). Published by Elsevier Inc. on behalf of World Federation for Ultrasound in Medicine & Biology.