Yuanyuan Pei1, Wenjing Gao1, Longjiang E1, Changpin Dai2, Jin Han3, Haiyu Wang2, Huiying Liang1,4,5. 1. Clinical Data Center of Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China. 2. Department of Ultrasonography, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China. 3. Prenatal Diagnosis Center of Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China. 4. Clinical Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, China. 5. Guangdong Provincial Children's Medical Research Center, Guangzhou, China.
Abstract
Background: Early gestational age (GA) assessment using ultrasound is a routine and frequent examination performed in hospitals whereby clinicians manually measure the size of the gestational sac using ultrasound and calculate GA. However, the error is often substantial, and the process is laborious. To overcome these challenges, we propose a new system to assess early GA using a new end-to-end computer vision system and a new biometric measurement method based on ultrasound video. Methods: In this retrospective study, a new system was provided. B-ultrasound videos were first decomposed into two-dimensional (2D) images, and the contours of the gestational sac were extracted and drawn. The maximum length and short diameter of the gestational sac were then automatically measured and GA was calculated using the Hellman formula. Finally, through human-machine comparison, the clinicians' assessment errors were analyzed by SPSS 26. Results: In this study, 29,829 2D images of 191 B-ultrasound videos were evaluated using the new system. Clinicians usually require 15-20 min to complete assessments of GA, whereas with the new system assessments can be completed in approximately 30 s. Moreover, a human-machine comparison showed that the system helped intermediate skills clinicians improve their relative diagnostic error by 13.45% with an absolute error of 7 days. In addition, the new system was used to identify other lesions in the uterus and measure their size as a "sanity check". Conclusions: The proposed new system is a practical, reproducible, and reliable approach for assessing early GA. 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Background: Early gestational age (GA) assessment using ultrasound is a routine and frequent examination performed in hospitals whereby clinicians manually measure the size of the gestational sac using ultrasound and calculate GA. However, the error is often substantial, and the process is laborious. To overcome these challenges, we propose a new system to assess early GA using a new end-to-end computer vision system and a new biometric measurement method based on ultrasound video. Methods: In this retrospective study, a new system was provided. B-ultrasound videos were first decomposed into two-dimensional (2D) images, and the contours of the gestational sac were extracted and drawn. The maximum length and short diameter of the gestational sac were then automatically measured and GA was calculated using the Hellman formula. Finally, through human-machine comparison, the clinicians' assessment errors were analyzed by SPSS 26. Results: In this study, 29,829 2D images of 191 B-ultrasound videos were evaluated using the new system. Clinicians usually require 15-20 min to complete assessments of GA, whereas with the new system assessments can be completed in approximately 30 s. Moreover, a human-machine comparison showed that the system helped intermediate skills clinicians improve their relative diagnostic error by 13.45% with an absolute error of 7 days. In addition, the new system was used to identify other lesions in the uterus and measure their size as a "sanity check". Conclusions: The proposed new system is a practical, reproducible, and reliable approach for assessing early GA. 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Authors: Serena Yeung; Francesca Rinaldo; Jeffrey Jopling; Bingbin Liu; Rishab Mehra; N Lance Downing; Michelle Guo; Gabriel M Bianconi; Alexandre Alahi; Julia Lee; Brandi Campbell; Kayla Deru; William Beninati; Li Fei-Fei; Arnold Milstein Journal: NPJ Digit Med Date: 2019-03-01