Abstract：
OBJECTIVE: To develop and validate a fully automated AI system to extract standard planes, assess early gestational weeks, and compare the performance of the developed system to sonographers.
METHODS: In this three-center retrospective study, 214 consecutive pregnant women that underwent transvaginal ultrasounds between January and December 2018 were selected. Their ultrasound videos were automatically split into 38,941 frames using a particular program. First, an optimal deep-learning classifier was selected to extract the standard planes with key anatomical structures from the ultrasound frames. Second, an optimal segmentation model was selected to outline gestational sacs. Third, novel biometry was used to measure, select the largest gestational sac in the same video, and assess gestational weeks automatically. Finally, an independent test set was used to compare the performance of the system with that of sonographers. The outcomes were analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and mean similarity between two samples (mDice).
RESULTS: The standard planes were extracted with an AUC of 0.975, a sensitivity of 0.961, and a specificity of 0.979. The gestational sacs\' contours were segmented with a mDice of 0.974 (error less than 2 pixels). The comparison showed that the relative error of the tool in assessing gestational weeks was 12.44% and 6.92% lower and faster (min, 0.17 vs. 16.6 and 12.63) than that of the intermediate and senior sonographers, respectively.
CONCLUSIONS: This proposed end-to-end tool allows automatic assessment of gestational weeks in early pregnancy and may reduce manual analysis time and measurement errors.
CONCLUSIONS: The fully automated tool achieved high accuracy showing its potential to optimize the increasingly scarce resources of sonographers. Explainable predictions can assist in their confidence in assessing gestational weeks and provide a reliable basis for managing early pregnancy cases.
CONCLUSIONS: • The end-to-end pipeline enabled automatic identification of the standard plane containing the gestational sac in an ultrasound video, as well as segmentation of the sac contour, automatic multi-angle measurements, and the selection of the sac with the largest mean internal diameter to calculate the early gestational week. • This fully automated tool combining deep learning and intelligent biometry may assist the sonographer in assessing the early gestational week, increasing accuracy and reducing the analyzing time, thereby reducing observer dependence.
METHODS: In this three-center retrospective study, 214 consecutive pregnant women that underwent transvaginal ultrasounds between January and December 2018 were selected. Their ultrasound videos were automatically split into 38,941 frames using a particular program. First, an optimal deep-learning classifier was selected to extract the standard planes with key anatomical structures from the ultrasound frames. Second, an optimal segmentation model was selected to outline gestational sacs. Third, novel biometry was used to measure, select the largest gestational sac in the same video, and assess gestational weeks automatically. Finally, an independent test set was used to compare the performance of the system with that of sonographers. The outcomes were analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and mean similarity between two samples (mDice).
RESULTS: The standard planes were extracted with an AUC of 0.975, a sensitivity of 0.961, and a specificity of 0.979. The gestational sacs\' contours were segmented with a mDice of 0.974 (error less than 2 pixels). The comparison showed that the relative error of the tool in assessing gestational weeks was 12.44% and 6.92% lower and faster (min, 0.17 vs. 16.6 and 12.63) than that of the intermediate and senior sonographers, respectively.
CONCLUSIONS: This proposed end-to-end tool allows automatic assessment of gestational weeks in early pregnancy and may reduce manual analysis time and measurement errors.
CONCLUSIONS: The fully automated tool achieved high accuracy showing its potential to optimize the increasingly scarce resources of sonographers. Explainable predictions can assist in their confidence in assessing gestational weeks and provide a reliable basis for managing early pregnancy cases.
CONCLUSIONS: • The end-to-end pipeline enabled automatic identification of the standard plane containing the gestational sac in an ultrasound video, as well as segmentation of the sac contour, automatic multi-angle measurements, and the selection of the sac with the largest mean internal diameter to calculate the early gestational week. • This fully automated tool combining deep learning and intelligent biometry may assist the sonographer in assessing the early gestational week, increasing accuracy and reducing the analyzing time, thereby reducing observer dependence.
摘要：
目标：开发和验证全自动AI系统以提取标准飞机，评估早期孕周，并将开发的系统的性能与超声波检查人员进行比较。
方法：在这项三中心回顾性研究中，选择了在2018年1月至12月期间接受经阴道超声检查的214名连续孕妇。他们的超声视频使用特定程序自动分为38,941帧。首先,选择最佳深度学习分类器从超声帧中提取具有关键解剖结构的标准平面.第二,选择了最佳分割模型来勾画妊娠囊的轮廓。第三，新的生物统计学被用来测量，在同一视频中选择最大的孕囊，并自动评估孕周。最后,使用独立的测试集来比较系统的性能与超声波检查者的性能。使用受试者工作特征曲线下面积(AUC)分析结果，灵敏度,特异性，和两个样本之间的平均相似性(mDice)。
结果:以0.975的AUC、0.961的灵敏度和0.979的特异性提取标准平面。以0.974的mDice分割妊娠囊的轮廓(误差小于2个像素)。比较表明，该工具评估孕周的相对误差分别为12.44％和6.92％，更低和更快（min，0.17vs.16.6和12.63）比中级和高级超声医师的水平高，分别。
结论：该拟议的端到端工具允许在妊娠早期自动评估孕周，并可能减少手动分析时间和测量误差。
结论：全自动工具实现了高精度，显示出其优化日益稀缺的超声医师资源的潜力。可解释的预测可以帮助他们评估孕周的信心，并为管理早孕病例提供可靠的基础。
结论：•端到端管道能够在超声视频中自动识别包含妊娠囊的标准平面，以及囊轮廓的分割，自动多角度测量，选择平均内径最大的囊来计算孕周早期。•这种结合了深度学习和智能生物测量的全自动工具可以帮助超声医师评估孕周早期。提高准确性并减少分析时间，从而减少对观察者的依赖。
方法：在这项三中心回顾性研究中，选择了在2018年1月至12月期间接受经阴道超声检查的214名连续孕妇。他们的超声视频使用特定程序自动分为38,941帧。首先,选择最佳深度学习分类器从超声帧中提取具有关键解剖结构的标准平面.第二,选择了最佳分割模型来勾画妊娠囊的轮廓。第三，新的生物统计学被用来测量，在同一视频中选择最大的孕囊，并自动评估孕周。最后,使用独立的测试集来比较系统的性能与超声波检查者的性能。使用受试者工作特征曲线下面积(AUC)分析结果，灵敏度,特异性，和两个样本之间的平均相似性(mDice)。
结果:以0.975的AUC、0.961的灵敏度和0.979的特异性提取标准平面。以0.974的mDice分割妊娠囊的轮廓(误差小于2个像素)。比较表明，该工具评估孕周的相对误差分别为12.44％和6.92％，更低和更快（min，0.17vs.16.6和12.63）比中级和高级超声医师的水平高，分别。
结论：该拟议的端到端工具允许在妊娠早期自动评估孕周，并可能减少手动分析时间和测量误差。
结论：全自动工具实现了高精度，显示出其优化日益稀缺的超声医师资源的潜力。可解释的预测可以帮助他们评估孕周的信心，并为管理早孕病例提供可靠的基础。
结论：•端到端管道能够在超声视频中自动识别包含妊娠囊的标准平面，以及囊轮廓的分割，自动多角度测量，选择平均内径最大的囊来计算孕周早期。•这种结合了深度学习和智能生物测量的全自动工具可以帮助超声医师评估孕周早期。提高准确性并减少分析时间，从而减少对观察者的依赖。
