found in 23 of the 35 (65.7%) cases with abnormal MDFE and was found in 17 of the 85 (20%) cases with normal MDFE. Conclusions: The MDFE can screen out most complex CHD during 13-14 weeks and can advance the fetal CHD screening system from the second trimester to the first trimester with a high degree of accuracy. VP17.11 The value of four-chamber view with three-vessel trachea view in ultrasonic screening for fetal cardiovascular anomalies in first trimester pregnancy
Oral communication abstractsResults: A total of 444 ultrasound evaluations were performed on 119 CHD fetuses, with a median of 2 measurements per fetus. CHD fetuses showed a small head at diagnosis (BPD −1.32 ± 0.99 z scores, HC −0.79 ± 1.02 z-scores), maintained throughout gestation (figure a). UtA and UA Doppler showed an increase towards the end of pregnancy, whereas no changes were observed in MCA or cerebroplacental ratio (CPR) with gestational age (figure b). Conclusions: CHD fetuses showed smaller head biometries since the second trimester of pregnancy, suggesting an early insult that persists throughout gestation. UtA and UA Doppler resistance increased, which may indicate co-existing placental impairment in CHD cases.Supporting information can be found in the online version of this abstract
Objective: Congenital heart defects (CHD) are still missed despite nearly universal prenatal ultrasound screening programs, which may result in severe morbidity or even death. Deep machine learning (DL) can automate image recognition from ultrasound. The aim of this study was to apply a previously developed DL model trained on images from a tertiary center, to fetal ultrasound images obtained during the second-trimester standard anomaly scan in a low-risk population. Methods: All pregnancies with isolated severe CHD in the Northwestern region of the Netherlands between 2015 and 2016 with available stored images were evaluated, as well as a sample of normal fetuses' examinations from the same region. We compared initial clinical diagnostic accuracy (made in real time), model accuracy, and performance of blinded human experts with access only to the stored images (like the model). We analyzed performance by study characteristics such as duration, quality (independently scored by study investigators), number of stored images, and availability of screening views. Results: A total of 42 normal fetuses and 66 cases of isolated CHD at birth were analyzed. Of the abnormal cases, 31 were missed and 35 were detected at the time of the clinical anatomy scan (sensitivity 53 percent). Model sensitivity and specificity was 91 and 93 percent, respectively. Blinded human experts (n=3) achieved sensitivity and specificity of 55+/-10 percent (range 47-67 percent) and 71+/-13 percent (range 57-83 percent), respectively. There was a statistically significant difference in model correctness by expert-grader quality score (p=0.04). Abnormal cases included 19 lesions the model had not encountered in its training; the model's performance (15/19 correct) was not statistically significantly different on previously encountered vs. never before seen lesions (p=0.07). Conclusions: A previously trained DL algorithm out-performed human experts in detecting CHD in a cohort in which over 50 percent of CHD cases were initially missed clinically. Notably, the DL algorithm performed well on community-acquired images in a low-risk population, including lesions it had not been previously exposed to. Furthermore, when both the model and blinded human experts had access to stored images alone, the model outperformed expert humans. Together, these findings support the proposition that use of DL models can improve prenatal detection of CHD.
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