366: Placental volume measurements early in pregnancy predict adverse perinatal outcomes

Schwartz, Nadav; Coletta, Jaclyn; Srinivas, Sindhu K.; Pessel, Cara; Timor, Ilan; Parry, Sam; Salafia, Carolyn

doi:10.1016/j.ajog.2009.10.381

Cited by 3 publications

(2 citation statements)

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“…The AUC results from our logistic regression model were comparable to or higher than previous work. 7,8 The main advantage of our model is that apart from PV, it only uses easily obtainable data as features (tobacco use, chronic hypertension, pregestational diabetes, nulliparity, age, race, and BMI). Therefore, it does not require additional blood tests, imaging, or repeat visits.…”

Section: Discussionmentioning

confidence: 99%

“…Placental size, measured by 2D ultrasound has demonstrated weak associations with adverse outcomes. 4 Advances in 3D ultrasound (3DUS) have allowed investigators to demonstrate associations between first trimester placental volume (PV) and clinical outcomes, [5][6][7][8][9] but the currently available segmentation techniques are laborious and require significant offline processing with manual input. In order to develop clinical tools that can ultimately be incorporated into clinical practice, automated segmentation methods are needed that can robustly evaluate placental morphology in a validated and clinically relevant manner.…”

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confidence: 99%

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Fully Automated Placental Volume Quantification From 3D Ultrasound for Prediction of Small‐for‐Gestational‐Age Infants

Schwartz

Oguz

Wang

et al. 2021

J of Ultrasound Medicine

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Objectives-Early placental volume (PV) has been associated with small-for-gestational-age infants born under the 10th/5th centiles (SGA10/SGA5). Manual or semiautomated PV quantification from 3D ultrasound (3DUS) is time intensive, limiting its incorporation into clinical care. We devised a novel convolutional neural network (CNN) pipeline for fully automated placenta segmentation from 3DUS images, exploring the association between the calculated PV and SGA.Methods-Volumes of 3DUS obtained from singleton pregnancies at 11-14 weeks' gestation were automatically segmented by our CNN pipeline trained and tested on 99/25 images, combining two 2D and one 3D models with downsampling/ upsampling architecture. The PVs derived from the automated segmentations (PV CNN ) were used to train multivariable logistic-regression classifiers for SGA10/ SGA5. The test performance for predicting SGA was compared to PVs obtained via the semiautomated VOCAL (GE-Healthcare) method (PV VOCAL ).Results-We included 442 subjects with 37 (8.4%) and 18 (4.1%) SGA10/SGA5 infants, respectively. Our segmentation pipeline achieved a mean Dice score of 0.88 on an independent test-set. Adjusted models including PV CNN or PV VOCAL were similarly predictive of SGA10 (area under curve [AUC]: PV CNN = 0.780, PV VOCAL = 0.768). The addition of PV CNN to a clinical model without any PV included (AUC = 0.725) yielded statistically significant improvement in AUC (P < .05); whereas PV VOCAL did not (P = .105). Moreover, when predicting SGA5, including the PV CNN (0.897) brought statistically significant improvement over both the clinical model (0.839, P = .015) and the PV VOCAL model (0.870, P = .039).Conclusions-First trimester PV measurements derived from our CNN segmentation pipeline are significantly associated with future SGA. This fully automated tool enables the incorporation of including placental volumetric biometry into the bedside clinical evaluation as part of a multivariable prediction model for risk stratification and patient counseling.

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Section: Discussionmentioning

confidence: 99%