Background Placental growth and function are imperative for healthy fetal growth; data on placentas can inform research and clinical care. Measuring placental size after delivery should be easy, but current methods are hard to standardize and error prone. We developed PlacentaVision using artificial intelligence (AI)-based models, to automatically, accurately, and precisely measure placentas from digital photographs.

Objective We aimed to compare placental disc morphology between gross pathology examination (human measurements) and our automated PlacentaVision model (AI measurements).

Methods PlacentaVision is a multi-site study to assess placental morphology, features, and pathologies from digital photographs. We built a large dataset of digital placenta photographs and clinical data from singleton births at three large hospitals: Northwestern Memorial (Chicago; n=24,933), UPMC Magee-Womens (Pittsburgh; n=1198) and Mbarara Regional Referral (Uganda, n=1715). Data and images were from the medical record for Northwestern, part of a biobank study for Magee, and from our prospective studies for Mbarara. We compared long and short disc axis length (defined by Amsterdam criteria) between human and AI-based PlacentaVision measurements by calculating the difference and using Bland-Altman; we stratified by site, disc shape, infant sex, and term/preterm birth.

Results Mean (SD) disc length was 19.2 (3.1) and 18.6 (3.1) cm from PlacentaVision and human measurement, respectively, with a difference of 0.57 (2.19) cm. Disc width was 16.3 (2.3) cm and 16.1 (2.4) cm from PlacentaVision and human measurement, respectively, with a difference of 0.25 (1.85) cm. Bland-Altman limits of agreement were −3.7 to 4.9 cm for length and −3.4 to 3.9 cm for width. Irregularly-shaped placentas had a greater difference between PlacentaVision and human measurements compared to those with round/oval shapes (length differences of 1.53 and 0.45 cm respectively). Further, there were length differences by site (Northwestern 0.6, Magee 0.0, and Mbarara 0.4) and gestational age at birth (preterm 0.71, term 0.53 cm), but similar results for male and female placentas. Results for width were similar to length.

Conclusions AI-based measurements were less than a cm from human measurements overall. Our findings of larger differences for irregular shapes and preterm may indicate it is difficult for humans to measure irregular or small placentas according to protocol. PlacentaVision can automate and standardize the process.

The authors have declared no competing interest.

https://osf.io/9e6nm/overview

Research reported in this publication was supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health (NIH) under award R01EB030130. Data from Northwestern Memorial Hospital were collected by the hospital per standard protocol independent of the financial support from the NIH. Data from Magee-Womens Hopsital were obtained from the Steve N. Caritis Magee Obstetric Maternal-Infant (MOMI) Database and Biobank, supported by the R.K. Mellon Foundation and the University of Pittsburgh Clinical and Translational Science Institute (5UL1TR001857-02). Data collected at MUST in Uganda from a study supported by the National Institute of Child Health and Human Development of the NIH under awards R01HD112302 and K23AI138856, the Burroughs Wellcome Fund/American Society of Tropical Medicine and Hygiene Postdoctoral Fellowship (ASTMH), and NIH R01EB030130. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or ASTMH. The models to develop PlacentaVision used cluster computers at the National Center for Supercomputing Applications and the Pittsburgh Supercomputing Center through an allocation from the Advanced Cyberinfrastructure Coordination Ecosystem Services & Support (ACCESS) program, which is supported by NSF grants 2138259, 2138286, 2138307, 2137603, and 2138296.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The Pennsylvania State University Institutional Review Board (IRB) served as the single IRB for all research in the US and gave ethical approval for this work (STUDY00020697); the MUST Research Ethics Committee reviewed and gave ethical approval for the research at MUST (HS3159ES).

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