Assessment of an AI-based Tool for Population-wide Collection of Placental Morphological Data

E.J. Camm (1), G. Wong (2), Y. Pan (3), J.Z. Wang (3), J.A. Goldstein (4), A. Arcot (5), C.N. Murphy (2), H. Hansji (6), Y.T. Mangwiro (6), R. Saffery (6,7), M.E. Wlodek (2,6), C.S. Wyrwoll (8), A.D. Gernand (5), T.J. Kaitu'u-Lino (2)

(1) The Ritchie Centre, Hudson Institute of Medical Research, Clayton, VIC, Australia
(2) Translational Obstetrics Group, Mercy Hospital for Women, Dept. of Obstetrics and Gynaecology, University of Melbourne, Heidelberg, VIC, Australia
(3) College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA, USA
(4) Department of Pathology, Northwestern Feinberg School of Medicine, Chicago, IL, USA
(5) Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA, USA
(6) Murdoch Children’s Research Institute, The Royal Children’s Hospital, Parkville, VIC, Australia
(7) Department of Paediatrics, The University of Melbourne, Melbourne, VIC, Australia
(8) School of Human Sciences, The University of Western Australia, Perth, WA, Australia
Abstract:

Objectives

Automated placental assessment could allow accurate and timely morphological/pathological measurements at scale. We undertook a pilot study using an artificial intelligence-based assessment system (AI-PLAX) to ascertain the potential of a state-wide rollout as part of Generation Victoria, assessing the impact of time post-delivery, user, and technology used for image capture, on a range of derived placental data.

Study design

Ten placentas were imaged by three different users and imaging technologies (iPad, iPhone, Samsung) at (0 h), 24 h, and 48 h post-delivery. Using AI-PLAX, disc size (short and long length, perimeter, area), shape (normal, abnormal), cord insertion type (central, eccentric), cord coiling, abruption (retroplacental hematoma), and meconium staining were determined.

Results

When analysing the maternal surface of the placenta, time in cold storage post-delivery had modest effects on placental dimensions, with decreases in the short length (24–48 h: −3.7 %), disc area (0–24 h: 4.7 % and 0–48 h: −7.4 %), and perimeter (0–48 h: −3.8 %) observed. There was marginal impact on placental dimensions when the placenta was imaged by different users, including long length (+1.9 %), disc area (+2.9 %), and perimeter (+2.0 %). Measures of placental size were not impacted by the type of technology used to capture the images. When analysing the fetal surface of the placenta, more variance in placental size measures were observed between users. Abruption detection was not affected by any parameter. Time between delivery and imaging impacted apparent meconium staining – likely reflecting changes in fetal surface colour over time. Meconium staining was not affected by technology or user.

Conclusions

This study supports the feasibility of the collection of placenta images for later morphological analysis by AI-PLAX, with measures obtained minimally influenced by time in cold storage, user imaging the placenta, or technology to capture the images.

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Citation: E.J. Camm, G. Wong, Y. Pan, J.Z. Wang, J.A. Goldstein, A. Arcot, C.N. Murphy, H. Hansji, Y.T. Mangwiro, R. Saffery, M.E. Wlodek, C.S. Wyrwoll, A.D. Gernand and T.J. Kaitu'u-Lino, ``Assessment of an AI-based Tool for Population-wide Collection of Placental Morphological Data,'' European Journal of Obstetrics & Gynecology and Reproductive Biology, vol. 299, pp. 110-117, 2024.

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