Camm E J, Wong G, Pan Y, Wang J Z, Goldstein J A, Arcot A, Murphy C N, Hansji H, Mangwiro Y T, Saffery R, Wlodek M E, Wyrwoll C S, Gernand A D, Kaitu'u-Lino T J
The Ritchie Centre, Hudson Institute of Medical Research, Clayton, VIC, Australia.
Translational Obstetrics Group, Mercy Hospital for Women, Dept. of Obstetrics and Gynaecology, University of Melbourne, Heidelberg, VIC, Australia.
Eur J Obstet Gynecol Reprod Biol. 2024 Aug;299:110-117. doi: 10.1016/j.ejogrb.2024.05.043. Epub 2024 Jun 1.
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.
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.
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.
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.
自动化胎盘评估能够大规模地进行准确且及时的形态学/病理学测量。我们开展了一项试点研究，使用基于人工智能的评估系统（AI-PLAX）来确定作为维多利亚时代计划一部分在全州范围内推广的潜力，评估分娩后时间、使用者以及用于图像采集的技术对一系列衍生胎盘数据的影响。
在分娩后0小时、24小时和48小时，由三名不同的使用者使用三种不同的成像技术（iPad、iPhone、三星手机）对10个胎盘进行成像。使用AI-PLAX确定胎盘盘状大小（短径和长径、周长、面积）、形状（正常、异常）、脐带插入类型（中央型、偏心型）、脐带螺旋、胎盘早剥（胎盘后血肿）和胎粪染色情况。
在分析胎盘母体面时，分娩后冷藏时间对胎盘尺寸有适度影响，观察到短径减小（24 - 48小时：-3.7%）、盘状面积减小（0 - 24小时：4.7%和0 - 48小时：-7.4%）以及周长减小（0 - 48小时：-3.8%）。当由不同使用者对胎盘进行成像时，对胎盘尺寸有轻微影响，包括长径增加（+1.9%）、盘状面积增加（+2.9%）和周长增加（+2.0%）。胎盘大小测量不受用于采集图像的技术类型影响。在分析胎盘胎儿面时，使用者之间观察到胎盘大小测量的差异更大。胎盘早剥检测不受任何参数影响。分娩与成像之间的时间影响明显的胎粪染色——可能反映了胎儿表面颜色随时间的变化。胎粪染色不受技术或使用者影响。
本研究支持通过AI-PLAX收集胎盘图像以供后续形态学分析的可行性，所获得的测量结果受冷藏时间、对胎盘成像的使用者或采集图像的技术影响最小。
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