Kozlovska H. O., Demchenko K. O., Hrytsenko A. O.
MORPHOLOGICAL ANALYSIS OF THE PLACENTA USING ARTIFICIAL INTELLIGENCE
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About the author:
Kozlovska H. O., Demchenko K. O., Hrytsenko A. O.
Heading:
PROSPECTS FOR THE DEVELOPMENT OF MORPHOLOGICAL AND CLINICAL RESEARCH
Type of article:
Scientific article
Annotation:
The placenta is a key organ of the feto-maternal unit, and its morphological status reflects the course of pregnancy and perinatal outcomes. Conventional light microscopy and morphometry remain the gold standard for morphological assessment; however, they are labor-intensive and partly subjective. The development of digital pathology and artificial intelligence (AI), particularly deep learning, has introduced new opportunities for the automated analysis of placental histological specimens and for the detection of changes associated with placental dysfunction and pregnancy complications. The aim of this review is to summarize current literature data on the application of artificial intelligence algorithms in the morphological analysis of the human placenta. Studies published between 2020 and 2025 were reviewed, focusing on the use of deep learning and computer vision methods for the quantitative assessment of placental structural components and for the identification of lesions associated with preeclampsia and other pregnancy complications. It is shown that the use of AI improves the objectivity and reproducibility of morphological assessment and expands the possibilities of morphometric analysis. At the same time, the main limitations of implementing these approaches are outlined, including the need for standardized research protocols and rigorous clinical validation of AI algorithms.
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Bibliography:
- Marletta S, Ferri N, Gentile L, D’Amati A, Giordano G, Rossi R. Application of digital imaging and artificial intelligence to pathology of the placenta. Pediatr Dev Pathol. 2023;26(1):5-12. DOI: 10.1177/10935266221137953.
- Hutchinson JC, Picarsic J, McGenity C, Treanor D, Williams B, Sebire NJ. Whole slide imaging, artificial intelligence, and ma- chine learning in pediatric and perinatal pathology: current status and future directions. Pediatr Dev Pathol. 2025;28(2):91-98. DOI:10.1177/10935266241299073.
- d’Amati A, Baldini GM, Difonzo T, Dellino M, Cerbone M, Caruso G. Artificial intelligence in placental pathology: new diagnostic imaging tools in evolution and in perspective. J Imaging. 2025;11(4):110. DOI: 10.3390/jimaging11 040110.
- Vanea C, Džigurski J, Rukins V, Dodi O, Siigur S, Marom EM, et al. Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY. Nat Commun. 2024;15:2710. DOI: 10.1038/s41467-024-46986-2.
- Jung YM, Park S, Ahn Y, Kim H, Kim EN, Hye EP, et al. Identification of preeclamptic placenta in whole slide images using artificial intel- ligence placenta analysis. J Korean Med Sci. 2024;39:e271. DOI: 10.3346/jkms.2024.39.e271.
- Tertyshnyk DYu, Prokopiuk OS, Prokopiuk VYu, Lazurenko VV, Borzenkova IV, Chernyak OL. Morphological features of placenta in placen- tal dysfunction associated with diabetes mellitus. Ukr J Med Biol Sport. 2022;7(1):79-85. DOI: 10.26693/jmbs07.01.079.
- Stanek J. Placental pathology in the era of digital pathology. Placenta. 2021;104:56-63.
- Salafia CM, Charles AK, Maas EM. Placental structure in fetal growth restriction. Placenta. 2019;84:20-26.
- Rajaraman S, Antani SK. Deep learning in histopathology: challenges and future directions. Med Image Anal. 2020;65:101786. DOI:10.1016/j.media.2020. 101786.
- Chen PHC, Gadepalli K, MacDonald R, Liu Y, Knaflic TN, Korfiatis P, et al. An overview of deep learning in medical imaging. Nat Biomed Eng. 2019;3:753-772. DOI: 10.1038/s41551-019-0449-0.
Publication of the article:
«Bulletin of problems biology and medicine», 2025 Issue 4, 179, addition, 46-47 pages, index UDC 611.013.85:004.89
