Application of Digital Imaging and Artificial Intelligence to Pathology of the Placenta. (January 2023)

• Record Type: Journal Article
• Authors: Marletta, Stefano; Pantanowitz, Liron; Santonicco, Nicola; Caputo, Alessandro; Bragantini, Emma; Brunelli, Matteo; Girolami, Ilaria; Eccher, Albino
• Is Part Of: Pediatric and developmental pathology. Volume 26:Number 1(2023)
• Page Start: 5
• Page End: 12
• Publication Date: 2023-01
• Subjects: 3D-imaging -- digital pathology -- placenta -- systematic review -- whole slide imaging; Pediatric pathology -- Periodicals; Children -- Diseases -- Periodicals; Diagnosis, Laboratory -- Periodicals; Abnormalities, Human -- Periodicals; Child development -- Periodicals; Pediatrics -- Periodicals; 616.07
• DOI: 10.1177/10935266221137953
• Languages: English
• ISSNs: 1093-5266

Abstract

Digital imaging, including the use of artificial intelligence, has been increasingly applied to investigate the placenta and its related pathology. However, there has been no comprehensive review of this body of work to date. The aim of this study was to therefore review the literature regarding digital pathology of the placenta. A systematic literature search was conducted in several electronic databases. Studies involving the application of digital imaging and artificial intelligence techniques to human placental samples were retrieved and analyzed. Relevant articles were categorized by digital image technique and their relevance to studying normal and diseased placenta. Of 2008 retrieved articles, 279 were included. Digital imaging research related to the placenta was often coupled with immunohistochemistry, confocal microscopy, 3D reconstruction, and/or deep learning algorithms. By significantly increasing pathologists' ability to recognize potentially prognostic relevant features and by lessening inter-observer variability, published data overall indicate that the application of digital pathology to placental and perinatal diseases, along with clinical and radiology correlation, has great potential to improve fetal and maternal health care including the selection of targeted therapy in high-risk pregnancy.