Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective
Artificial intelligence (AI) has emerged as a transformative tool in placental pathology, offering novel diagnostic methods that promise to improve accuracy, reduce inter-observer variability, and positively impact pregnancy outcomes. The primary objective of this review is to summarize recent devel...
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MDPI AG
2025-04-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/4/110 |
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| author | Antonio d’Amati Giorgio Maria Baldini Tommaso Difonzo Angela Santoro Miriam Dellino Gerardo Cazzato Antonio Malvasi Antonella Vimercati Leonardo Resta Gian Franco Zannoni Eliano Cascardi |
| author_facet | Antonio d’Amati Giorgio Maria Baldini Tommaso Difonzo Angela Santoro Miriam Dellino Gerardo Cazzato Antonio Malvasi Antonella Vimercati Leonardo Resta Gian Franco Zannoni Eliano Cascardi |
| author_sort | Antonio d’Amati |
| collection | DOAJ |
| description | Artificial intelligence (AI) has emerged as a transformative tool in placental pathology, offering novel diagnostic methods that promise to improve accuracy, reduce inter-observer variability, and positively impact pregnancy outcomes. The primary objective of this review is to summarize recent developments in AI applications tailored specifically to placental histopathology. Current AI-driven approaches include advanced digital image analysis, three-dimensional placental reconstruction, and deep learning models such as GestAltNet for precise gestational age estimation and automated identification of histological lesions, including decidual vasculopathy and maternal vascular malperfusion. Despite these advancements, significant challenges remain, notably dataset heterogeneity, interpretative limitations of current AI algorithms, and issues regarding model transparency. We critically address these limitations by proposing targeted solutions, such as augmenting training datasets with annotated artifacts, promoting explainable AI methods, and enhancing cross-institutional collaborations. Finally, we outline future research directions, emphasizing the refinement of AI algorithms for routine clinical integration and fostering interdisciplinary cooperation among pathologists, computational researchers, and clinical specialists. |
| format | Article |
| id | doaj-art-8b0055a1d4074bf1bdf03d3dd5f18d43 |
| institution | OA Journals |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-8b0055a1d4074bf1bdf03d3dd5f18d432025-08-20T02:17:59ZengMDPI AGJournal of Imaging2313-433X2025-04-0111411010.3390/jimaging11040110Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in PerspectiveAntonio d’Amati0Giorgio Maria Baldini1Tommaso Difonzo2Angela Santoro3Miriam Dellino4Gerardo Cazzato5Antonio Malvasi6Antonella Vimercati7Leonardo Resta8Gian Franco Zannoni9Eliano Cascardi10Pathology Unit, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari, Piazza Giulio Cesare 11, 70124 Bari, Italy1st Unit of Obstetrics and Gynecology, Department of Interdisciplinary Medicine (DIM), University of Bari, 70124 Bari, Italy1st Unit of Obstetrics and Gynecology, Department of Interdisciplinary Medicine (DIM), University of Bari, 70124 Bari, ItalyAnatomic Pathology Unit, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica S. Cuore, 00136 Rome, Italy1st Unit of Obstetrics and Gynecology, Department of Interdisciplinary Medicine (DIM), University of Bari, 70124 Bari, ItalyPathology Unit, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari, Piazza Giulio Cesare 11, 70124 Bari, Italy1st Unit of Obstetrics and Gynecology, Department of Interdisciplinary Medicine (DIM), University of Bari, 70124 Bari, Italy1st Unit of Obstetrics and Gynecology, Department of Interdisciplinary Medicine (DIM), University of Bari, 70124 Bari, ItalyPathology Unit, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari, Piazza Giulio Cesare 11, 70124 Bari, ItalyAnatomic Pathology Unit, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica S. Cuore, 00136 Rome, ItalyPathology Unit, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari, Piazza Giulio Cesare 11, 70124 Bari, ItalyArtificial intelligence (AI) has emerged as a transformative tool in placental pathology, offering novel diagnostic methods that promise to improve accuracy, reduce inter-observer variability, and positively impact pregnancy outcomes. The primary objective of this review is to summarize recent developments in AI applications tailored specifically to placental histopathology. Current AI-driven approaches include advanced digital image analysis, three-dimensional placental reconstruction, and deep learning models such as GestAltNet for precise gestational age estimation and automated identification of histological lesions, including decidual vasculopathy and maternal vascular malperfusion. Despite these advancements, significant challenges remain, notably dataset heterogeneity, interpretative limitations of current AI algorithms, and issues regarding model transparency. We critically address these limitations by proposing targeted solutions, such as augmenting training datasets with annotated artifacts, promoting explainable AI methods, and enhancing cross-institutional collaborations. Finally, we outline future research directions, emphasizing the refinement of AI algorithms for routine clinical integration and fostering interdisciplinary cooperation among pathologists, computational researchers, and clinical specialists.https://www.mdpi.com/2313-433X/11/4/110digital pathologyartificial intelligence (AI)placental histopathologymachine learning (ML)pregnancy |
| spellingShingle | Antonio d’Amati Giorgio Maria Baldini Tommaso Difonzo Angela Santoro Miriam Dellino Gerardo Cazzato Antonio Malvasi Antonella Vimercati Leonardo Resta Gian Franco Zannoni Eliano Cascardi Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective Journal of Imaging digital pathology artificial intelligence (AI) placental histopathology machine learning (ML) pregnancy |
| title | Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective |
| title_full | Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective |
| title_fullStr | Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective |
| title_full_unstemmed | Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective |
| title_short | Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective |
| title_sort | artificial intelligence in placental pathology new diagnostic imaging tools in evolution and in perspective |
| topic | digital pathology artificial intelligence (AI) placental histopathology machine learning (ML) pregnancy |
| url | https://www.mdpi.com/2313-433X/11/4/110 |
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