Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective DOI Creative Commons
Antonio d’Amati, Giorgio Maria Baldini,

Tommaso Difonzo

et al.

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(4), P. 110 - 110

Published: April 3, 2025

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 summarize recent developments AI applications tailored specifically histopathology. Current AI-driven approaches include advanced digital image analysis, three-dimensional reconstruction, deep learning models such GestAltNet for precise gestational age estimation automated identification histological lesions, including decidual vasculopathy maternal vascular malperfusion. Despite these advancements, significant challenges remain, notably dataset heterogeneity, interpretative limitations current algorithms, issues regarding model transparency. We critically address by proposing targeted solutions, augmenting training datasets with annotated artifacts, promoting explainable methods, enhancing cross-institutional collaborations. Finally, we outline future research directions, emphasizing the refinement algorithms routine clinical integration fostering interdisciplinary cooperation among pathologists, computational researchers, specialists.

Language: Английский

Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective DOI Creative Commons
Antonio d’Amati, Giorgio Maria Baldini,

Tommaso Difonzo

et al.

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(4), P. 110 - 110

Published: April 3, 2025

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 summarize recent developments AI applications tailored specifically histopathology. Current AI-driven approaches include advanced digital image analysis, three-dimensional reconstruction, deep learning models such GestAltNet for precise gestational age estimation automated identification histological lesions, including decidual vasculopathy maternal vascular malperfusion. Despite these advancements, significant challenges remain, notably dataset heterogeneity, interpretative limitations current algorithms, issues regarding model transparency. We critically address by proposing targeted solutions, augmenting training datasets with annotated artifacts, promoting explainable methods, enhancing cross-institutional collaborations. Finally, we outline future research directions, emphasizing the refinement algorithms routine clinical integration fostering interdisciplinary cooperation among pathologists, computational researchers, specialists.

Language: Английский

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