| Autor: |
Benjamin Wu, Gilbert Moeckel |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Journal of Pathology Informatics, Vol 14, Iss , Pp 100184- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2153-3539 |
| DOI: |
10.1016/j.jpi.2022.100184 |
| Popis: |
The development of rapid and accurate Whole Slide Imaging (WSI) has paved the way for the application of Artificial Intelligence (AI) to digital pathology. The availability of WSI in the recent years allowed the rapid development of various AI technologies to blossom. WSI-based digital pathology combined with neural networks can automate arduous and time-consuming tasks of slide evaluation. Machine Learning (ML)-based AI has been demonstrated to outperform pathologists by eliminating inter- and intra-observer subjectivity, obtaining quantitative data from slide images, and extracting hidden image patterns that are relevant to disease subtype and progression. In this review, we outline the functionality of different AI technologies such as neural networks and deep learning and discover how aspects of different diseases make them benefit from the implementation of AI. AI has proven to be valuable in many different organs, with this review focusing on the liver, kidney, and lungs. We also discuss how AI and image analysis not only can grade diseases objectively but also discover aspects of diseases that have prognostic value. In the end, we review the current status of the integration of AI in pathology and share our vision on the future of digital pathology. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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