Application of digital pathology and machine learning in the liver, kidney and lung diseases
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 com...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Journal of Pathology Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353922007842 |
| _version_ | 1797946229625192448 |
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| author | Benjamin Wu Gilbert Moeckel |
| author_facet | Benjamin Wu Gilbert Moeckel |
| author_sort | Benjamin Wu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T21:07:42Z |
| format | Article |
| id | doaj.art-62828a3b3e5d4286b2c962b61a3a0445 |
| institution | Directory Open Access Journal |
| issn | 2153-3539 |
| language | English |
| last_indexed | 2024-04-10T21:07:42Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj.art-62828a3b3e5d4286b2c962b61a3a04452023-01-22T04:38:59ZengElsevierJournal of Pathology Informatics2153-35392023-01-0114100184Application of digital pathology and machine learning in the liver, kidney and lung diseasesBenjamin Wu0Gilbert Moeckel1Horace Mann School, Bronx, NY, USA; Corresponding author at: 950 Post Rd., Scarsdale, NY 10583, USA.Department of Pathology, Yale University School of Medicine, New Haven, CT, USAThe 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.http://www.sciencedirect.com/science/article/pii/S2153353922007842Digital pathologyMachine learning (ML)Whole slide imaging (WSI)LiverKidneyLung |
| spellingShingle | Benjamin Wu Gilbert Moeckel Application of digital pathology and machine learning in the liver, kidney and lung diseases Journal of Pathology Informatics Digital pathology Machine learning (ML) Whole slide imaging (WSI) Liver Kidney Lung |
| title | Application of digital pathology and machine learning in the liver, kidney and lung diseases |
| title_full | Application of digital pathology and machine learning in the liver, kidney and lung diseases |
| title_fullStr | Application of digital pathology and machine learning in the liver, kidney and lung diseases |
| title_full_unstemmed | Application of digital pathology and machine learning in the liver, kidney and lung diseases |
| title_short | Application of digital pathology and machine learning in the liver, kidney and lung diseases |
| title_sort | application of digital pathology and machine learning in the liver kidney and lung diseases |
| topic | Digital pathology Machine learning (ML) Whole slide imaging (WSI) Liver Kidney Lung |
| url | http://www.sciencedirect.com/science/article/pii/S2153353922007842 |
| work_keys_str_mv | AT benjaminwu applicationofdigitalpathologyandmachinelearningintheliverkidneyandlungdiseases AT gilbertmoeckel applicationofdigitalpathologyandmachinelearningintheliverkidneyandlungdiseases |