Accelerating pharmaceutical R&D with a user-friendly AI system for histopathology image analysis
A system for analysis of histopathology data within a pharmaceutical R&D environment has been developed with the intention of enabling interdisciplinary collaboration. State-of-the-art AI tools have been deployed as easy-to-use self-service modules within an open-source whole slide image viewing...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-01-01
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| Series: | Journal of Pathology Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353923001517 |
| _version_ | 1797660310547464192 |
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| author | Brendon Lutnick Albert Juan Ramon Brandon Ginley Carlos Csiszer Alex Kim Io Flament Pablo F. Damasceno Jonathan Cornibe Chaitanya Parmar Kristopher Standish Oscar Carrasco-Zevallos Stephen S.F. Yip |
| author_facet | Brendon Lutnick Albert Juan Ramon Brandon Ginley Carlos Csiszer Alex Kim Io Flament Pablo F. Damasceno Jonathan Cornibe Chaitanya Parmar Kristopher Standish Oscar Carrasco-Zevallos Stephen S.F. Yip |
| author_sort | Brendon Lutnick |
| collection | DOAJ |
| description | A system for analysis of histopathology data within a pharmaceutical R&D environment has been developed with the intention of enabling interdisciplinary collaboration. State-of-the-art AI tools have been deployed as easy-to-use self-service modules within an open-source whole slide image viewing platform, so that non-data scientist users (e.g., clinicians) can utilize and evaluate pre-trained algorithms and retrieve quantitative results. The outputs of analysis are automatically cataloged in the database to track data provenance and can be viewed interactively on the slide as annotations or heatmaps. Commonly used models for analysis of whole slide images including segmentation, extraction of hand-engineered features for segmented regions, and slide-level classification using multi-instance learning are included and new models can be added as needed. The source code that supports running inference with these models internally is backed up by a robust CI/CD pipeline to ensure model versioning, robust testing, and seamless deployment of the latest models. Examples of the use of this system in a pharmaceutical development workflow include glomeruli segmentation, enumeration of podocyte count from WT-1 immuno-histochemistry, measurement of beta-1 integrin target engagement from immunofluorescence, digital glomerular phenotyping from periodic acid-Schiff histology, PD-L1 score prediction using multi-instance learning, and the deployment of the open-source Segment Anything model to speed up annotation. |
| first_indexed | 2024-03-11T18:28:57Z |
| format | Article |
| id | doaj.art-730c1ce279ab4c97b36a7f19717de57e |
| institution | Directory Open Access Journal |
| issn | 2153-3539 |
| language | English |
| last_indexed | 2024-03-11T18:28:57Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj.art-730c1ce279ab4c97b36a7f19717de57e2023-10-13T13:53:21ZengElsevierJournal of Pathology Informatics2153-35392023-01-0114100337Accelerating pharmaceutical R&D with a user-friendly AI system for histopathology image analysisBrendon Lutnick0Albert Juan Ramon1Brandon Ginley2Carlos Csiszer3Alex Kim4Io Flament5Pablo F. Damasceno6Jonathan Cornibe7Chaitanya Parmar8Kristopher Standish9Oscar Carrasco-Zevallos10Stephen S.F. Yip11Corresponding author.; Janssen R&D, Data Sciences, Raritan, NJ 08869, USAJanssen R&D, Data Sciences, Raritan, NJ 08869, USAJanssen R&D, Data Sciences, Raritan, NJ 08869, USAJanssen R&D, Data Sciences, Raritan, NJ 08869, USAJanssen R&D, Data Sciences, Raritan, NJ 08869, USAJanssen R&D, Data Sciences, Raritan, NJ 08869, USAJanssen R&D, Data Sciences, Raritan, NJ 08869, USAJanssen R&D, Data Sciences, Raritan, NJ 08869, USAJanssen R&D, Data Sciences, Raritan, NJ 08869, USAJanssen R&D, Data Sciences, Raritan, NJ 08869, USAJanssen R&D, Data Sciences, Raritan, NJ 08869, USAJanssen R&D, Data Sciences, Raritan, NJ 08869, USAA system for analysis of histopathology data within a pharmaceutical R&D environment has been developed with the intention of enabling interdisciplinary collaboration. State-of-the-art AI tools have been deployed as easy-to-use self-service modules within an open-source whole slide image viewing platform, so that non-data scientist users (e.g., clinicians) can utilize and evaluate pre-trained algorithms and retrieve quantitative results. The outputs of analysis are automatically cataloged in the database to track data provenance and can be viewed interactively on the slide as annotations or heatmaps. Commonly used models for analysis of whole slide images including segmentation, extraction of hand-engineered features for segmented regions, and slide-level classification using multi-instance learning are included and new models can be added as needed. The source code that supports running inference with these models internally is backed up by a robust CI/CD pipeline to ensure model versioning, robust testing, and seamless deployment of the latest models. Examples of the use of this system in a pharmaceutical development workflow include glomeruli segmentation, enumeration of podocyte count from WT-1 immuno-histochemistry, measurement of beta-1 integrin target engagement from immunofluorescence, digital glomerular phenotyping from periodic acid-Schiff histology, PD-L1 score prediction using multi-instance learning, and the deployment of the open-source Segment Anything model to speed up annotation.http://www.sciencedirect.com/science/article/pii/S2153353923001517VisualizationAnnotationModel catalogingSegmentationSegment Anything |
| spellingShingle | Brendon Lutnick Albert Juan Ramon Brandon Ginley Carlos Csiszer Alex Kim Io Flament Pablo F. Damasceno Jonathan Cornibe Chaitanya Parmar Kristopher Standish Oscar Carrasco-Zevallos Stephen S.F. Yip Accelerating pharmaceutical R&D with a user-friendly AI system for histopathology image analysis Journal of Pathology Informatics Visualization Annotation Model cataloging Segmentation Segment Anything |
| title | Accelerating pharmaceutical R&D with a user-friendly AI system for histopathology image analysis |
| title_full | Accelerating pharmaceutical R&D with a user-friendly AI system for histopathology image analysis |
| title_fullStr | Accelerating pharmaceutical R&D with a user-friendly AI system for histopathology image analysis |
| title_full_unstemmed | Accelerating pharmaceutical R&D with a user-friendly AI system for histopathology image analysis |
| title_short | Accelerating pharmaceutical R&D with a user-friendly AI system for histopathology image analysis |
| title_sort | accelerating pharmaceutical r d with a user friendly ai system for histopathology image analysis |
| topic | Visualization Annotation Model cataloging Segmentation Segment Anything |
| url | http://www.sciencedirect.com/science/article/pii/S2153353923001517 |
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