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Artificial intelligence-based real-time histopathology of gastric cancer using confocal laser endomicroscopy

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dc.contributor.authorCho, H-
dc.contributor.authorMoon, D-
dc.contributor.authorHeo, SM-
dc.contributor.authorChu, J-
dc.contributor.authorBae, H-
dc.contributor.authorChoi, S-
dc.contributor.authorLee, Y-
dc.contributor.authorKim, D-
dc.contributor.authorJo, Y-
dc.contributor.authorKim, K-
dc.contributor.authorHwang, K-
dc.contributor.authorLee, D-
dc.contributor.authorChoi, HK-
dc.contributor.authorKim, S-
dc.date.accessioned2024-07-10T03:11:25Z-
dc.date.available2024-07-10T03:11:25Z-
dc.date.issued2024-
dc.identifier.urihttp://repository.ajou.ac.kr/handle/201003/32666-
dc.description.abstractThere has been a persistent demand for an innovative modality in real-time histologic imaging, distinct from the conventional frozen section technique. We developed an artificial intelligence-driven real-time evaluation model for gastric cancer tissue using confocal laser endomicroscopic system. The remarkable performance of the model suggests its potential utilization as a standalone modality for instantaneous histologic assessment and as a complementary tool for pathologists’ interpretation.-
dc.language.isoen-
dc.titleArtificial intelligence-based real-time histopathology of gastric cancer using confocal laser endomicroscopy-
dc.typeArticle-
dc.identifier.pmid38877301-
dc.identifier.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11178780-
dc.contributor.affiliatedAuthorLee, D-
dc.contributor.affiliatedAuthorKim, S-
dc.type.localJournal Papers-
dc.identifier.doi10.1038/s41698-024-00621-x-
dc.citation.titleNPJ precision oncology-
dc.citation.volume8-
dc.citation.number1-
dc.citation.date2024-
dc.citation.startPage131-
dc.citation.endPage131-
dc.identifier.bibliographicCitationNPJ precision oncology, 8(1). : 131-131, 2024-
dc.identifier.eissn2397-768X-
dc.relation.journalidJ02397768X-
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Pathology
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