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

Haeyon Cho; Damin Moon; So Mi Heo; Jinah Chu; Hyunsik Bae; Sangjoon Choi; Yubin Lee; Dongmin Kim; Yeonju Jo; Kyuyoung Kim; Kyungmin Hwang; Dakeun Lee; Heung Kook Choi; Seokhwi Kim

doi:10.1038/s41698-024-00621-x

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

Haeyon Cho
, Damin Moon
, So Mi Heo
, Jinah Chu
, Hyunsik Bae
, Sangjoon Choi
, Yubin Lee
, Dongmin Kim
, Yeonju Jo
, Kyuyoung Kim
, Kyungmin Hwang
, Dakeun Lee
, Heung Kook Choi
, Seokhwi Kim

University of Ulsan
JLK Inc.
Ajou University
Kangbuk Samsung Hospital
Seegene Medical Foundation
VPIX Medical Inc.

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

There 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.

Original language	English
Article number	131
Journal	npj Precision Oncology
Volume	8
Issue number	1
DOIs	https://doi.org/10.1038/s41698-024-00621-x
State	Published - Dec 2024
Externally published	Yes

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This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1038/s41698-024-00621-x

Cite this

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title = "Artificial intelligence-based real-time histopathology of gastric cancer using confocal laser endomicroscopy",

abstract = "There 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{\textquoteright} interpretation.",

author = "Haeyon Cho and Damin Moon and Heo, \{So Mi\} and Jinah Chu and Hyunsik Bae and Sangjoon Choi and Yubin Lee and Dongmin Kim and Yeonju Jo and Kyuyoung Kim and Kyungmin Hwang and Dakeun Lee and Choi, \{Heung Kook\} and Seokhwi Kim",

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doi = "10.1038/s41698-024-00621-x",

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T1 - Artificial intelligence-based real-time histopathology of gastric cancer using confocal laser endomicroscopy

AU - Cho, Haeyon

AU - Moon, Damin

AU - Heo, So Mi

AU - Chu, Jinah

AU - Bae, Hyunsik

AU - Choi, Sangjoon

AU - Lee, Yubin

AU - Kim, Dongmin

AU - Jo, Yeonju

AU - Kim, Kyuyoung

AU - Hwang, Kyungmin

AU - Lee, Dakeun

AU - Choi, Heung Kook

AU - Kim, Seokhwi

PY - 2024/12

Y1 - 2024/12

N2 - There 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.

AB - There 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.

UR - https://www.scopus.com/pages/publications/85195899182

U2 - 10.1038/s41698-024-00621-x

DO - 10.1038/s41698-024-00621-x

M3 - Article

AN - SCOPUS:85195899182

SN - 2397-768X

VL - 8

JO - npj Precision Oncology

JF - npj Precision Oncology

IS - 1

M1 - 131

ER -

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

Abstract

UN SDGs

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