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Data from A Convolutional Neural Network Uses Microscopic Images to Differentiate between Mouse and Human Cell Lines and Their Radioresistant Clones

Authors :
Hideshi Ishii
Kazuhiko Ogawa
Masaki Mori
Yuichiro Doki
Daisuke Okuzaki
Daisuke Motooka
Katsutoshi Sato
Taroh Satoh
Toshihiro Kudo
Daisuke Sakai
Zhihao Li
Keisuke Tamari
Koichi Kawamoto
Jun Koseki
Ayumu Asai
Masamitsu Konno
Masayasu Toratani
Publication Year :
2023
Publisher :
American Association for Cancer Research (AACR), 2023.

Abstract

Artificial intelligence (AI) trained with a convolutional neural network (CNN) is a recent technological advancement. Previously, several attempts have been made to train AI using medical images for clinical applications. However, whether AI can distinguish microscopic images of mammalian cells has remained debatable. This study assesses the accuracy of image recognition techniques using the CNN to identify microscopic images. We also attempted to distinguish between mouse and human cells and their radioresistant clones. We used phase-contrast microscopic images of radioresistant clones from two cell lines, mouse squamous cell carcinoma NR-S1, and human cervical carcinoma ME-180. We obtained 10,000 images of each of the parental NR-S1 and ME-180 controls as well as radioresistant clones. We trained the CNN called VGG16 using these images and obtained an accuracy of 96%. Features extracted by the trained CNN were plotted using t-distributed stochastic neighbor embedding, and images of each cell line were well clustered. Overall, these findings suggest the utility of image recognition using AI for predicting minute differences among phase-contrast microscopic images of cancer cells and their radioresistant clones.Significance:This study demonstrates rapid and accurate identification of radioresistant tumor cells in culture using artifical intelligence; this should have applications in future preclinical cancer research.

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........eda993eec23a49f1af91d9d8c2a25017
Full Text :
https://doi.org/10.1158/0008-5472.c.6510293.v1