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

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. Cancer Res; 78(23); 6703-7. Ó2018 AACR.

Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity

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Label-free detection of rare circulating tumor cells by image analysis and machine learning

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“…Thus, in this work, we aim to develop an automatic tool for accurate detection of CTCs as a promising step for diagnosis and clinical management of cancer patients. Machine learning (ML) has become a superior tool for developing automated processes of classification, sorting, and detection [34][35][36][37] . ML algorithms build a mathematical or statistical model based on sample "training data" with known "ground truth" annotations, to make inference or predictions.…”

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“…Our work is aimed at developing a fast and accurate technique for locating and counting tumor cells among the mixed cells after enrichment from whole blood. For state-of-the-art works using deep learning methods 36,37 , the model accuracy can be 85-95%, given a large amount of data with distinct differences between the candidate images. In this study, because the CTCs are rare cells from patients' blood samples, the data size is relatively small.…”