Objective To explore the feasibility of artificial intelligence (AI)-assisted pathological diagnosis on lung adenocarcinoma cells by using deep learning model.

Methods From March 2019 to December 2021, 110 specimens of lung adenocarcinoma were collected from the Affiliated Hospital of Nantong University, the Chest Hospital of Shanghai Jiao Tong University and Zhongshan Hospital, Fudan University and 20 specimens of non-cancerous pleural effusion as the control group. Two methods of cell separation (routine technology and new treatment technology with single cell separation solution) were used. Then making liquid based thin layer cell slides, H-E staining, digital scanning of whole slide image (WSI) and saving as digital files. Afterwards AI-assisted diagnosis was performed. After cutting and image pretreatment, LabelImg software was used to label pleural fluid cells, box and note cell types. Typical cell samples were selected to label lymphocytes, mesothelial cells and adenocarcinoma cells respectively. A total of 800 images were labeled for training. And then machine learning, suspected and confirmed lung adenocarcinoma cells were trained with Yolo V4 model, cells of different classifications were trained with Inception V3 model, and another 250 images were taken for testing.

Results The trained Yolo V4 model could identify suspected + confirmed lung cancer cells in HE staining smears of pleural fluid cells (mAP 20%). The trained Inception V3 model can classify lymphatic, mesothelial, and suspected + confirmed lung adenocarcinoma cells segmented by a single cell in the pathological images from exfoliated cells of pleural fluid with an accuracy of 98%. Single cell separation solution can increase the number of cancer cells that could be clearly labeled, and increase the efficiency and accuracy of single cell target recognition.

Conclusion Based on deep learning method, the AI model can detect and classify lung adenocarcinoma cells in exfoliated pleural fluid cells, and can be used to assist the pathologic diagnosis of lung cancer. Improving the efficiency of cell separation and unified standardized preparation are helpful to promote clinical application.