Effect of Specimen Processing Technique on Cell Detection and Classification by Artificial Intelligence. Issue 5 (18th March 2023)

• Record Type: Journal Article
• Authors: Maruyama, Sayumi; Sakabe, Nanako; Ito, Chihiro; Shimoyama, Yuka; Sato, Shouichi; Ikeda, Katsuhide
• Is Part Of: American journal of clinical pathology. Volume 159:Issue 5(2023)
• Page Start: 448
• Page End: 454
• Publication Date: 2023-03-18
• Subjects: Cytopathology -- Artificial intelligence -- Deep learning -- Cell detection -- Cell classification; Diagnosis, Laboratory -- Periodicals; Pathology -- Periodicals; 616.07
• DOI: 10.1093/ajcp/aqac178
• Languages: English
• ISSNs: 0002-9173

Abstract

Abstract: Objectives: Cytomorphology is known to differ depending on the processing technique, and these differences pose a problem for automated diagnosis using deep learning. We examined the as-yet unclarified relationship between cell detection or classification using artificial intelligence (AI) and the AutoSmear (Sakura Finetek Japan) and liquid-based cytology (LBC) processing techniques. Methods: The "You Only Look Once" (YOLO), version 5x, algorithm was trained on the AutoSmear and LBC preparations of 4 cell lines: lung cancer (LC), cervical cancer (CC), malignant pleural mesothelioma (MM), and esophageal cancer (EC). Detection and classification rates were used to evaluate the accuracy of cell detection. Results: When preparations of the same processing technique were used for training and detection in the 1-cell (1C) model, the AutoSmear model had a higher detection rate than the LBC model. When different processing techniques were used for training and detection, detection rates of LC and CC were significantly lower in the 4-cell (4C) model than in the 1C model, and those of MM and EC were approximately 10% lower in the 4C model. Conclusions: In AI-based cell detection and classification, attention should be paid to cells whose morphologies change significantly depending on the processing technique, further suggesting the creation of a training model.