RRC ID 83120
著者 Ikeda K, Sakabe N, Ito C, Shimoyama Y, Toda K, Fukuda K, Yoshizaki Y, Sato S, Nagata K.
タイトル Staining, magnification, and algorithmic conditions for highly accurate cell detection and cell classification by deep learning.
ジャーナル Am J Clin Pathol
Abstract OBJECTIVES:Research into cytodiagnosis has seen an active exploration of cell detection and classification using deep learning models. We aimed to clarify the challenges of magnification, staining methods, and false positives in creating general purpose deep learning-based cytology models.
METHODS:Using 11 types of human cancer cell lines, we prepared Papanicolaou- and May-Grünwald-Giemsa (MGG)-stained specimens. We created deep learning models with different cell types, staining, and magnifications from each cell image using the You Only Look Once, version 8 (YOLOv8) algorithm. Detection and classification rates were calculated to compare the models.
RESULTS:The classification rates of all the created models were over 95.9%. The highest detection rates of the Papanicolaou and MGG models were 92.3% and 91.3%, respectively. The highest detection rates of the object detection and instance segmentation models, which were 11 cell types with Papanicolaou staining, were 94.6% and 91.7%, respectively.
CONCLUSIONS:We believe that the artificial intelligence technology of YOLOv8 has sufficient performance for applications in screening and cell classification in clinical settings. Conducting research to demonstrate the efficacy of YOLOv8 artificial intelligence technology on clinical specimens is crucial for overcoming the unique challenges associated with cytology.
巻・号 161(4)
ページ 399-410
公開日 2024-4-3
DOI 10.1093/ajcp/aqad162
PII 7491579
PMID 38134350
MeSH Artificial Intelligence Cytodiagnosis / methods Deep Learning* Humans Neoplasms* / diagnosis Staining and Labeling
IF 2.094
リソース情報
ヒト・動物細胞 A549(RCB0098) ACC-MESO-1(RCB2292) HeLa HMMME(RCB0819) K562 MCF7(RCB1904) MKN45(RCB1001) OVK18(RCB1903)