論文 - 詳細
| RRC ID | 74808 |
|---|---|
| 著者 | Maruyama S, Sakabe N, Ito C, Shimoyama Y, Sato S, Ikeda K. |
| タイトル | Effect of Specimen Processing Technique on Cell Detection and Classification by Artificial Intelligence. |
| ジャーナル | Am J Clin Pathol |
| 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. |
| 公開日 | 2023-3-18 |
| DOI | 10.1093/ajcp/aqac178 |
| PII | 7080508 |
| PMID | 36933198 |
| MeSH | Algorithms Artificial Intelligence* Cytodiagnosis / methods Early Detection of Cancer / methods Female Humans Uterine Cervical Neoplasms* / diagnosis |
| IF | 2.094 |
| リソース情報 | |
| ヒト・動物細胞 | A549(RCB0098) HeLa |