{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T20:16:38Z","timestamp":1790885798940,"version":"4.1.0"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2023,3,18]],"date-time":"2023-03-18T00:00:00Z","timestamp":1679097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001691","name":"JSPS","doi-asserted-by":"publisher","award":["JP21K18077"],"award-info":[{"award-number":["JP21K18077"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>The \u201cYou Only Look Once\u201d (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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ajcp\/aqac178","type":"journal-article","created":{"date-parts":[[2023,3,18]],"date-time":"2023-03-18T17:16:57Z","timestamp":1679159817000},"page":"448-454","source":"Crossref","is-referenced-by-count":9,"title":["Effect of Specimen Processing Technique on Cell Detection and Classification by Artificial Intelligence"],"prefix":"10.1093","volume":"159","author":[{"given":"Sayumi","family":"Maruyama","sequence":"first","affiliation":[{"name":"Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine , Nagoya , Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nanako","family":"Sakabe","sequence":"additional","affiliation":[{"name":"Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine , Nagoya , Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chihiro","family":"Ito","sequence":"additional","affiliation":[{"name":"Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine , Nagoya , Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuka","family":"Shimoyama","sequence":"additional","affiliation":[{"name":"Pathophysiology Sciences, Department of Integrated 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