{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T14:52:59Z","timestamp":1790175179613,"version":"4.1.0"},"reference-count":30,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T00:00:00Z","timestamp":1709596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["2021R1A2C2013630"],"award-info":[{"award-number":["2021R1A2C2013630"]}]},{"name":"National Research Foundation of Korea (NRF)","award":["00-2022-0034"],"award-info":[{"award-number":["00-2022-0034"]}]},{"name":"Seoul National University Bundang Hospital (SNUBH) Research Fund","award":["2021R1A2C2013630"],"award-info":[{"award-number":["2021R1A2C2013630"]}]},{"name":"Seoul National University Bundang Hospital (SNUBH) Research Fund","award":["00-2022-0034"],"award-info":[{"award-number":["00-2022-0034"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Ascites cytology is a cost-effective test for metastatic colorectal cancer (CRC) in the abdominal cavity. However, metastatic carcinoma of the peritoneum is difficult to diagnose based on biopsy findings, and ascitic aspiration cytology has a low sensitivity and specificity and a high inter-observer variability. The aim of the present study was to apply artificial intelligence (AI) to classify benign and malignant cells in ascites cytology patch images of metastatic CRC using a deep convolutional neural network. Datasets were collected from The OPEN AI Dataset Project, a nationwide cytology dataset for AI research. The numbers of patch images used for training, validation, and testing were 56,560, 7068, and 6534, respectively. We evaluated 1041 patch images of benign and metastatic CRC in the ascitic fluid to compare the performance of pathologists and an AI algorithm, and to examine whether the diagnostic accuracy of pathologists improved with the assistance of AI. This AI method showed an accuracy, a sensitivity, and a specificity of 93.74%, 87.76%, and 99.75%, respectively, for the differential diagnosis of malignant and benign ascites. The diagnostic accuracy and sensitivity of the pathologist with the assistance of the proposed AI method increased from 86.8% to 90.5% and from 73.3% to 79.3%, respectively. The proposed deep learning method may assist pathologists with different levels of experience in diagnosing metastatic CRC cells of ascites.<\/jats:p>","DOI":"10.3390\/cancers16051064","type":"journal-article","created":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T11:26:59Z","timestamp":1709638019000},"page":"1064","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Artificial-Intelligence-Assisted Detection of Metastatic Colorectal Cancer Cells in Ascitic Fluid"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0271-2493","authenticated-orcid":false,"given":"Hyung Kyung","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Pathology, Seoul National University Bundang Hospital, Seongnam 13620, Republic of Korea"},{"name":"Department of Pathology, Samsung Medical Center, Seoul 06351, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eunkyung","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Pathology, Soonchunyang University Hospital Bucheon, Bucheon 14584, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeonghyo","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Pathology, Seoul National University Bundang Hospital, Seongnam 13620, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8767-9033","authenticated-orcid":false,"given":"Kwangil","family":"Yim","sequence":"additional","affiliation":[{"name":"Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6575-8870","authenticated-orcid":false,"given":"Jamshid","family":"Abdul-Ghafar","sequence":"additional","affiliation":[{"name":"Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1908-9696","authenticated-orcid":false,"given":"Kyung Jin","family":"Seo","sequence":"additional","affiliation":[{"name":"Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jang Won","family":"Seo","sequence":"additional","affiliation":[{"name":"AI Team, MTS Company Inc., Seoul 06178, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gyungyub","family":"Gong","sequence":"additional","affiliation":[{"name":"Department of Pathology, Asan Medical Center, Seoul 05505, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nam Hoon","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Pathology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milim","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Pathology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5221-4516","authenticated-orcid":false,"given":"Chong Woo","family":"Yoo","sequence":"additional","affiliation":[{"name":"Department of Pathology, National Cancer Center, Goyang 10408, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8615-3064","authenticated-orcid":false,"given":"Yosep","family":"Chong","sequence":"additional","affiliation":[{"name":"Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"650098","DOI":"10.3389\/fonc.2021.650098","article-title":"Peritoneal metastases from colorectal cancer: Defining and addressing the challenges","volume":"11","author":"Kranenburg","year":"2021","journal-title":"Front. 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