{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T08:43:20Z","timestamp":1775810600603,"version":"3.50.1"},"reference-count":18,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Vox Sanguinis"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Objectives<\/jats:title>\n                    <jats:p>Plasma quality control (QC) in transfusion medicine relies on subjective visual inspections, which vary depending on the technologist's experience and lighting conditions, potentially compromising safety. We developed an automated system to standardize the detection of plasma colour and turbidity abnormalities, and aimed to evaluate its accuracy and reliability in a transfusion setting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>At the Blood Transfusion Center, Khon Kaen University, Thailand, three experienced technologists classified 973 plasma bags. The system was trained on 789 bags (467 normal, 322 abnormal) and tested on 184 bags (145 normal, 39 abnormal) in Phase 1 and 486 bags (287 normal, 199 abnormal) in Phase 2. A sorting machine captured images under controlled lighting, and a deep learning model assessed the quality. Performance was measured using accuracy, sensitivity, specificity and precision, with 95% confidence intervals (CIs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The system achieved 87.5% accuracy (95% CI: 82.7%\u201391.4%) in Phase 1 and 94.7% accuracy (95% CI: 92.3%\u201396.3%) in Phase 2, with 100% sensitivity (95% CI: 98.1%\u2013100%) and 90.9% specificity (95% CI: 87.1%\u201393.7%) in Phase 2, ensuring no abnormal units were missed. Cross\u2010validation on the training set yielded 94.8% accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This automated QC system offers a reliable alternative to manual inspection, minimizing errors and reducing wastage (26\u2009units vs. 29\u201386 with manual methods). Its high sensitivity and integration with a sorting machine support its potential to standardize plasma QC, enhancing patient safety and blood bank efficiency.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/vox.70122","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:40:32Z","timestamp":1759192832000},"page":"474-480","source":"Crossref","is-referenced-by-count":3,"title":["Beyond the human eye: Artificial intelligence revolutionizing plasma quality control"],"prefix":"10.1111","volume":"121","author":[{"given":"Kriangsak","family":"Jenwitheesuk","sequence":"first","affiliation":[{"name":"Blood Transfusion Center, Faculty of Medicine Khon Kaen University  Khon Kaen Thailand"},{"name":"Surgery Department, Faculty of Medicine Khon Kaen University  Khon Kaen Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Poonsup","family":"Sripara","sequence":"additional","affiliation":[{"name":"Blood Transfusion Center, Faculty of Medicine Khon Kaen University  Khon Kaen Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Komsan","family":"Sayan","sequence":"additional","affiliation":[{"name":"Office of Digital Technology, Khon Kaen University  Khon Kaen Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanut","family":"Padee","sequence":"additional","affiliation":[{"name":"Office of Digital Technology, Khon Kaen University  Khon Kaen Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anupong","family":"Tita","sequence":"additional","affiliation":[{"name":"Computer Technical Officer of Kaedam Hospital  Kaedam Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2220-7742","authenticated-orcid":false,"given":"Ronnarit","family":"Boonyarat","sequence":"additional","affiliation":[{"name":"Blood Transfusion Center, Faculty of Medicine Khon Kaen University  Khon Kaen Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.transci.2014.01.011"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.4103\/ajts.ajts_97_21"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.4103\/ajts.AJTS_34_17"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.4103\/ajts.AJTS_117_18"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.transci.2021.103294"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.4103\/GJTM.GJTM_63_19"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1128\/CMR.7.3.290"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1515\/cclm-2017-0532"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12020510"},{"key":"e_1_2_9_11_1","first-page":"1","article-title":"Comparison between visual inspection of turbidity and quantitative determination of triglycerides in plasma bags","volume":"58","author":"Hauser AB","year":"2022","journal-title":"J Bras Patol Med Lab"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1814259"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2023.101207"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01106-8"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14161808"},{"key":"e_1_2_9_16_1","first-page":"1096","article-title":"Revolutionizing healthcare advances in AI\u2010driven medical imaging for precision diagnosis","volume":"13","author":"Ganaie SA","year":"2024","journal-title":"Int J Sci 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