{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T09:04:31Z","timestamp":1784192671563,"version":"3.55.0"},"reference-count":42,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Biophotonics"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    The objective of the study was to demonstrate the modified Bragg\u2010Williams type analysis of digital microscopic images is an analytically valid methodology for classifying squamous cervical cytology. We analyzed 5318 single cell cervical cytology images from the SIPaKMeD and Herlev databases to calculate a Bragg\u2010Williams order parameter (S\n                    <jats:sup>2<\/jats:sup>\n                    ) using a pixel intensity histogram\u2010based approach. A classification model for S\n                    <jats:sup>2<\/jats:sup>\n                    was created to differentiate normal, LSIL and HSIL. A set of test images from different databases were blindly classified using defined ranges to determine the sensitivity, specificity, and areas under the curve. Results showed distinct ranges of S\n                    <jats:sup>2<\/jats:sup>\n                    for Normal: 0.7181\u20130.8633 SD 0.1452, LSIL: 0.580\u20130.6626 SD 0.0826, and HSIL: 0.3289\u20130.550 SD 0.2211, with dysplastic cells exhibiting lower degrees of order. The S\n                    <jats:sup>2<\/jats:sup>\n                    classification model was found to have 100% sensitivity and specificity over the sample set, demonstrating the suitability of the methodology for cervical cytology classification.\n                  <\/jats:p>","DOI":"10.1002\/jbio.70294","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T08:16:22Z","timestamp":1780042582000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diagnostic Accuracy of Entropy Based Image Analysis of Cervical Precancerous Cells"],"prefix":"10.1002","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7990-7059","authenticated-orcid":false,"given":"Jennifer","family":"Makin","sequence":"first","affiliation":[{"name":"Department of Obstetrics, Gynecology and Reproductive Sciences Magee\u2010Womens Research Institute, University of Pittsburgh School of Medicine  Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esther","family":"Elishaev","sequence":"additional","affiliation":[{"name":"Department of Pathology Magee\u2010Womens Hospital, University of Pittsburgh Medical Center  Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3021-3122","authenticated-orcid":false,"given":"Steven M.","family":"Durbin","sequence":"additional","affiliation":[{"name":"College of Engineering University of Hawai\u02bbi at M\u0101noa  Honolulu Hawaii USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert A.","family":"Makin","sequence":"additional","affiliation":[{"name":"Department of Computer Science Western Michigan University  Kalamazoo Michigan USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.851367"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnma.2020.03.002"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2016-012753"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1136\/ijgc-2020-001285"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/14737159.2023.2203816"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ogc.2007.09.004"},{"key":"e_1_2_11_8_1","volume-title":"Diagnostic Principles and Clinical Correlates","author":"Cibas E. 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