{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T02:10:14Z","timestamp":1777169414570,"version":"3.51.4"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T00:00:00Z","timestamp":1639008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82101183 81770955"],"award-info":[{"award-number":["82101183 81770955"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioeng. Biotechnol."],"abstract":"<jats:p><jats:bold>Purpose:<\/jats:bold> The purpose of this study was to establish a novel bilateral differential topographic algorithm and assess its efficacy for screening of keratoconus and corneal ectasia before corneal refractive surgery.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> One hundred and sixty-one consecutive patients (115 men and 46 women, aged 22.8 \u00b1 6.8\u00a0years) with keratoconus, including clinical keratoconus, subclinical keratoconus, forme fruste keratoconus (FFK), and corneal ectasia (KC group) and one hundred and seventy-four consecutive patients (97 men and 77 women, aged 25.1 \u00b1 6.7\u00a0years) with ametropia (control group) visiting the Eye and ENT hospital of Fudan University from June 2018 to April 2021 were included. Bilateral differential keratometry, elevation, and pachymetry topographies were composed based on raw topographic data obtained by a Scheimpflug imaging anterior segment analyzer. Key bilateral differential characteristic parameters were calculated. SPSS 20 (SPSS Inc., IBM) was used for statistical analyses and the receiver operating characteristic (ROC) curves were used to determine the diagnostic efficacies.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> Mann-Whitney tests detected that the front keratometry, front elevation, corneal pachymetry, and back elevation maximal, mean, and standard deviation values within a 1.5-mm radius of the bilateral differential topography were all significantly higher in the KC group than in the control group (all <jats:italic>p<\/jats:italic>-values &amp;lt;0.001). The front keratometry mean (\u0394FKmean) and standard deviation (\u0394FKsd) and the front elevation standard deviation (\u0394FEsd) and maximal (\u0394FEmax) values within a 1.5-mm radius of the bilateral differential topography yielded the four highest accuracies (area under the ROC curve = 0.985, 0.985, 0.984, and 0.983, respectively) for discriminating KC cases (including FFK cases) from normal cases. Cut-off values of 0.75 diopters (D) for the \u0394FKmean, 0.67 D for the \u0394FKsd, 2.9\u00a0\u03bcm for the \u0394FEsd, and 14.6\u00a0\u03bcm for the \u0394FEmax had the highest sensitivities (95.7, 95.0, 96.9, and 95.0%, respectively) and specificities (96.0, 97.7, 94.8, and 95.4%, respectively).<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> Bilateral differential topographic parameters may be efficient for the early detection of keratoconus and corneal ectasia secondary to corneal refractive surgery. This bilateral differential topographic algorithm may complement conventional diagnostic models by improving the sensitivity and specificity of screening for early keratoconus and ectasia before corneal refractive surgeries.<\/jats:p>","DOI":"10.3389\/fbioe.2021.772982","type":"journal-article","created":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T11:19:20Z","timestamp":1639048760000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Bilateral Differential Topography\u2014A Novel Topographic Algorithm for Keratoconus and Ectatic Disease Screening"],"prefix":"10.3389","volume":"9","author":[{"given":"Yang","family":"Shen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiyong","family":"Xian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuanqi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingtao","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2021,12,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1167\/tvst.9.13.30","article-title":"Classification of Color-Coded Scheimpflug Camera Corneal Tomography Images Using Deep Learning","volume":"9","author":"Abdelmotaal","year":"2020","journal-title":"Trans. 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