{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:38:58Z","timestamp":1787027938209,"version":"build-2736575974"},"reference-count":36,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100020027","name":"Dr. Rolf M. Schwiete Stiftung","doi-asserted-by":"publisher","award":["2020-024"],"award-info":[{"award-number":["2020-024"]}],"id":[{"id":"10.13039\/501100020027","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Keratoconus (KC) is the most common corneal ectasia. This condition affects quality of vision, especially when it is progressive, and a timely and stage-related treatment is mandatory. Therefore, early diagnosis is crucial to preserve visual acuity. Medical data may be used either in their raw state or in a preprocessed form. Software modifications introduced through updates may potentially affect outcomes. Unlike preprocessed data, raw data preserve their original format across software versions and provide a more consistent basis for clinical analysis. The objective of this study was to distinguish between healthy and KC corneas from raw optical coherence tomography data by using a convolutional neural network. Methods: In total, 2737 eye examinations acquired with the Casia2 anterior-segment optical coherence tomography (Tomey, Nagoya, Japan) were decided by three experienced ophthalmologists to belong to one of three classes: \u2018normal\u2019, \u2018ectasia\u2019, or \u2018other disease\u2019. Each eye examination consisted of sixteen meridional slice images. The dataset included 744 examinations. DenseNet121, EfficientNet-B0, MobileNetV3-Large and ResNet18 were modified for use as convolutional neural networks for prediction. All reported metric values were rounded to four decimal places. Results: The overall accuracy for the modified DenseNet121, modified EfficientNet-B0, modified MobileNetV3-Large and modified ResNet18 is 91.27%, 91.27%, 92.86% and 89.68%, respectively. The macro-averaged sensitivity, macro-averaged specificity, macro-averaged Positive Predictive Value and macro-averaged F1 score for the modified DenseNet121, modified EfficientNet-B0, modified MobileNetV3-Large and modified ResNet18 are reported as 91.27%, 91.27%, 92.86% and 89.68%; 95.63%, 95.63%, 96.43% and 94.84%; 91.58% 91.65%, 92.91% and 90.24%; and 91.35%, 91.29%, 92.85% and 89.81%, respectively. Conclusions: The successful use of a convolutional neural network with raw optical coherence tomography data demonstrates the potential of raw data to be used instead of preprocessed data for diagnosing KC in ophthalmology.<\/jats:p>","DOI":"10.3390\/diagnostics15243115","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T10:35:51Z","timestamp":1765190151000},"page":"3115","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Learning-Based Diagnosis of Corneal Condition by Using Raw Optical Coherence Tomography Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0328-0411","authenticated-orcid":false,"given":"Maziar","family":"Mirsalehi","sequence":"first","affiliation":[{"name":"Department of Experimental Ophthalmology, Saarland University, Kirrberger Street 100, 66424 Homburg, Saarland, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Schwemm","sequence":"additional","affiliation":[{"name":"Department of Experimental Ophthalmology, Saarland University, Kirrberger Street 100, 66424 Homburg, Saarland, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0423-3624","authenticated-orcid":false,"given":"Elias","family":"Flockerzi","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Saarland University Medical Centre UKS, Kirrberger Street 100, 66424 Homburg, Saarland, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8019-1481","authenticated-orcid":false,"given":"N\u00f3ra","family":"Szentm\u00e1ry","sequence":"additional","affiliation":[{"name":"Department of Experimental Ophthalmology, Saarland University, Kirrberger Street 100, 66424 Homburg, Saarland, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8190-7277","authenticated-orcid":false,"given":"Alaa Din","family":"Abdin","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Saarland University Medical Centre UKS, Kirrberger Street 100, 66424 Homburg, Saarland, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9701-8204","authenticated-orcid":false,"given":"Berthold","family":"Seitz","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Saarland University Medical Centre UKS, Kirrberger Street 100, 66424 Homburg, Saarland, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2263-4023","authenticated-orcid":false,"given":"Achim","family":"Langenbucher","sequence":"additional","affiliation":[{"name":"Department of Experimental Ophthalmology, Saarland University, Kirrberger Street 100, 66424 Homburg, Saarland, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pang, G., Wang, C., Wang, X., Li, X., and Meng, Q. 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