David A Merle, Astrid Heidinger, Jutta Horwath-Winter, Wolfgang List, Heimo Bauer, Michael Weissensteiner, Patrick Kraus-Füreder, Michael Mayrhofer-Reinhartshuber, Philipp Kainz, Gernot Steinwender, Andreas Wedrich
{"title":"通过基于人工智能的图像分析对角膜溃疡和角膜侵蚀进行自动测量和三维拟合。","authors":"David A Merle, Astrid Heidinger, Jutta Horwath-Winter, Wolfgang List, Heimo Bauer, Michael Weissensteiner, Patrick Kraus-Füreder, Michael Mayrhofer-Reinhartshuber, Philipp Kainz, Gernot Steinwender, Andreas Wedrich","doi":"10.1080/02713683.2024.2344197","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Artificial intelligence (AI)-tools hold great potential to compensate for missing resources in health-care systems but often fail to be implemented in clinical routine. Intriguingly, no-code and low-code technologies allow clinicians to develop Artificial intelligence (AI)-tools without requiring in-depth programming knowledge. Clinician-driven projects allow to adequately identify and address real clinical needs and, therefore, hold superior potential for clinical implementation. In this light, this study aimed for the clinician-driven development of a tool capable of measuring corneal lesions relative to total corneal surface area and eliminating inaccuracies in two-dimensional measurements by three-dimensional fitting of the corneal surface.</p><p><strong>Methods: </strong>Standard slit-lamp photographs using a blue-light filter after fluorescein instillation taken during clinical routine were used to train a fully convolutional network to automatically detect the corneal white-to-white distance, the total fluorescent area and the total erosive area. Based on these values, the algorithm calculates the affected area relative to total corneal surface area and fits the area on a three-dimensional representation of the corneal surface.</p><p><strong>Results: </strong>The developed algorithm reached dice scores >0.9 for an automated measurement of the relative lesion size. Furthermore, only 25% of conventional manual measurements were within <i>a</i> ± 10% range of the ground truth.</p><p><strong>Conclusions: </strong>The developed algorithm is capable of reliably providing exact values for corneal lesion sizes. Additionally, three-dimensional modeling of the corneal surface is essential for an accurate measurement of lesion sizes. Besides telemedicine applications, this approach harbors great potential for clinical trials where exact quantitative and observer-independent measurements are essential.</p>","PeriodicalId":10782,"journal":{"name":"Current Eye Research","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automated Measurement and Three-Dimensional Fitting of Corneal Ulcerations and Erosions via AI-Based Image Analysis.\",\"authors\":\"David A Merle, Astrid Heidinger, Jutta Horwath-Winter, Wolfgang List, Heimo Bauer, Michael Weissensteiner, Patrick Kraus-Füreder, Michael Mayrhofer-Reinhartshuber, Philipp Kainz, Gernot Steinwender, Andreas Wedrich\",\"doi\":\"10.1080/02713683.2024.2344197\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Artificial intelligence (AI)-tools hold great potential to compensate for missing resources in health-care systems but often fail to be implemented in clinical routine. Intriguingly, no-code and low-code technologies allow clinicians to develop Artificial intelligence (AI)-tools without requiring in-depth programming knowledge. Clinician-driven projects allow to adequately identify and address real clinical needs and, therefore, hold superior potential for clinical implementation. In this light, this study aimed for the clinician-driven development of a tool capable of measuring corneal lesions relative to total corneal surface area and eliminating inaccuracies in two-dimensional measurements by three-dimensional fitting of the corneal surface.</p><p><strong>Methods: </strong>Standard slit-lamp photographs using a blue-light filter after fluorescein instillation taken during clinical routine were used to train a fully convolutional network to automatically detect the corneal white-to-white distance, the total fluorescent area and the total erosive area. Based on these values, the algorithm calculates the affected area relative to total corneal surface area and fits the area on a three-dimensional representation of the corneal surface.</p><p><strong>Results: </strong>The developed algorithm reached dice scores >0.9 for an automated measurement of the relative lesion size. Furthermore, only 25% of conventional manual measurements were within <i>a</i> ± 10% range of the ground truth.</p><p><strong>Conclusions: </strong>The developed algorithm is capable of reliably providing exact values for corneal lesion sizes. Additionally, three-dimensional modeling of the corneal surface is essential for an accurate measurement of lesion sizes. Besides telemedicine applications, this approach harbors great potential for clinical trials where exact quantitative and observer-independent measurements are essential.</p>\",\"PeriodicalId\":10782,\"journal\":{\"name\":\"Current Eye Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Eye Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/02713683.2024.2344197\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/4/30 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"OPHTHALMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Eye Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/02713683.2024.2344197","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/4/30 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
Automated Measurement and Three-Dimensional Fitting of Corneal Ulcerations and Erosions via AI-Based Image Analysis.
Purpose: Artificial intelligence (AI)-tools hold great potential to compensate for missing resources in health-care systems but often fail to be implemented in clinical routine. Intriguingly, no-code and low-code technologies allow clinicians to develop Artificial intelligence (AI)-tools without requiring in-depth programming knowledge. Clinician-driven projects allow to adequately identify and address real clinical needs and, therefore, hold superior potential for clinical implementation. In this light, this study aimed for the clinician-driven development of a tool capable of measuring corneal lesions relative to total corneal surface area and eliminating inaccuracies in two-dimensional measurements by three-dimensional fitting of the corneal surface.
Methods: Standard slit-lamp photographs using a blue-light filter after fluorescein instillation taken during clinical routine were used to train a fully convolutional network to automatically detect the corneal white-to-white distance, the total fluorescent area and the total erosive area. Based on these values, the algorithm calculates the affected area relative to total corneal surface area and fits the area on a three-dimensional representation of the corneal surface.
Results: The developed algorithm reached dice scores >0.9 for an automated measurement of the relative lesion size. Furthermore, only 25% of conventional manual measurements were within a ± 10% range of the ground truth.
Conclusions: The developed algorithm is capable of reliably providing exact values for corneal lesion sizes. Additionally, three-dimensional modeling of the corneal surface is essential for an accurate measurement of lesion sizes. Besides telemedicine applications, this approach harbors great potential for clinical trials where exact quantitative and observer-independent measurements are essential.
期刊介绍:
The principal aim of Current Eye Research is to provide rapid publication of full papers, short communications and mini-reviews, all high quality. Current Eye Research publishes articles encompassing all the areas of eye research. Subject areas include the following: clinical research, anatomy, physiology, biophysics, biochemistry, pharmacology, developmental biology, microbiology and immunology.