Medizinische Universität Graz - Research portal
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Merle, DA; Heidinger, A; Horwath-Winter, J; List, W; Bauer, H; Weissensteiner, M; Kraus-Füreder, P; Mayrhofer-Reinhartshuber, M; Kainz, P; Steinwender, G; Wedrich, A.
Automated Measurement and Three-Dimensional Fitting of Corneal Ulcerations and Erosions via AI-Based Image Analysis.
Curr Eye Res. 2024; 1-8.
Doi: 10.1080/02713683.2024.2344197
Web of Science
PubMed
FullText
FullText_MUG
- Leading authors Med Uni Graz
- Merle David Adrian
- Co-authors Med Uni Graz
- Bauer Heimo
- Heidinger Astrid
- Horwath-Winter Jutta
- List Wolfgang Michael
- Steinwender Gernot
- Wedrich Andreas
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- Abstract:
- 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.
- Find related publications in this database (Keywords)
- Cornea
- artificial intelligence
- ulceration
- erosion
- ocular surface