دورية أكاديمية
Clinically applicable deep learning-based decision aids for treatment of neovascular AMD
| العنوان: | Clinically applicable deep learning-based decision aids for treatment of neovascular AMD |
|---|---|
| المؤلفون: | Gutfleisch, Matthias, Ester, Oliver, Aydin, Soekmen, Quassowski, Martin, Spital, Georg, Lommatzsch, Albrecht, Rothaus, Kai, Dubis, Adam Michael, Pauleikhoff, Daniel |
| المصدر: | Graefes Archive for Clinical and Experimental Ophthalmology , 260 (7) pp. 2217-2230. (2022) |
| بيانات النشر: | Springer Verlag |
| سنة النشر: | 2022 |
| المجموعة: | University College London: UCL Discovery |
| مصطلحات موضوعية: | Science & Technology, Life Sciences & Biomedicine, Ophthalmology, Neovascular age-related macular degeneration (nAMD), Anti-VEGF therapy, Artificial intelligence, Deep learning network, Convolutional neural network, Treatment algorithms, MACULAR DEGENERATION, PREDICTION, RANIBIZUMAB |
| الوصف: | Purpose: Anti-vascular endothelial growth factor (Anti-VEGF) therapy is currently seen as the standard for treatment of neovascular AMD (nAMD). However, while treatments are highly effective, decisions for initial treatment and retreatment are often challenging for non-retina specialists. The purpose of this study is to develop convolutional neural networks (CNN) that can differentiate treatment indicated presentations of nAMD for referral to treatment centre based solely on SD-OCT. This provides the basis for developing an applicable medical decision support system subsequently. Methods: SD-OCT volumes of a consecutive real-life cohort of 1503 nAMD patients were analysed and two experiments were carried out. To differentiate between no treatment class vs. initial treatment nAMD class and stabilised nAMD vs. active nAMD, two novel CNNs, based on SD-OCT volume scans, were developed and tested for robustness and performance. In a step towards explainable artificial intelligence (AI), saliency maps of the SD-OCT volume scans of 24 initial indication decisions with a predicted probability of > 97.5% were analysed (score 0–2 in respect to staining intensity). An AI benchmark against retina specialists was performed. Results: At the first experiment, the area under curve (AUC) of the receiver-operating characteristic (ROC) for the differentiation of patients for the initial analysis was 0.927 (standard deviation (SD): 0.018), for the second experiment (retreatment analysis) 0.865 (SD: 0.027). The results were robust to downsampling (¼ of the original resolution) and cross-validation (tenfold). In addition, there was a high correlation between the AI analysis and expert opinion in a sample of 102 cases for differentiation of patients needing treatment (κ = 0.824). On saliency maps, the relevant structures for individual initial indication decisions were the retina/vitreous interface, subretinal space, intraretinal cysts, subretinal pigment epithelium space, and the choroid. Conclusion: The developed AI algorithms ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | text |
| اللغة: | English |
| العلاقة: | https://discovery.ucl.ac.uk/id/eprint/10159596/1/Gutfleischetal_Accepted.pdfTest; https://discovery.ucl.ac.uk/id/eprint/10159596Test/ |
| الإتاحة: | https://discovery.ucl.ac.uk/id/eprint/10159596/1/Gutfleischetal_Accepted.pdfTest https://discovery.ucl.ac.uk/id/eprint/10159596Test/ |
| حقوق: | open |
| رقم الانضمام: | edsbas.1E037863 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |