Introduction. Glaucoma and non-arteritic anterior ischemic optic neuropathy (NAION) are optic neuropathies that can both lead to irreversible blindness. Several studies have compared optical coherence tomography angiography (OCTA) findings in glaucoma and NAION in the presence of similar functional and structural damages with contradictory results. The goal of this study was to use a deep learning system to differentiate OCTA in glaucoma and NAION. Material and methods. Sixty eyes with glaucoma (including primary open angle glaucoma, angle-closure glaucoma, normal tension glaucoma, pigmentary glaucoma, pseudoexfoliative glaucoma and juvenile glaucoma), thirty eyes with atrophic NAION and forty control eyes (NC) were included. All patients underwent OCTA imaging and automatic segmentation was used to analyze the macular superficial capillary plexus (SCP) and the radial peripapillary capillary (RPC) plexus. We used the classic convolutional neural network (CNN) architecture of ResNet50. Attribution maps were obtained using the “Integrated Gradients” method. Results. The best performances were obtained with the SCP + RPC model achieving a mean area under the receiver operating characteristics curve (ROC AUC) of 0.94 (95% CI 0.92–0.96) for glaucoma, 0.90 (95% CI 0.86–0.94) for NAION and 0.96 (95% CI 0.96–0.97) for NC. Conclusion. This study shows that deep learning architecture can classify NAION, glaucoma and normal OCTA images with a good diagnostic performance and may outperform the specialist assessment.
Cet article s'intéresse à la classification automatique de cellules de l'épithélium pavimenteux pour le depistage cancer du col de l'utérus en s'appuyant sur les outils de l'apprentissage profond. Nous étudions differentes architectures sur un jeu de données public nommé Herlev qui consiste à classifier des images de cellules, issues d'un frottis du col de l'utérus, au regard de l'anormalité qu'elles représentent. De plus, nous utilisons et adaptons une méthode d'attribution afin de mettre en lumière les caratéristiques cytomorphologiques discriminantes qui sont utilisées pour la classification. A travers cet article, nous détaillerons les methodes et architectures qui nous permettent d'atteindre des performances optimisées: 74.
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