AI-based Clinical Assessment of Optic Nerve Head Robustness Superseding Biomechanical Testing

• URL: http://arxiv.org/abs/2206.04689v1
• Date: Thu, 9 Jun 2022 11:29:28 GMT
• Title: AI-based Clinical Assessment of Optic Nerve Head Robustness Superseding Biomechanical Testing
• Authors: Fabian A. Braeu, Thanadet Chuangsuwanich, Tin A. Tun, Alexandre H. Thiery, Tin Aung, George Barbastathis, Micha\"el J.A. Girard
• Abstract summary: We propose an AI-driven approach that can assess the robustness of a given ONH solely from a single OCT scan of the ONH. Longitudinal studies should establish whether ONH robustness could help us identify fast visual field loss progressors.
• Score: 54.306443917863355
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: $\mathbf{Purpose}$: To use artificial intelligence (AI) to: (1) exploit biomechanical knowledge of the optic nerve head (ONH) from a relatively large population; (2) assess ONH robustness from a single optical coherence tomography (OCT) scan of the ONH; (3) identify what critical three-dimensional (3D) structural features make a given ONH robust. $\mathbf{Design}$: Retrospective cross-sectional study. $\mathbf{Methods}$: 316 subjects had their ONHs imaged with OCT before and after acute intraocular pressure (IOP) elevation through ophthalmo-dynamometry. IOP-induced lamina-cribrosa deformations were then mapped in 3D and used to classify ONHs. Those with LC deformations superior to 4% were considered fragile, while those with deformations inferior to 4% robust. Learning from these data, we compared three AI algorithms to predict ONH robustness strictly from a baseline (undeformed) OCT volume: (1) a random forest classifier; (2) an autoencoder; and (3) a dynamic graph CNN (DGCNN). The latter algorithm also allowed us to identify what critical 3D structural features make a given ONH robust. $\mathbf{Results}$: All 3 methods were able to predict ONH robustness from 3D structural information alone and without the need to perform biomechanical testing. The DGCNN (area under the receiver operating curve [AUC]: 0.76 $\pm$ 0.08) outperformed the autoencoder (AUC: 0.70 $\pm$ 0.07) and the random forest classifier (AUC: 0.69 $\pm$ 0.05). Interestingly, to assess ONH robustness, the DGCNN mainly used information from the scleral canal and the LC insertion sites. $\mathbf{Conclusions}$: We propose an AI-driven approach that can assess the robustness of a given ONH solely from a single OCT scan of the ONH, and without the need to perform biomechanical testing. Longitudinal studies should establish whether ONH robustness could help us identify fast visual field loss progressors.

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