AI-based clinical assessment of optic nerve head robustness superseding biomechanical testing

• Published in: British journal of ophthalmology Vol. 108; no. 2; pp. 223 - 231
• Main Authors: Braeu, Fabian A, Chuangsuwanich, Thanadet, Tun, Tin A, Perera, Shamira, Husain, Rahat, Thiery, Alexandre H, Aung, Tin, Barbastathis, George, Girard, Michaël J A
• Format: Journal Article
• Language: English
• Published: BMA House, Tavistock Square, London, WC1H 9JR BMJ Publishing Group Ltd 01.02.2024; BMJ Publishing Group LTD
• Subjects: Algorithms; Artificial intelligence; Biomechanics; Cerebrospinal fluid; Datasets; Deformation; Ethnicity; Eye movements; Glaucoma; imaging; intraocular pressure; Optic nerve; Optics; Tomography; Germany; Singapore; intraocular pressure; imaging; optic nerve; glaucoma
• ISSN: 0007-1161; 1468-2079; 1468-2079
• DOI: 10.1136/bjo-2022-322374

Background/aimsTo use artificial intelligence (AI) to: (1) exploit biomechanical knowledge of the optic nerve head (ONH) from a relatively large population; (2) assess ONH robustness (ie, sensitivity of the ONH to changes in intraocular pressure (IOP)) from a single optical coherence tomography (OCT) volume scan of the ONH without the need for biomechanical testing and (3) identify what critical three-dimensional (3D) structural features dictate ONH robustness.Methods316 subjects had their ONHs imaged with OCT before and after acute IOP elevation through ophthalmo-dynamometry. IOP-induced lamina cribrosa (LC) deformations were then mapped in 3D and used to classify ONHs. Those with an average effective LC strain superior to 4% were considered fragile, while those with a strain 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 convolutional neural network (DGCNN). The latter algorithm also allowed us to identify what critical 3D structural features make a given ONH robust.ResultsAll three methods were able to predict ONH robustness from a single OCT volume scan alone and without the need to perform biomechanical testing. The DGCNN (area under the curve (AUC): 0.76±0.08) outperformed the autoencoder (AUC: 0.72±0.09) and the random forest classifier (AUC: 0.69±0.05). Interestingly, to assess ONH robustness, the DGCNN mainly used information from the scleral canal and the LC insertion sites.ConclusionsWe propose an AI-driven approach that can assess the robustness of a given ONH solely from a single OCT volume 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.PrecisUsing geometric deep learning, we can assess optic nerve head robustness (ie, sensitivity to a change in IOP) from a standard OCT scan that might help to identify fast visual field loss progressors.