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

BACKGROUND/AIMS: 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 (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. METHODS: 316 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. RESULTS: All 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. CONCLUSIONS: We 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. PRECIS: Using 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.

A decision-support tool for funding health innovations at a tertiary academic medical center.

OBJECTIVES: To report the processes used to design and implement an assessment tool to inform funding decisions for competing health innovations in a tertiary hospital. METHODS: We designed an assessment tool for health innovation proposals with three components: "value to the institution," "novelty," and "potential for adoption and scaling." The "value to the institution" component consisted of twelve weighted value attributes identified from the host institution's annual report; weights were allocated based on a survey of the hospital's leaders. The second and third components consisted of open-ended questions on "novelty" and "barriers to implementation" to support further dialogue. Purposive literature review was performed independently by two researchers for each assessment. The assessment tool was piloted during an institutional health innovation funding cycle. RESULTS: We used 17 days to evaluate ten proposals. The completed assessments were shared with an independent group of panellists, who selected five projects for funding. Proposals with the lowest scores for "value to the institution" had less perceived impact on the patient-related value attributes of "access," "patient centeredness," "health outcomes," "prevention," and "safety." Similar innovations were reported in literature in seven proposals; potential barriers to implementation were identified in six proposals. We included a worked example to illustrate the assessment process. CONCLUSIONS: We developed an assessment tool that is aligned with local institutional priorities. Our tool can augment the decision-making process when funding health innovation projects. The tool can be adapted by others facing similar challenges of trying to choose the best health innovations to fund.

Longitudinal assessment of optic nerve head changes using optical coherence tomography in a primate microbead model of ocular hypertension.

In humans, the longitudinal characterisation of early optic nerve head (ONH) damage in ocular hypertension (OHT) is difficult as patients with glaucoma usually have structural ONH damage at the time of diagnosis. Previous studies assessed glaucomatous ONH cupping by measuring the anterior lamina cribrosa depth (LCD) and minimal rim width (MRW) using optical coherence tomography (OCT). In this study, we induced OHT by repeated intracameral microbead injections in 16 cynomolgus primates (10 unilateral; 6 bilateral) and assessed the structural changes of the ONH longitudinally to observe early changes. Elevated intraocular pressure (IOP) in OHT eyes was maintained for 7 months and serial OCT measurements were performed during this period. The mean IOP was significantly elevated in OHT eyes when compared to baseline and compared to the control eyes. Thinner MRW and deeper LCD values from baseline were observed in OHT eyes with the greatest changes seen between month 1 and month 2 of OHT. Both the mean and maximum IOP values were significant predictors of MRW and LCD changes, although the maximum IOP was a slightly better predictor. We believe that this model could be useful to study IOP-induced early ONH structural damage which is important for understanding glaucoma pathogenesis.

Clinical Assessment of Lamina Cribrosa Curvature in Eyes with Primary Open-Angle Glaucoma.

PURPOSE: Quantitative evaluation of lamina cribrosa (LC) posterior bowing in primary open-angle glaucoma (POAG) eyes using swept-source optical coherence tomography. METHODS: Patients with POAG (n = 123 eyes) and healthy individuals of a similar age (n = 92 eyes) were prospectively recruited. Anterior laminar insertion depth (ALID) was defined as the vertical distance between the anterior laminar insertion and a reference plane connecting the Bruch's membrane openings (BMO). The mean LC depth (mLCD) was approximated by dividing the area enclosed by the anterior LC, the BMO reference plane, and the two vertical lines for ALID measurement by the length between those two vertical lines. The LC curvature index was defined as the difference between the mLCD and the ALID. The factors influencing the LC curvature index were evaluated. RESULTS: The ALID and mLCD were significantly larger in POAG eyes than in healthy controls (P < 0.05). The LC curvature index was significantly larger in POAG eyes than in healthy controls on both the horizontal (85.8 ± 34.1 vs. 68.2 ± 32.3 µm) and vertical meridians (49.8 ± 38.5 vs. 32.2 ± 31.1 µm, all P < 0.001). Multivariate regression showed significant associations of greater disc area (P < 0.001), vertical C/D ratio (P < 0.001) and mLCD (P < 0.001), smaller rim area (P = 0.001), thinner average RNFLT (P < 0.001), and myopic refraction (P = 0.049) with increased LC curvature index. There was no difference in the LC curvature index between mild (MD > -6 dB) and moderate-to-advanced glaucoma (MD < -6 dB, P = 0.95). CONCLUSIONS: LC posterior bowing was increased in POAG eyes, and was significantly associated with structural optic nerve head (ONH) changes but not with functional glaucoma severity. Quantitative evaluation of LC curvature can facilitate assessment of glaucomatous ONH change.