PurposeWe provide a standardized set of terminology, definitions, and thresholds of myopia and its main ocular complications.MethodsCritical review of current terminology and choice of myopia thresholds was done to ensure that the proposed standards are appropriate for clinical research purposes, relevant to the underlying biology of myopia, acceptable to researchers in the field, and useful for developing health policy.ResultsWe recommend that the many descriptive terms of myopia be consolidated into the following descriptive categories: myopia, secondary myopia, axial myopia, and refractive myopia. To provide a framework for research into myopia prevention, the condition of “pre-myopia” is defined. As a quantitative trait, we recommend that myopia be divided into myopia (i.e., all myopia), low myopia, and high myopia. The current consensus threshold value for myopia is a spherical equivalent refractive error ≤ −0.50 diopters (D), but this carries significant risks of classification bias. The current consensus threshold value for high myopia is a spherical equivalent refractive error ≤ −6.00 D. “Pathologic myopia” is proposed as the categorical term for the adverse, structural complications of myopia. A clinical classification is proposed to encompass the scope of such structural complications.ConclusionsStandardized definitions and consistent choice of thresholds are essential elements of evidence-based medicine. It is hoped that these proposals, or derivations from them, will facilitate rigorous, evidence-based approaches to the study and management of myopia.

PurposeTo determine the correlation between the perimetric outcomes from perimetry software Melbourne Rapid Fields (MRF) run on an Apple iPad tablet and those from the Humphrey Field Analyzer (HFA).MethodsThe MRF software was designed with features including variable fixation and fast thresholding using Bayes logic. Here, we report a cross-sectional study on 90 eyes from 90 participants: 12 had normal optic nerves and 78 had glaucoma with various degrees of visual field loss (41 mild and 37 moderate-severe). Exclusion criteria were patients with worse than 20/40 vision or recent intraocular surgery. The visual field outcomes of MRF were compared against those returned from the HFA 24-2 SITA standard. Participants were tested twice on the MRF to establish test–retest repeatability.ResultsThe test durations were shorter on MRF than HFA (5.7 ± 0.1 vs. 6.3 ± 0.1 minutes, P < 0.001). MRF showed a high level of concordance in its outcomes with HFA (intraclass coefficient [ICC] = 0.93 for mean defect [MD] and 0.86 for pattern deviation [PD]) although the MRF tended to give a less negative MD (1.4 dB bias) compared with the HFA. MRF also showed levels of test–retest reliability comparable to HFA (ICC = 0.93 for MD and 0.89 for PD, 95% limits of agreement −4.5 to 4.3 dB).ConclusionThe perimetry results from the MRF have a strong correlation to the HFA outcomes. MRF also has test–retest reliability comparable to HFA.Translational RelevancePortable tablet perimetry may allow accurate assessment of visual field when standard perimetry machines are unavailable or unsuitable.

The purpose of this study is to evaluate the feasibility and patient acceptability of a novel artificial intelligence (AI)-based diabetic retinopathy (DR) screening model within endocrinology outpatient settings. Adults with diabetes were recruited from two urban endocrinology outpatient clinics and single-field, non-mydriatic fundus photographs were taken and graded for referable DR ( ≥ pre-proliferative DR). Each participant underwent; (1) automated screening model; where a deep learning algorithm (DLA) provided real-time reporting of results; and (2) manual model where retinal images were transferred to a retinal grading centre and manual grading outcomes were distributed to the patient within 2 weeks of assessment. Participants completed a questionnaire on the day of examination and 1-month following assessment to determine overall satisfaction and the preferred model of care. In total, 96 participants were screened for DR and the mean assessment time for automated screening was 6.9 minutes. Ninety-six percent of participants reported that they were either satisfied or very satisfied with the automated screening model and 78% reported that they preferred the automated model over manual. The sensitivity and specificity of the DLA for correct referral was 92.3% and 93.7%, respectively. AI-based DR screening in endocrinology outpatient settings appears to be feasible and well accepted by patients.