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PURPOSE: Automated machine learning (AutoML) allows clinicians without coding experience to build their own deep learning (DL) models. This study assesses the performance of AutoML in diagnosing trachoma from field-collected conjunctival images and compares it to expert-designed DL models. METHODS: Two ophthalmology trainees without coding experience carried out AutoML model design using a publicly available image data set of field-collected conjunctival images (1656 labeled images). We designed two binary models to differentiate trachomatous inflammation-follicular (TF) and trachomatous inflammation-intense (TI) from normal. We then integrated an Edge model into an Android application using Google Firebase to make offline diagnoses. RESULTS: The AutoML models showed high diagnostic properties in the classification tasks that were comparable or better than the bespoke DL models. The TF model had an area under the precision-recall curve (AuPRC) of 0.945, sensitivity of 87%, specificity of 88%, and accuracy of 88%. The TI model had an AuPRC of 0.975, sensitivity of 95%, specificity of 92%, and accuracy of 93%. Through the Android app and using an external dataset, the AutoML model had an AuPRC of 0.875, sensitivity of 83%, specificity of 81%, and accuracy of 83%. CONCLUSION: AutoML models created by ophthalmologists without coding experience were comparable or better than bespoke models trained on the same dataset. Using AutoML to create models and edge computing to deploy them into smartphone-based apps, our approach brings the whole spectrum of DL model design into the hands of clinicians. This approach has the potential to democratize access to artificial intelligence. [ABSTRACT FROM AUTHOR]

Aims Automated machine learning (AutoML) is a novel tool in artificial intelligence (AI). This study assessed the discriminative performance of AutoML in differentiating retinal vein occlusion (RVO), retinitis pigmentosa (RP) and retinal detachment (RD) from normal fundi using ultra-widefield (UWF) pseudocolour fundus images. Methods Two ophthalmologists without coding experience carried out AutoML model design using a publicly available image data set (2137 labelled images). The data set was reviewed for low-quality and mislabeled images and then uploaded to the Google Cloud AutoML Vision platform for training and testing. We designed multiple binary models to differentiate RVO, RP and RD from normal fundi and compared them to bespoke models obtained from the literature. We then devised a multiclass model to detect RVO, RP and RD. Saliency maps were generated to assess the interpretability of the model. Results The AutoML models demonstrated high diagnostic properties in the binary classification tasks that were generally comparable to bespoke deeplearning models (area under the precision-recall curve (AUPRC) 0.921-1, sensitivity 84.91%-89.77%, specificity 78.72%-100%). The multiclass AutoML model had an AUPRC of 0.876, a sensitivity of 77.93% and a positive predictive value of 82.59%. The per-label sensitivity and specificity, respectively, were normal fundi (91.49%, 86.75%), RVO (83.02%, 92.50%), RP (72.00%, 100%) and RD (79.55%,96.80%). Conclusion AutoML models created by ophthalmologists without coding experience can detect RVO, RP and RD in UWF images with very good diagnostic accuracy. The performance was comparable to bespoke deep-learning models derived by AI experts for RVO and RP but not for RD. [ABSTRACT FROM AUTHOR]

Background/rationale Artificial intelligence (AI)-based clinical decision support tools, being developed across multiple fields in medicine, need to be evaluated for their impact on the treatment and outcomes of patients as well as optimisation of the clinical workflow. The RAZORBILL study will investigate the impact of advanced AI segmentation algorithms on the disease activity assessment in patients with neovascular agerelated macular degeneration (nAMD) by enriching three-dimensional (3D) retinal optical coherence tomography (OCT) scans with automated fluid and layer quantification measurements. Methods RAZORBILL is an observational, multicentre, multinational, open-label study, comprising two phases: (a) clinical data collection (phase I): an observational study design, which enforces neither strict visit schedule nor mandated treatment regimen was chosen as an appropriate design to collect data in a real-world clinical setting to enable evaluation in phase II and (b) OCT enrichment analysis (phase II): de-identified 3D OCT scans will be evaluated for disease activity. Within this evaluation, investigators will review the scans once enriched with segmentation results (i.e., highlighted and quantified pathological fluid volumes) and once in its original (i.e., non-enriched) state. This review will be performed using an integrated crossover design, where investigators are used as their own controls allowing the analysis to account for differences in expertise and individual disease activity definitions. Conclusions In order to apply novel AI tools to routine clinical care, their benefit as well as operational feasibility need to be carefully investigated. RAZORBILL will inform on the value of AI-based clinical decision support tools. It will clarify if these can be implemented in clinical treatment of patients with nAMD and whether it allows for optimisation of individualised treatment in routine clinical care. [ABSTRACT FROM AUTHOR]

Background/aims To evaluate the non-invasive measurement of ocular rigidity (OR), an important biomechanical property of the eye, as a predictor of intraocular pressure (IOP) elevation after anti-vascular endothelial growth factor (anti-VEGF) intravitreal injection (IVI). Methods Subjects requiring IVI of anti-VEGF for a pre-existing retinal condition were enrolled in this prospective cross-sectional study. OR was assessed in 18 eyes of 18 participants by measurement of pulsatile choroidal volume change using video-rate optical coherence tomography, and pulsatile IOP change using dynamic contour tonometry. IOP was measured using Tono-Pen XL before and immediately following the injection and was correlated with OR. Results The average increase in IOP following IVI was 19±9 mm Hg, with a range of 7-33 mm Hg. The Spearman correlation coefficient between OR and IOP elevation following IVI was 0.796 (p<0.001), showing higher IOP elevation in more rigid eyes. A regression line was also calculated to predict the IOP spike based on the OR coefficient, such that IOP spike=664.17 mm Hg·μL×OR + 4.59 mm Hg. Conclusion This study shows a strong positive correlation between OR and acute IOP elevation following IVI. These findings indicate that the non-invasive measurement of OR could be an effective tool in identifying patients at risk of IOP spikes following IVI. [ABSTRACT FROM AUTHOR]

The article describes the case of a 54-year-old Caucasian man who was referred for distorted vision in the left eye for the previous 10 days and has a remote history of central serous chorioretinopathy (CSC) in the left eye 20 years earlier. It cites the results of the optical coherence tomography (OCT) of the right eye. It also discusses how genetic testing revealed a predisposition for age-related macular degeneration (AMD)-related choroidal neovascularization (CNV) in the left eye.