AI as a Medical Device for Ophthalmic Imaging in Europe, Australia, and the United States:Protocol for a Systematic Scoping Review of Regulated Devices

BACKGROUND: Artificial intelligence as a medical device (AIaMD) has the potential to transform many aspects of ophthalmic care, such as improving accuracy and speed of diagnosis, addressing capacity issues in high-volume areas such as screening, and detecting novel biomarkers of systemic disease in the eye (oculomics). In order to ensure that such tools are safe for the target population and achieve their intended purpose, it is important that these AIaMD have adequate clinical evaluation to support any regulatory decision. Currently, the evidential requirements for regulatory approval are less clear for AIaMD compared to more established interventions such as drugs or medical devices. There is therefore value in understanding the level of evidence that underpins AIaMD currently on the market, as a step toward identifying what the best practices might be in this area. In this systematic scoping review, we will focus on AIaMD that contributes to clinical decision-making (relating to screening, diagnosis, prognosis, and treatment) in the context of ophthalmic imaging.OBJECTIVE: This study aims to identify regulator-approved AIaMD for ophthalmic imaging in Europe, Australia, and the United States; report the characteristics of these devices and their regulatory approvals; and report the available evidence underpinning these AIaMD.METHODS: The Food and Drug Administration (United States), the Australian Register of Therapeutic Goods (Australia), the Medicines and Healthcare products Regulatory Agency (United Kingdom), and the European Database on Medical Devices (European Union) regulatory databases will be searched for ophthalmic imaging AIaMD through a snowballing approach. PubMed and clinical trial registries will be systematically searched, and manufacturers will be directly contacted for studies investigating the effectiveness of eligible AIaMD. Preliminary regulatory database searches, evidence searches, screening, data extraction, and methodological quality assessment will be undertaken by 2 independent review authors and arbitrated by a third at each stage of the process.RESULTS: Preliminary searches were conducted in February 2023. Data extraction, data synthesis, and assessment of methodological quality commenced in October 2023. The review is on track to be completed and submitted for peer review by April 2024.CONCLUSIONS: This systematic review will provide greater clarity on ophthalmic imaging AIaMD that have achieved regulatory approval as well as the evidence that underpins them. This should help adopters understand the range of tools available and whether they can be safely incorporated into their clinical workflow, and it should also support developers in navigating regulatory approval more efficiently.INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52602.</p

A Deep-Learning Algorithm to Predict Short-Term Progression to Geographic Atrophy on Spectral-Domain Optical Coherence Tomography

IMPORTANCE: The identification of patients at risk of progressing from intermediate age-related macular degeneration (iAMD) to geographic atrophy (GA) is essential for clinical trials aimed at preventing disease progression. DeepGAze is a fully automated and accurate convolutional neural network-based deep learning algorithm for predicting progression from iAMD to GA within 1 year from spectral-domain optical coherence tomography (SD-OCT) scans. OBJECTIVE: To develop a deep-learning algorithm based on volumetric SD-OCT scans to predict the progression from iAMD to GA during the year following the scan. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study included participants with iAMD at baseline and who either progressed or did not progress to GA within the subsequent 13 months. Participants were included from centers in 4 US states. Data set 1 included patients from the Age-Related Eye Disease Study 2 AREDS2 (Ancillary Spectral-Domain Optical Coherence Tomography) A2A study (July 2008 to August 2015). Data sets 2 and 3 included patients with imaging taken in routine clinical care at a tertiary referral center and associated satellites between January 2013 and January 2023. The stored imaging data were retrieved for the purpose of this study from July 1, 2022, to February 1, 2023. Data were analyzed from May 2021 to July 2023. EXPOSURE: A position-aware convolutional neural network with proactive pseudointervention was trained and cross-validated on Bioptigen SD-OCT volumes (data set 1) and validated on 2 external data sets comprising Heidelberg Spectralis SD-OCT scans (data sets 2 and 3). MAIN OUTCOMES AND MEASURES: Prediction of progression to GA within 13 months was evaluated with area under the receiver-operator characteristic curves (AUROC) as well as area under the precision-recall curve (AUPRC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. RESULTS: The study included a total of 417 patients: 316 in data set 1 (mean [SD] age, 74 [8]; 185 [59%] female), 53 in data set 2, (mean [SD] age, 83 [8]; 32 [60%] female), and 48 in data set 3 (mean [SD] age, 81 [8]; 32 [67%] female). The AUROC for prediction of progression from iAMD to GA within 1 year was 0.94 (95% CI, 0.92-0.95; AUPRC, 0.90 [95% CI, 0.85-0.95]; sensitivity, 0.88 [95% CI, 0.84-0.92]; specificity, 0.90 [95% CI, 0.87-0.92]) for data set 1. The addition of expert-annotated SD-OCT features to the model resulted in no improvement compared to the fully autonomous model (AUROC, 0.95; 95% CI, 0.92-0.95; P = .19). On an independent validation data set (data set 2), the model predicted progression to GA with an AUROC of 0.94 (95% CI, 0.91-0.96; AUPRC, 0.92 [0.89-0.94]; sensitivity, 0.91 [95% CI, 0.74-0.98]; specificity, 0.80 [95% CI, 0.63-0.91]). At a high-specificity operating point, simulated clinical trial recruitment was enriched for patients progressing to GA within 1 year by 8.3- to 20.7-fold (data sets 2 and 3). CONCLUSIONS AND RELEVANCE: The fully automated, position-aware deep-learning algorithm assessed in this study successfully predicted progression from iAMD to GA over a clinically meaningful time frame. The ability to predict imminent GA progression could facilitate clinical trials aimed at preventing the condition and could guide clinical decision-making regarding screening frequency or treatment initiation

Gothic Revival Architecture Before Horace Walpole's Strawberry Hill

The Gothic Revival is generally considered to have begun in eighteenth-century Britain with the construction of Horace Walpole’s villa, Strawberry Hill, Twickenham, in the late 1740s. As this chapter demonstrates, however, Strawberry Hill is in no way the first building, domestic or otherwise, to have recreated, even superficially, some aspect of the form and ornamental style of medieval architecture. Earlier architects who, albeit often combining it with Classicism, worked in the Gothic style include Sir Christopher Wren, Nicholas Hawksmoor, William Kent and Batty Langley, aspects of whose works are explored here. While not an exhaustive survey of pre-1750 Gothic Revival design, the examples considered in this chapter reveal how seventeenth- and eighteenth-century Gothic emerged and evolved over the course of different architects’ careers, and how, by the time that Walpole came to create his own Gothic ‘castle’, there was already in existence in Britain a sustained Gothic Revivalist tradition

Synapse Formation in the Zebrafish Lateral Line

Although much is known about how axons and dendrites are guided to a target tissue, little is understood regarding how pre- and postsynaptic partners are matched for synapse formation. The zebrafish lateral line offers the opportunity for greater insight into this process. Hair cells in the lateral-line neuromast exist as two intermingled subpopulations, anteriorly and posteriorly polarized cells. Afferent neurons form synapses with hair cells of only one subpopulation, and this polarity-specific innervation arises independently of synaptic activity. The research presented in my thesis deepens the understanding of synapse formation in the zebrafish lateral line. First, I examine the neuronal architecture of the neuromast at nanometer-scale resolution by imaging the tissue by serial block-face electron microscopy. The data demonstrate that afferent neurons show a polarity preference at the earliest stages of hair-cell innervation, and additionally that the synaptic arrangement appears to arise from interactions among neurons for access to synaptic ribbons rather than being mediated by an accessory cell. I next describe a novel phenomenon, the extension of transient, dynamic projections from the base of nascent hair cells beginning shortly after mitosis. The projections extend toward nearby mature hair-cell synapses and filopodia arising from afferent terminals extend directly along them toward unoccupied synaptic ribbons. Hair-cell projections lacking stable association of afferent neurons are larger than those that are stably innervated. The appearance of hair-cell projections is contemporaneous with the initiation of contact between afferent neurons and nascent hair cells, and the disappearance of projections coincides with the appearance of pre- and postsynaptic markers proteins. I propose a model in which hair-cell projections act as cellular scaffolds for the guidance of neurons to available synaptic sites. Finally, I describe a novel method for collecting subpopulations of cells for gene expression analysis, which I employ to compare the transcriptomes of anteriorly and posteriorly polarized hair cells. I identified a number of differentially expressed candidate genes that might mediate polarity-specific afferent innervation. This work expands the repertoire of tools available for investigations of the neuromast and enhances the understanding of synapse formation in the zebrafish lateral line

Artificial intelligence as a medical device for ophthalmic imaging in Europe, Australia, and the United States: protocol for a systematic scoping review of regulated devices

Protocol for a systematic scoping revie

Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models

While color fundus photos are used in routine clinical practice to diagnose ophthalmic conditions, evidence suggests that ocular imaging contains valuable information regarding the systemic health features of patients. These features can be identified through computer vision techniques including deep learning (DL) artificial intelligence (AI) models. We aim to construct a DL model that can predict systemic features from fundus images and to determine the optimal method of model construction for this task. Data were collected from a cohort of patients undergoing diabetic retinopathy screening between March 2020 and March 2021. Two models were created for each of 12 systemic health features based on the DenseNet201 architecture: one utilizing transfer learning with images from ImageNet and another from 35,126 fundus images. Here, 1277 fundus images were used to train the AI models. Area under the receiver operating characteristics curve (AUROC) scores were used to compare the model performance. Models utilizing the ImageNet transfer learning data were superior to those using retinal images for transfer learning (mean AUROC 0.78 vs. 0.65, p-value &lt; 0.001). Models using ImageNet pretraining were able to predict systemic features including ethnicity (AUROC 0.93), age &gt; 70 (AUROC 0.90), gender (AUROC 0.85), ACE inhibitor (AUROC 0.82), and ARB medication use (AUROC 0.78). We conclude that fundus images contain valuable information about the systemic characteristics of a patient. To optimize DL model performance, we recommend that even domain specific models consider using transfer learning from more generalized image sets to improve accuracy