Target Product Profile for a Machine Learning-Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study.

BACKGROUND: Diabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation. OBJECTIVE: This study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England. METHODS: This work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence's Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from "definitely exclude" to "definitely include," and suggest edits. The document will be iterated between rounds based on participants' feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote. RESULTS: Phase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024. CONCLUSIONS: The multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50568.

Understanding Ocular Surface Inflammation in Tears Before and After Autologous Cultivated Limbal Epithelial Stem Cell Transplantation.

INTRODUCTION: We aimed to determine the expression of inflammatory cytokines in the tears of patients with unilateral total limbal stem cell deficiency (TLSCD) caused by chemical burns before and after autologous cultivated limbal epithelial stem cell transplantation (CLET). METHODS: Tear samples were collected from both eyes of 23 patients with unilateral TLSCD and 11 healthy controls, at fixed timepoints before and after CLET. Dissolved molecules were extracted from Schirmer's strips using a standardised method and analysed on an array plate of ten inflammatory cytokines (V-Plex Proinflammatory Panel 1 Human Kit, MSD). RESULTS: IL1ß expression was significantly elevated in the TLSCD eye compared with the unaffected eye at baseline (p < 0.0001) but decreased to normal 3 months post-CLET (p = 0.22). IL6 and IL8 were unaffected at baseline but significantly elevated in the TLSCD eyes at 1 month post-CLET (p = 0.001 and p < 0.0001, respectively). IL6 returned to normal at 3 months and IL8 at 6 months post-CLET. There was a significant renewed increase in IL1ß, IL6 and IL8 expression at 12 months post-CLET (p < 0.0001, p = 0.0001 and p = 0.0003, respectively). IFNγ, IL10 and IL12p70 expression were significantly reduced in both eyes of patients with unilateral TLSCD at all timepoints. CONCLUSION: IL1ß is a specific marker of inflammation in TLSCD eyes that could be therapeutically targeted pre-CLET to improve stem cell engraftment. At 12 months post-CLET the spike in levels of IL1ß, IL6 and IL8 coincides with cessation of topical steroids, suggesting ongoing subclinical inflammation. We therefore recommend not discontinuing topical steroid treatment in cases where penetrating keratoplasty is indicated; however, further investigation is needed to ascertain this. TRIAL REGISTRATION: European Union Drug Regulating Authorities Clinical Trials Database (EuDRACT 2011-000608-16); ISRCTN (International Standard Randomised Controlled Trial Number (isrctn51772481).

Synthetic OCT Data Generation to Enhance the Performance of Diagnostic Models for Neurodegenerative Diseases.

Purpose: Optical coherence tomography (OCT) has recently emerged as a source for powerful biomarkers in neurodegenerative diseases such as multiple sclerosis (MS) and neuromyelitis optica (NMO). The application of machine learning techniques to the analysis of OCT data has enabled automatic extraction of information with potential to aid the timely diagnosis of neurodegenerative diseases. These algorithms require large amounts of labeled data, but few such OCT data sets are available now. Methods: To address this challenge, here we propose a synthetic data generation method yielding a tailored augmentation of three-dimensional (3D) OCT data and preserving differences between control and disease data. A 3D active shape model is used to produce synthetic retinal layer boundaries, simulating data from healthy controls (HCs) as well as from patients with MS or NMO. Results: To evaluate the generated data, retinal thickness maps are extracted and evaluated under a broad range of quality metrics. The results show that the proposed model can generate realistic-appearing synthetic maps. Quantitatively, the image histograms of the synthetic thickness maps agree with the real thickness maps, and the cross-correlations between synthetic and real maps are also high. Finally, we use the generated data as an augmentation technique to train stronger diagnostic models than those using only the real data. Conclusions: This approach provides valuable data augmentation, which can help overcome key bottlenecks of limited data. Translational Relevance: By addressing the challenge posed by limited data, the proposed method helps apply machine learning methods to diagnose neurodegenerative diseases from retinal imaging.

Retinal arterial pulsation as an indicator of raised intraocular pressure.

Ophthalmic emergencies are invariably challenging for the non-specialist to identify and evaluate, and may be complicated by occult but vision threatening raised intraocular pressure. We present a case of hypertensive uveitis accompanied by the finding of retinal arterial pulsation, which when visualised by direct ophthalmoscopy allows the non-specialist to identify significantly raised intraocular pressure requiring urgent evaluation by an ophthalmologist.