This manuscript is important, well-written, and will be of interest to a wide audience. Methods and data quality are appropriate. Conclusions in line with results. Kudos to the authors for a straightforward, well-executed, and significant study. I mention three points on which authors might choose to expand. 1. Implications of foundation models for medicine Authors might consider adding a sentence or two expanding on the implications of foundation models for medicine. The term (while not the idea) is relatively recent and some readers might benefit from an expanded explanation of the concept’s implications. In particular, it’s not just that foundation models can substantially reduce resources required to build usable health care AI (to democratize it, as authors write). It’s that in so doing these models could help direct support away from many single use, costly, initially impressive, AI tech demos, that rarely become integrated into routine care and feed skepticism of health care AI’s benefits. 2. Potential weaknesses associated with study’s data sets Authors acknowledge limitations associated with relying on datasets drawn almost entirely from the UK. However, because historically this literature (broadly, health care AI) has been prone to exaggeration—enabled often by ignoring limits created by homogeneity of people represented in study datasets—authors might consider highlighting this limitation not only at the end of the paper but also in one of the many passages noting the RETFound’s generalizability. 3. What is a clinical task Claims about the utility of RETFound to perform “a broad range of clinical tasks” (line 172) are accurate in their immediate context. But authors might examine what they mean by clinical tasks. When most people read the phrase, they assume a clinician—a person—is doing something, a task. The clinical tasks to which authors refer in this manuscript are tasks that can be achieved by AI. The point here is not lament AI’s potential replacement of people, it is rather to point out that AI is capable at this stageeven informed by foundation models – of performing only a very limited set of clinical tasks. To perform a “broad range” of “diverse” clinical tasks, health care AI needs still to better integrate human-AI collaboration. Authors might consider revising phrases about clinical tasks to more accurately express AI’s limitations in this regard.

A foundation model for generalizable disease detection from retinal images.

There is no consensus regarding the classification of optic neuritis, and precise diagnostic criteria are not available. This reality means that the diagnosis of disorders that have optic neuritis as the first manifestation can be challenging. Accurate diagnosis of optic neuritis at presentation can facilitate the timely treatment of individuals with multiple sclerosis, neuromyelitis optica spectrum disorder, or myelin oligodendrocyte glycoprotein antibody-associated disease. Epidemiological data show that, cumulatively, optic neuritis is most frequently caused by many conditions other than multiple sclerosis. Worldwide, the cause and management of optic neuritis varies with geographical location, treatment availability, and ethnic background. We have developed diagnostic criteria for optic neuritis and a classification of optic neuritis subgroups. Our diagnostic criteria are based on clinical features that permit a diagnosis of possible optic neuritis; further paraclinical tests, utilising brain, orbital, and retinal imaging, together with antibody and other protein biomarker data, can lead to a diagnosis of definite optic neuritis. Paraclinical tests can also be applied retrospectively on stored samples and historical brain or retinal scans, which will be useful for future validation studies. Our criteria have the potential to reduce the risk of misdiagnosis, provide information on optic neuritis disease course that can guide future treatment trial design, and enable physicians to judge the likelihood of a need for long-term pharmacological management, which might differ according to optic neuritis subgroups. 

/pdf/diagnosis-and-classification-of-optic-neuritis-30tee5hi.pdf

Diagnosis and classification of optic neuritis

A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study.

There is no consensus regarding the classification of optic neuritis, and precise diagnostic criteria are not available. This reality means that the diagnosis of disorders that have optic neuritis as the first manifestation can be challenging. Accurate diagnosis of optic neuritis at presentation can facilitate the timely treatment of individuals with multiple sclerosis, neuromyelitis optica spectrum disorder, or myelin oligodendrocyte glycoprotein antibody-associated disease. Epidemiological data show that, cumulatively, optic neuritis is most frequently caused by many conditions other than multiple sclerosis. Worldwide, the cause and management of optic neuritis varies with geographical location, treatment availability, and ethnic background. We have developed diagnostic criteria for optic neuritis and a classification of optic neuritis subgroups. Our diagnostic criteria are based on clinical features that permit a diagnosis of possible optic neuritis; further paraclinical tests, utilising brain, orbital, and retinal imaging, together with antibody and other protein biomarker data, can lead to a diagnosis of definite optic neuritis. Paraclinical tests can also be applied retrospectively on stored samples and historical brain or retinal scans, which will be useful for future validation studies. Our criteria have the potential to reduce the risk of misdiagnosis, provide information on optic neuritis disease course that can guide future treatment trial design, and enable physicians to judge the likelihood of a need for long-term pharmacological management, which might differ according to optic neuritis subgroups.

Retinal vessel diameters and function in cardiovascular risk and disease

Purpose Retinal signatures of systemic disease (‘oculomics’) are increasingly being revealed through a combination of high-resolution ophthalmic imaging and sophisticated modelling strategies. Progress is currently limited not mainly by technical issues, but by the lack of large labelled datasets, a sine qua non for deep learning. Such data are derived from prospective epidemiological studies, in which retinal imaging is typically unimodal, cross-sectional, of modest number and relates to cohorts, which are not enriched with subpopulations of interest, such as those with systemic disease. We thus linked longitudinal multimodal retinal imaging from routinely collected National Health Service (NHS) data with systemic disease data from hospital admissions using a privacy-by-design third-party linkage approach. Participants Between 1 January 2008 and 1 April 2018, 353 157 participants aged 40 years or older, who attended Moorfields Eye Hospital NHS Foundation Trust, a tertiary ophthalmic institution incorporating a principal central site, four district hubs and five satellite clinics in and around London, UK serving a catchment population of approximately six million people. Findings to date Among the 353 157 individuals, 186 651 had a total of 1 337 711 Hospital Episode Statistics admitted patient care episodes. Systemic diagnoses recorded at these episodes include 12 022 patients with myocardial infarction, 11 735 with all-cause stroke and 13 363 with all-cause dementia. A total of 6 261 931 retinal images of seven different modalities and across three manufacturers were acquired from 1 54 830 patients. The majority of retinal images were retinal photographs (n=1 874 175) followed by optical coherence tomography (n=1 567 358). Future plans AlzEye combines the world’s largest single institution retinal imaging database with nationally collected systemic data to create an exceptional large-scale, enriched cohort that reflects the diversity of the population served. First analyses will address cardiovascular diseases and dementia, with a view to identifying hidden retinal signatures that may lead to earlier detection and risk management of these life-threatening conditions.

/pdf/alzeye-longitudinal-record-level-linkage-of-ophthalmic-3m6w3upi.pdf

AlzEye: longitudinal record-level linkage of ophthalmic imaging and hospital admissions of 353 157 patients in London, UK

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AlzEye: longitudinal record-level linkage of ophthalmic imaging and hospital admissions of 353 157 patients in London, UK