On the Quest for Ophthalmological Biomarkers for Long COVID: A Scoping Review

Abstract

COVID-19, which is caused by the SARS-CoV-2 virus, has caused millions of cases and fatalities around the world. It is clearer and clearer how ex-COVID-19 patients endure neurological symptoms, such as headaches and cognitive impairment, in addition to respiratory problems. Long COVID refers to symptoms that continue after the acute phase, impacting millions of people and having severe socioeconomic consequences. The pathogenesis of neurological symptoms in long-term COVID is still unknown, making diagnosis and management difficult. The purpose of this review is to investigate the ophthalmological/neurological effects of prolonged COVID and the possibility of eye-tracking technology as an objective biomarker for diagnosis and monitoring. A scoping literature review was carried out, yielding 15 relevant studies. Several ophthalmological signals, such as saccadic movements and pupillary reflexes, were found to be significantly affected in patients with long COVID. These signals were measured using a variety of methods, including infrared cameras and eye-tracking systems. The study emphasises the need for more research to develop standardised biomarkers for long COVID diagnosis and monitoring. Understanding the ophthalmological impacts of long COVID can help develop novel tools for assessing and controlling this disorder.

1. Introduction

COVID-19 is a respiratory infection caused by the SARS-CoV-2 virus for which, as of February 2025, over 777 million cases have been reported, and has contributed to 7 million deaths worldwide [1]. Frequently reported symptoms include fever, cough, fatigue, and dyspnoea [2]; however, many patients also experience neurological symptoms, such as headaches, cognitive impairment (‘brain fog’), and anosmia [3,4]. Despite widespread neurological symptoms, currently, there is little evidence for direct invasion of neuronal tissue or cerebrospinal fluid (CSF) by the virus [5]. Alternative hypotheses have been proposed to explain these neurological symptoms, including the expression of angiotensin-converting enzyme 2 (ACE-2) by neural cells [6], indirect encephalitis, thrombosis or hypoxia, and immune system hyperactivation [3].

Although mild COVID-19 cases typically resolve within two weeks of onset [7], sometimes symptoms persist beyond the acute phase of the disease, often lasting months. This condition, referred to as long COVID, post-acute sequelae of COVID-19 syndrome (PASC), or post COVID-19 syndrome, is defined by signs and symptoms consistent with COVID-19 infection over a period of 12 or more weeks that cannot be explained by an alternative diagnosis as outlined by the National Institute for Health and Care Excellence (NICE) [8]. Long COVID is estimated to have affected over 2 million people in the UK alone [9], causing significant long-term socio-economic implications [10]. Risk factors for long COVID include demographic characteristics (female sex and age), comorbidities (such as anxiety and depression), and severity of COVID infection, while vaccination may act as a protective factor [11].

Psychological and neuropsychiatric issues associated with long COVID, such as anxiety and depression, are commonly reported, with some researchers suggesting that these elements may be predominant in long COVID, akin to post-traumatic stress disorder (PTSD) [12]. The psychosocial burden is further intensified by unemployment, income loss, and increased reliance on social support.

Long COVID arises from a complicated interaction of different persisting immune processes. Often, there is also overlap with predisposing conditions with multi-system involvement (e.g., mast cell activation syndrome and perimenopausal syndrome). This results in a syndrome with varying combinations of hundreds of symptoms (the Centers for Disease Control and Prevention (CDC) refers to >200 symptoms). Amid this complexity, clinicians working with the long COVID population recognise patterns of symptom reporting among patients where symptoms have a nervous system basis (pain, fatigue, brain fog, headache, vision, balance, and hearing disturbance). Direct evidence of the pathological basis for central nervous system involvement is limited. There is evidence from postmortem studies [13], structural imaging [14], and functional imaging [15]. Ophthalmic signals provide a means of monitoring various aspects of brain function. We have taken an approach that structures information from ophthalmic signals according to impacts on brain function from long COVID pathology: altered resting state (e.g., visual cortical excitability); altered neuromodulation state (e.g., pupillary light reflex); and altered network connectivity (e.g., saccadic control).

Until the pathophysiology of the neurological symptoms of long COVID is fully understood, diagnosis and management remain challenging. NICE recommends early neurorehabilitation for patients with neurological sequelae, which affects over 80% of those hospitalised with COVID-19 [9,16]. However, many symptoms are non-specific, may not elicit any abnormal findings upon clinical examination, and may not be a direct result of COVID-19 infection. Additionally, examinations may also be subjective and rely on self-reported information and are, therefore, subject to bias. In contrast, automated eye feature evaluation can be used as an objective and non-invasive biomarker of neurocognitive status. This technology is reproducible, and more cost-effective and practical than invasive methods, such as analysis of biomarkers from serum or CSF [17]. The increasing availability of accurate, portable, and user-friendly eye trackers has expanded potential applications in various scenarios of this technology [18]. Previous studies have indicated that eye movements, such as saccadic movements (anti-saccade and prosaccade), smooth pursuit, and optokinetic nystagmus, are frequently affected in patients with long COVID, making them viable targets for diagnostic and monitoring tools [19,20,21].

Figure 1 offers a graphic representation of the rationale above and also briefs the scoping review process. This review examines the existing literature on the ophthalmological effects of long COVID to enhance understanding of its cognitive manifestations and inform the development of technologies for investigating this condition. A scoping review methodology was employed to evaluate the current evidence base, with findings structured within a biologically driven framework and translated into clinically relevant insights, informed by unmet needs observed in our long COVID rehabilitation service.

2. Materials and Methods

2.1. Registration and Reporting

This scoping literature review was registered on Open Science Framework (Retrieved from osf.io/qdc85). The reporting of this review was conducted according to PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) [22]. The checklist of PRISMA extension for scoping reviews is provided in Supplementary Material S2.

2.2. Search Strategy

A systematic literature search was performed on SCOPUS, which also covers most journal articles from the following databases: Science Direct, Web of Science, Medline, PubMed, EMBASE, and Reaxys. The search was last updated on 8 January 2025, using the following search string, which was co-created in a focus group, relying also on MESH terms generated with PUBMED: (TITLE-ABS-KEY (vng OR *nystagm* OR *saccad* OR slow-pursuit* OR smooth-pursuit* OR eye-movement* OR eye-track* OR pupillary OR pupillom*) OR TITLE-ABS-KEY (vog OR video-oculography OR videooculography OR videoculography) OR TITLE-ABS-KEY (vor OR vestibulo-ocular-reflex* OR vestibuloocular-reflex*) OR TITLE-ABS-KEY (optokinetic OR vestibular OR oculomotor OR ocular OR caloric) OR TITLE-ABS-KEY (vep OR visual-evoked-potential OR electroretinogra* OR phosphene-threshold OR visual-cortex-excitability) AND TITLE-ABS-KEY (long-covid* OR long-haul-covid* OR post-acute-covid* OR chronic-covid* OR post-covid*))

Further relevant grey literature was retrieved from the reference lists of the reviews that were returned by the first search to ensure literature saturation.

2.3. Study Eligibility

Broad inclusion criteria were only scientific articles that focused on ophthalmological signals (e.g., eye movement/tracking, pupillary light reflex, and visual evoked potentials) and long COVID in the English language. Those included studies had to be accessible to at least one of the authors.

Specific inclusion criteria were as follows:

• Studies of clinical populations being investigated for long COVID.
• Studies using automated technology for measuring the above symptoms.
• Studies reporting ophthalmological parameters and outcome measures relevant to the clinical management of patient populations.

Broad exclusion criteria were non-English language studies, non-primary, or non-peer reviewed research (dissertations, conference proceedings, and reviews, etc.), with reviews being inspected for potential overlaps to avoid any duplication. Ultimately, no reviews were included in the final selection.

Specific exclusion criteria were:

• Studies on fundoscopy.
• Studies on post-vaccination populations.
• Studies on healthy populations (unless healthy control comparator data were reported separately in a study also reporting on a patient population).

2.4. Study Selection

Two authors independently screened the studies by title and abstract, while three authors completed full-text screening against the inclusion and exclusion criteria, with conflicting decisions being mitigated by an additional unbiased reviewer.

2.5. Data Extraction and Synthesis

Relevant data were extracted and collected in an ad-hoc Excel sheet (see Supplementary Materials S1), organised by author, study type, participant number, inclusion/exclusion criteria, ophthalmological signal, company, methods, indicator of long COVID, and main findings. This was performed on the analysis. All included study results were merged together and critically compared using a narrative synthesis approach [23], which informs the design of novel methods and technologies for assessing and monitoring patients with long COVID.

3. Results

3.1. Summary of Results

The initial search returned 132 studies with the addition of 4 further studies that were retrieved from screening the reference list of the reviews initially returned. The records were last updated on 8 January 2025 to 269. These records were included in title/abstract screening. After applying the inclusion and exclusion criteria (i.e., the scientific articles in English focusing on ophthalmological signals/symptoms of long COVID), 19 studies were screened by the full text, of which 4 were excluded because of study design or methodology. The final set of included papers amounted to 15 in the review and extraction phase. A PRISMA flow chart is presented in Figure 2.

3.2. Study Characteristics

3.2.1. Study Types

The distribution of the study type among the included studies reflects methodological diversity. Among the 15 eligible studies, the most common study design is a cross-sectional study with five papers [20,24,25,26,27]; four [28,29,30,31] are cohort studies; three [32,33,34] are case–control studies; and three [35,36,37] are case studies. These exploratory and observational methodologies are useful to the research question but must be interpreted in context considering the heterogeneity in sample size and effect size.

3.2.2. Study Demographics

Table 1. Demographic data of study population.

Author&Year	N 1	Controls	Male	Female	Age (Years)	Setting	Days Since Diagnosis (Days) 2
Carbone 2022 [20]	78	23	41	37	55.2 ± 12.2	University	419.1 ± 26.0
De Giglio 2023 [36]	1	0	1	0	74	Hospital	60
Kelly 2022 [28]	377	300	245	132	28.4 ± 7.4	Hospital and university	>28
Bitirgen 2022 [24]	65	30	NA	NA	42.5 ± 10.7	University hospital	120 (60–150)
Garcia 2022 [29]	18	9	10	8	49.56 ± 9.14	University hospital	NA
Abdelrahman 2021 [25]	20	0	10	10	40 ± 9.58	Hospital	NA
Bellavia 2021 [26]	40	20	27	13	54.3 ± 16.7	Hospital	NA
Koskderelioglu 2022 [30]	120	44	48	72	39.0 ± 9.6	University	132 ± 66 (30–360)
Braceros 2021 [35]	1	0	1	0	28	Hospital	30
Sabel 2021 [37]	2	0	0	2	56 ± 16	Hospital	>28
González-Vides 2024 [32]	117	42	31	86	48.9 ± 11.15	Hospital and university	600
Jan Johansson 2024 [31]	38	0	9	29	46.8 ± 9.3	Hospital	660 (360–870)
Moritz Güttes 2024 [33]	179	49	88	91	35.63 ± 11	University hospital	442.49 ± 215
Vinuela-Navarro 2023 [27]	85	20	22	63	49.03 ± 6.67	Hospital	>28
Mehringer 2023 [34]	35	15	19	16	27.29 ± 3.84	University	389.25 ± 189.34

¹ ‘N’ is the number of the total subjects. ² The column “Days since diagnosis” reports the average number of days. Where possible and applicable the values were indicated as average ± standard deviation.

gives information about the demographic characteristics of the study population. It is noteworthy that many of the included studies are not strictly following the NICE standard of long COVID when selecting their subjects. Specifically, four studies [26,29,30,35,37] failed to report the time since diagnosis (TSD), while only six of the remaining eleven studies [20,24,31,32,33,34] established a minimum inclusion threshold of two months post-infection as recommended by NICE. Furthermore, some studies incorporated participants with a wide range of TSD, spanning from one to six weeks. To enhance the reliability and comparability of findings, it would be beneficial to examine the average TSD across these studies.

3.2.3. Ophthalmological Signal Types

Figure 3 gives an overview of the ophthalmological signals measured in the included papers. The most investigated biomarker of long COVID is the saccadic movement [20,25,27,28,29,31,36], followed by the pupillary light reflex (PLR) [24,26,27,35]. Finally, other ophthalmological biomarkers included smooth pursuit, gaze, nystagmus, visual evoked potential (VEP), visual field, and stereopsis performance.

3.2.4. Technologies

Figure 4 illustrates the most common technologies utilised to measure the signals above. The most frequently used technology was an infrared camera [24,26,28,29], followed by an eye-tracking camera [20,27,32], and then a fundus camera [35,37]. Some studies did not use any camera at all, preferring the use of a perimetry analyser [35,37] or electromyography [30,35,36]. A total of 3 out of 15 papers include headrest/chinrest [20,27,29] as their support equipment to stabilise participants’ heads, while three studies used head-mounted equipment like a virtual reality goggle [28,33,34]. Finally, the studies investigating PLR also utilised light flashes [24,35].

3.2.5. Ophthalmological Abnormalities

The 15 papers reviewed focused on different aspects of the ophthalmological signals in long COVID, providing valuable insights into the neurological implications of the condition.

Table 2. Differences in ophthalmological features between healthy participants and patients identified in the studies.

Ophthalmological Feature	Abnormalities Detected	Related Literature
Saccadic feature	Higher saccade error rate	[20,28,29,31,33]
	Prolonged saccade latency (reaction time)	[27,29,31,32,33]
Pursuit feature	Smooth pursuit abnormal (disrupted or unstable)	[27,28]
Nystagmus feature	Nystagmus (optokinetic and positional) abnormal	[25,28]
	Caloric weakness	[25]
Pupillary reflex feature	Increased latency of pupil contraction	[24]
	Reduced duration of pupil contraction	[24]
	Reduced latency of pupil dilation	[24]
	Higher dilatation velocity	[26]
	Higher absolute constriction amplitude	[26]
	Higher constriction index	[26]
	Higher baseline pupil diameter	[26]
	Reduced pupil constriction	[27]
	Reduced pupil dilation	[27]
Visual field feature	Suppression of static visual field perimetry	[35]
	Impaired peripheral field defects	[37]
VEP feature	Reduced VEP * amplitude	[30,35]
	Prolonged p100 latencies	[30]
	Abnormalities in several peripheral nerve measurements	[30]
Stereopsis performance feature	Increased reaction time	[33,34]
Other features	Mild macular thickening	[35]
	Abnormal ERG * implicit times	[35]
	Suppressed EOG * amplitude	[35]

* VEP = visual evoked potential, ERG = electroretinogram, EOG = electrooculogram.

outlines the main findings that the papers covered, while Supplementary Materials S1 offers a more detailed extraction table for reference. According to the different types of signals measured, the results are divided into eight categories: saccadic features, pursuit features, nystagmus features, pupillary reflex features, visual field features, visual evoked potential features, stereopsis performance feature, and other features.

The pupillary reflex feature included both increased constriction amplitude [26] and reduced pupil constriction [27].

4. Discussion

This review aimed to identify potential biomarkers for the diagnosis and monitoring of long COVID using ophthalmological signals. The volume and level of the evidence base reflect the relatively short time scale over which digital technology detecting ophthalmologic signals has been employed to study brain fog post-COVID-19. The search strategy identified 15 reports, most frequently reporting observational data on uncontrolled case series. Before synthesising the outputs with the most potential clinical relevance to post-COVID brain fog, it is important to understand some basic considerations required to interpret the evidence base.

Interpretation requires both awareness of the limitations of the study design and attention to the patient population studied. Some studies have inclusion criteria that require prior infection with COVID-19 but no threshold level of persisting symptoms. This sample may include patients with few or mild symptoms and a trajectory of slow improvement soon after infection. This sampled population could be very different from those seen today in post-COVID clinics, typically with brain fog impacting function and persisting multisystem involvement years after infection. It may be hypothesised that the effect sizes of differences detected in patients with few or mild symptoms would be greater in those more severely affected in the chronic stages, but this has not been studied directly. This assumption must be considered when extrapolating outputs to current post-COVID clinic populations. Use of a broad search strategy to capture all relevant evidence results in reporting from different contexts, such as ancillary testing in eye clinics. These outputs can contribute usefully to the research question but must be interpreted in context.

The breadth of evidence spans from single case reports through to relatively large controlled case series. Single case reports can highlight where the application of digital technology has increased understanding of the disease process to guide therapeutic formulations on an individual basis (e.g., DeGiglio et al., 2022 [36]; Braceros et al., 2021 [35]; Sabel et al., 2021 [37]). Specific outputs from these reports are harder to generalise to the post-COVID clinic population but do provide useful information (such as measures to extrapolate cortical excitability) to guide understanding of the multifactorial nature of post-COVID brain fog. In contrast to single case reports, the study with the largest sample size reported on 77 people after COVID-19 infection compared to normative values from a dataset of 300 healthy controls [28]. Although benefiting from a larger sample size, the analysis approach reported variance from the normative threshold but not the effect size of abnormality, making it hard to quantify post-COVID effects.

The outputs from our review can be mapped to theories of post-COVID brain fog derived from basic science and clinical studies. For comprehensive consideration of the potential mechanistic basis, readers are directed to useful editorials [38] and reviews [39]. Within this, we highlight areas where digital ophthalmic biosignals identified in our review can be mapped to potential biologic frameworks. Most broadly, ophthalmic biosignals have the potential to identify problems relating to disordered neuromodulation, neuroenergetics, and network efficiency, and to monitor responses to therapy.

Neuroenergetic disturbances may be one component driving post-COVID fatigue and brain fog as persisting inflammatory responses drive disturbed mitochondrial function and microglial activation (reviewed in [39]). Astrocytes play a vital role in neurometabolic coupling, and SARS-CoV-2 persistence in astrocytes is seen post-mortem [40]. Disturbed astrocyte control of the local metabolic environment and energy substrates negatively impacts neuronal excitability and neurotransmission. Abnormal metabolic activity seen in functional brain imaging of patients with post-COVID brain fog is thought to relate to astrocyte effects on neuroenergetics impacting glutamate neurotransmission [41,42]. Disturbance of astrocyte neurometabolic coupling reduces lactate supply to neurons and also alters resting-state control of cortical excitability [43]. Visual symptoms reported frequently in our post-COVID clinic cohort include migraineous aura and visual snow, which are thought to reflect abnormal control of visual cortex excitability [44], possibly through neuroglial inflammatory processes [45]. A number of the ophthalmic biosignals reported relate to cortical excitability in the visual system. Braceros et al., 2021 [35] reported a patient with persisting post-COVID visual snow with changes in the visual evoked potential and a reduced VEP100 amplitude (such a change can be seen when manipulating increased cortical excitability with excitatory non-invasive brain stimulation) [46]. In contrast to the case report of visual evoked potential amplitude alteration in a symptomatic post-COVID patient, the larger series of Koskderelioglu et al. [30], measuring VEPs in 65 people after COVID infection of whom around 14–18% had persisting symptoms, concluded no difference from healthy controls in VEP amplitude (the measure shown to change with manipulated cortical excitability). Another series measuring VEPs in 44 people after COVID-19 also reported no differences in p100 amplitude compared to healthy controls [47]. VEP parameters may be useful for tracking disease trajectory or response to therapy in people with visual system symptoms.

Related to visual evoked potential markers, another experiment presents an example of non-invasive neuromodulation potentially manipulating cortical network excitability. In two patients with post-COVID brain fog, the use of transcranial Alternating Current Stimulation targeting retinal neurons was suggested to improve performance in psychometric tests of attention and memory. The authors demonstrated a change in digital markers of retinal vessel dynamics (Dynamic Vascular Analyzer) and hypothesised downstream effects on synchrony in cortical networks (but this was not measured directly) [37].

A linked concept related to post-COVID brain fog and potentially measured by digital ophthalmic biosignals is that of efficiency in large-scale brain networks. This is of course influenced by neuroenergetic coupling at a local neuronal population level but also by the balance in neuromodulatory systems controlling whole-brain distributed network function (for reviews see Bressler and Menon, 2010; Avery and Krichmar, 2017) [48,49]. Infection-induced inflammatory stress states can result in an imbalance in neuromodulatory systems (reviewed by DiSabato et al., 2017 [50]). This in turn can influence the efficiency of connectivity in major brain networks. Brain imaging studies have identified changes in effective connectivity inversely correlating with cognitive performance in people with post-COVID brain fog [51,52] (noting that findings are variable between studies, e.g., see Scardua-Silva et al. [53], 2024, where no Default Mode Network functional connectivity changes were identified). Even in people not reporting significant cognitive complaints after COVID-19 infection, psychometric testing identified deficits in networks serving episodic memory and sustained attention [54]. In relation to this, ophthalmic biosignals are potentially useful, firstly because the pupillary light reflex can provide information on the balance between major neuromodulatory systems, and secondly, because efficiency in brain networks controlling eye movement strategies may have parallels to functions in other major brain networks.

Changes in pupil dilatation have been shown to correlate with activity in brain networks very relevant to the most commonly reported post-COVID cognitive complaints. Pupil parameters vary with increasing cognitive loads during tasks demanding attention, salience, and working memory networks. Pupil reactivity is shown to correlate with activity in these brain networks using functional brain imaging during tasks [55,56]. This corresponds to adrenergic neuromodulatory systems guiding attention, and balancing bottom-up control from the brainstem locus coeruleus [57] and top-down control from the Prefrontal Cortex [58]. Two series compared pupillary light reflex parameters in people recovering from COVID-19 to healthy controls using automated pupillometry. A finding common to both studies was shorter pupil contraction duration [24,59]. Karahan et al. [59] also reported shorter dilation latency, and Bitirigen et al. [24] reported prolonged latency of contraction. Interpretation of these observations is complicated, noting that these parameters are sympathetically driven but ultimately represent the interaction between parasympathetic vagal afferents and sympathetic locus coeruleus outputs, which then inhibit ongoing parasympathetic activity and activate pupil dilator muscles. This interaction is influenced by persisting inflammatory responses to infection, which impose neuroimmunomodulatory influence on the vagal response [60]. Useful information to note in interpreting these pupillary light reflex parameters comes from a longitudinal study of people with autonomic dysfunction, measured serially, prior to and following COVID-19 infection. Following COVID-19 infection, there was a reversal of the dominant presentation toward parasympathetic excess being more problematic than sympathetic withdrawal [61]. Irrespective of the precise interpretation of the mechanism underlying the pupillary light reflex changes, the findings of these studies demonstrate that on an individual level, automated pupillometry may usefully track changes in the balance of neuromodulatory systems. A study measuring continuous oscillation of pupil diameter with the Low/High Index of Pupillary Activity (LHIPA) during a virtual reality task did not identify differences between a post-COVID sample and healthy controls [34].

Eye-tracking technology offers another digital marker of efficiency in brain networks. There are parallels between brain networks controlling visual search strategy [62] and salience network-controlled interactions between the Default Mode Network and central executive networks [48]. Eye movement tasks can be scaled to increase demands of network connectivity, such as adding a requirement for impulse inhibition in anti-saccades or memory-guided saccades. An eye-tracking study reported a reduced accuracy rate in memory-guided saccades, and prolonged latency in anti-saccades compared to healthy controls (noting the sample size was nine participants in each group) [29]. Other studies also report abnormalities of antisaccades [20,27]. Consistent with these observations of eye-tracking abnormalities becoming more prominent in tasks with higher demands of network function is the observation from a series of 77 people following COVID-19 infection assessed with a battery of 14 eye-tracking tasks in the Oculomotor Vestibular Reaction Time Cognitive test (OVRT-C) [28]. This study reported abnormalities relative to a normative population dataset threshold for tasks with higher cognitive demand, such as antisaccades or a combination with reaction time tasks.

Another potentially useful digital ophthalmic biomarker is provided by measures of blink rate during cognitive tasks recorded with eye-tracking technology. A study by Garcia-Cena et al. [29] reported a higher total blink rate through the entire eye movement battery in people with post-COVID symptoms. Interpretation of blink rate is complicated, and it is timing relative to stimulus rather than total rate, which may provide the most useful information. It is suggested that during a task the timing of blinking may mark attentional set-shifting, either to shift to a new stimulus or renew perception if attention is maintained on the same stimulus. In this way, the timing of blinks may provide information about switching between the Default Mode Network and Central Executive Network [63,64].

The primary limitation of this scoping review lies in the variability of search terms used to describe long COVID. The condition is often referred to by different terms across studies, with some researchers opting for phrases like “symptoms after COVID-19” rather than the standardised term “long COVID.” Furthermore, the reported cases of long COVID sometimes did not strictly adhere to NICE guidelines regarding the number of days after recovery or failed to report this information entirely, which makes it ambiguous to determine whether the condition qualifies as long COVID. This lack of standardisation introduces potential bias into the literature search process and the coverage of relevant studies. Inconsistent terminology presents a significant challenge in designing an effective search strategy for this review. Consequently, the establishment of standardised protocols and reporting guidelines, such as those recommended by NICE and WHO, is strongly encouraged in long COVID research to enhance consistency and reduce ambiguity.

5. Conclusions

In conclusion, the studies included in this review highlight the potential of digital ophthalmic biomarkers, such as eye tracking, pupillometry, and visual evoked potentials, in assessing and monitoring long COVID. These biomarkers provide valuable insights into neuroenergetic disturbances and network inefficiencies, which are thought to contribute to the cognitive impairments seen in long COVID. Notable features extracted from the studies include changes in visual cortical excitability, pupillary light reflex parameters, and abnormalities in eye movement tasks such as anti-saccades and memory-guided saccades. While these findings offer promising directions for objective assessments, further research is required to standardise methods, refine techniques, and validate these biomarkers in larger, more diverse populations. Future studies should focus on the development of clinically applicable, cost-effective digital tools, and explore the role of ophthalmic biomarkers in diagnosing, monitoring, and even guiding therapeutic interventions for post-COVID conditions.