Neuro-opthalmic Conditions Misdiagnosed as Glaucoma: A Retrospective Study of Cases and Contributing Factors in a Developing Setting

Abstract

Purpose

The aims are to study the prevalence, causes, and challenges of neuro-ophthalmic disorders being misdiagnosed as glaucoma in a tertiary care setting and to identify strategies for improving diagnostic skills in resource-constrained settings.

Design

Single-center, retrospective, cross-sectional study over a period of 6 months, that is, July–December 2024, at a tertiary care hospital in Central India.

Methodology

Medical records of 53 patients who presented to the glaucoma clinic during the study period were reviewed. All of them had already been diagnosed with glaucoma at other facilities. Seventeen such patients, who were later established to have nonglaucomatous (neuro-ophthalmic) conditions, were analyzed. Patients who were proven to have either primary or secondary glaucoma after comprehensive ophthalmic and neuroimaging evaluation were excluded. The demographic data, clinical findings, investigation results, and causes of misdiagnosis were analyzed.

Results

The proportion of misdiagnosis was 32% (17/53). Compressive optic neuropathy was the most common mimicker, followed by demyelinating optic neuropathy at 23.5% and ischemic optic neuropathy at 17.6%. Major contributing factors to diagnostic error included falsely elevated intraocular pressure (IOP), optic disk cupping with pallor, family history of glaucoma, and lack of access to central corneal thickness assessment, visual field analysis, and neuroimaging. Visual field analysis and magnetic resonance imaging (MRI) were key investigations in diagnosis.

Conclusion

Neuro-ophthalmic conditions mistaken for glaucoma lead to delayed and inappropriate management. The introduction of structured diagnostic algorithms, multimodal imaging, and clinician training, supported by telemedicine and artificial intelligence (AI)-assisted tools, may lead to a significant reduction in misdiagnosis and promote earlier diagnosis, especially in resource-constrained healthcare settings.

Introduction

Glaucoma is a chronic progressive optic neuropathy and remains one of the most important causes of irreversible blindness worldwide, including in India, with a prevalence of 1.2–3.68% in adults above 40 years.^1–5 It is often termed the “silent thief of sight” due to its insidious onset and irreversible visual loss if undetected. Though open-angle glaucoma (OAG) is the most common form, differentiation of OAG from other optic neuropathies continues to remain a challenge for clinicians, especially in community and secondary care settings.^1–5

Compressive, ischemic, demyelinating, hereditary, nutritional, and toxic optic neuropathies are several neuro-ophthalmic disorders that may appear clinically similar to glaucoma, with signs of optic disk cupping, thinning of the retinal nerve fiber layer (RNFL), and visual field defects.^6,7 Such an overlap in structural and functional changes may lead to misdiagnosis, particularly where advanced imaging or neuroimaging facilities are not available.^8,9

In resource-constrained settings, limited access to advanced diagnostics such as optical coherence tomography (OCT), central corneal thickness (CCT) measurement, and neuroimaging, combined with overlapping clinical features between glaucomatous and nonglaucomatous optic neuropathies, increases the risk of misdiagnosis.^8,9 This may result in inappropriate initiation of intraocular pressure (IOP)-lowering therapy and delayed recognition of potentially treatable conditions.^8,12

Recent studies confirm the role of multimodality imaging, including OCT-based RNFL and macular ganglion cell analysis, in differentiating glaucomatous from nonglaucomatous optic nerve damage.^13,14

It is against this background that the present study was undertaken to evaluate the prevalence, reasons, and patterns of neuro-ophthalmic conditions misdiagnosed as glaucoma in a tertiary care setting. It further identifies diagnostic gaps contributing to such errors and thus proposes strategies to improve diagnostic accuracy, which is highly relevant for low-resource healthcare environments where early and accurate differentiation is a major challenge.

Methodology

It is a retrospective, cross-sectional study conducted at a tertiary care hospital and research institute in Central India. Patients attending the glaucoma clinic over a period of 6 months, from July 2024 to December 2024, were studied. The study focused on individuals who were initially diagnosed with glaucoma elsewhere (in resource-limited settings) and were started on medication but were later found to have a neuro-ophthalmic condition. Records of a total of 53 patients who attended the glaucoma clinic during a period of 6 months were reviewed, and 17 were found to have been misdiagnosed with glaucoma. These patients were referred from outside facilities or came for a second opinion at the glaucoma clinic of a tertiary care center, with a prior diagnosis of glaucoma made elsewhere. Patients who had undergone comprehensive ophthalmic evaluation, including IOP measurement, central corneal thickness measurements, optic nerve head assessment, visual field testing, and neuroimaging and were eventually diagnosed with nonglaucomatous optic neuropathy were included in the study. Patients with confirmed primary or secondary glaucoma based on optic nerve head changes, visual field defects, and IOP elevation were excluded. The study adhered to the tenets of the Declaration of Helsinki, and approval was obtained from the Institutional Human Ethics Committee (IHEC) of the institute prior to initiation (EC/NEW/INST.2020/1322). The requirement for individual informed consent was waived by the ethics committee due to the retrospective nature and use of de-identified data.

The following data were collected systematically: demographic details; presenting symptoms such as vision loss, pain, headache, or other presenting complaints; onset, duration, and progression of vision loss; whether glaucoma had been diagnosed at an outside healthcare facility; systemic medical history; medication history; use of IOP-lowering medication; family history of glaucoma; and other ocular conditions. Patients who were subjected to a detailed, comprehensive assessment, including IOP measurement [Goldmann applanation tonometry (GAT)], central corneal thickness assessment, optic disk evaluation (fundoscopy), optical coherence tomography retinal nerve fiber layer testing (OCT RNFL), Humphrey visual field (HVF) analysis (30-2), magnetic resonance imaging (MRI) of the brain and orbit with 2 mm optic nerve cuts, and fundus fluorescein angiography (FFA), were included in the study. The medical records were reviewed in sequence: clinical findings → IOP and central corneal thickness (CCT) measurements → fundus evaluation → OCT RNFL and HVF reports → red-flag features → MRI/computed tomography (CT), FFA, blood tests, and other relevant investigations → final diagnosis. The reasons for misdiagnosis and final diagnosis were documented and analyzed accordingly.

Results

Of 53 referred “glaucoma” cases, 17 (32%) were reclassified as nonglaucomatous optic neuropathies after comprehensive evaluation.

Among the misdiagnosed cases (n = 17), 58% were <50 years, 58.8% were male, and 76.5% had bilateral involvement. Figure 1 summarizes the investigations that led to clinical suspicion, principally Humphrey visual field (HVF) analysis, optical coherence tomography retinal nerve fiber layer testing (OCT RNFL), fundus examination, disk changes, and central corneal thickness (CCT) measurements. However, HVF analysis played a critical role in raising clinical suspicion across the majority of cases, complementing other investigations and reinforcing preliminary assessments. The characteristic HVF patterns and key distinguishing features across the various neuro-ophthalmic diagnoses are summarized in Table 1.

Fig. 1.

Investigations that led to clinical suspicion in study subjects (n = 17)

Table 1.

Visual field patterns across neuro-ophthalmic diagnoses

Diagnosis	Common HVF pattern	Key distinguishing feature
Compressive	Bitemporal/homonymous hemianopia	Vertical meridian respect
Demyelinating	Central/paracentral scotoma	Temporal wedge defects
Ischemic	Altitudinal defects	Sudden onset correlation
Traumatic/nutritional	Generalized depression	Temporal involvement
Overlap patterns	Arcuate/nasal step	Pallor > cupping, vertical respect

However, clinical suspicion was confirmed by neuroimaging (MRI of the brain and orbit with 2 mm optic nerve cuts), blood investigations, fundus fluorescein angiography (FFA), cerebrospinal fluid analysis, and aquaporin antibody testing. In our study, MRI of the brain and orbit with 2 mm optic nerve cuts (with contrast when indicated) was the preferred first-line investigation for suspected nonglaucomatous optic neuropathies, such as compressive, demyelinating, ischemic, or chiasmal lesions. Computed tomography (CT) scanning was reserved for cases in which MRI was contraindicated, such as in patients with pacemakers, metallic implants, or severe claustrophobia, or when bony involvement, orbital trauma, or acute hemorrhage was suspected. MRI (neuroimaging) was the gold standard for confirming the final diagnosis, along with other tests. Figure 2 shows the investigations that led to the final diagnosis in each case.

Fig. 2.

Investigations that led to the final diagnosis in study subjects, in percentage (n = 17)

Compressive and demyelinating optic neuropathies, followed by ischemic optic neuropathy, were the most commonly misdiagnosed conditions. Figure 3 represents the final diagnoses of the patients who were previously misdiagnosed with glaucoma.

Fig. 3.

Final diagnosis in study subjects, in percentage (n = 17)

The reasons for misdiagnosis were analyzed in detail, and the major factors contributing to misdiagnosis are summarized in Table 2. Increased IOP is a risk factor for glaucoma; however, increased central corneal thickness leads to falsely high IOP measurements, which can mislead the diagnosis. This, combined with optic disk changes or a family history of glaucoma, led to diagnostic bias. A dearth of clinical expertise in differentiating subtle optic nerve head changes also played a critical role in the misdiagnosis of these cases.

Table 2.

Reasons for misdiagnosis in patients with neuro-ophthalmic conditions

Diagnosis	No. of patients	Reason for misdiagnosis
Compressive optic neuropathy	4	Optic disk cupping and pallor Increased IOP † but with higher CCT ‡, optic disk pallor and cupping Increased IOP but with higher CCT, optic disk pallor with family h/o glaucoma Increased cupping
Demyelinating optic neuropathy	4	Increased IOP but with higher CCT, optic disk pallor in two cases family h/o glaucoma, optic disk cupping family h/o glaucoma, increased IOP but due to higher CCT optic disk cupping
Infarct in parietal lobe causing visual pathway defect (optic radiation)	1	Optic disk pallor and cupping
Ischemic optic neuropathy	3	Increased IOP, cupping of optic disk Optic disk cupping and pallor Increased IOP but with higher CCT, family h/o glaucoma, optic disk pallor
Nutritional optic neuropathy	1	Optic disk cupping, family history of glaucoma
Space occupying lesion compressing visual pathway (optic radiation)	2	Optic disk cupping, family h/o of glaucoma Bilateral eye narrow angles
Traumatic optic neuropathy	2	Optic disk cupping, family history of glaucoma, initially diagnosed due to increased IOP but with higher CCT, with cup to disk asymmetry Family h/o glaucoma, higher IOP with thicker CCT, gonioscope findings suggestive of pigmentation of trabecular meshwork

^‡CCT, central corneal thickness; ^†IOP, intra ocular pressure

Discussion

Neuro-ophthalmic conditions mimicking glaucoma pose a major diagnostic dilemma, particularly in resource-constrained medical environments with limited access to neuroimaging and clinical experts. Differentiating between glaucomatous optic neuropathy and nonglaucomatous optic neuropathies is crucial for initiating accurate treatment and avoiding the unwarranted use of IOP-lowering drugs for prolonged periods. Population-based studies from India, including the Chennai Glaucoma Study, Andhra Pradesh Eye Disease Study, Hooghly River Glaucoma Study, and the Aravind Comprehensive Eye Survey, have highlighted the diagnostic challenges of glaucoma in diverse demographic settings.^1–3 The reported prevalence of glaucoma in Indian adults over 40 years ranges from 1.2 to 3.68%, underscoring the need for improved screening and differentiation from mimicking neuro-ophthalmic disorders.^1–5 Considering the high frequency of glaucoma diagnoses in the community, careful clinical correlation and recognition of atypical features are critical for differentiating glaucomatous from neuro-ophthalmic optic neuropathies rather than relying on population prevalence alone.

Nonglaucomatous optic neuropathies such as compressive optic neuropathy, ischemic optic neuropathy, demyelinating disorders, and hereditary optic neuropathies exhibit overlapping features with glaucoma, including optic disk cupping, thinning of the retinal nerve fiber layer (RNFL) on optical coherence tomography (OCT), and visual field defects.^6,7 This often leads to misdiagnosis, particularly in resource-constrained medical environments where central corneal thickness assessment, OCT, and neuroimaging are unavailable.

The primary factors leading to misdiagnosis include overlapping clinical features, misleading IOP measurements, and limited access to diagnostic tools. Elevated IOP is commonly associated with glaucoma; however, normal-tension glaucoma exists, and many nonglaucomatous conditions present with secondary IOP elevation. Central corneal thickness (CCT) variations can further lead to misleading IOP readings, as increased central corneal thickness leads to falsely high IOP readings, thereby misleading the diagnosis.^8,9 While optic disk cupping is a hallmark of glaucoma, other neuro-ophthalmic conditions, such as compressive and ischemic optic neuropathy, can also cause cupping with associated pallor.¹⁰ In compressive optic neuropathies, optic disk changes appear similar to glaucomatous optic neuropathy, with an increased cup-to-disk ratio, and depend on the size and location of space-occupying lesions.¹¹ Compressive lesions may produce glaucomatous-like cupping even at normal IOP.^14,15 Key red-flag features suggesting a nonglaucomatous optic neuropathy include optic disk pallor disproportionate to cupping, visual field defects respecting the vertical meridian, younger age at presentation (<50 years), rapid progression despite adequately controlled IOP, and unilateral or asymmetric atypical presentation. Recognition of these features is critical in prompting further evaluation and avoiding misdiagnosis.

RNFL thinning patterns differ between glaucomatous and nonglaucomatous optic neuropathies, with the latter more commonly involving the temporal (papillomacular) region and the former affecting the superior and inferior quadrants, aiding clinical differentiation. Advances in OCT have further improved early detection, with relative macular ganglion cell–inner plexiform layer (mGCIPL) thinning being more prominent in nonglaucomatous optic neuropathies even when peripapillary RNFL thickness is comparable.¹⁶ Binasal mGCIPL thinning may precede RNFL loss in chiasmal lesions.^17,18 Additionally, Bruch's membrane opening minimum rim width is typically lower in glaucomatous optic neuropathy compared to nonglaucomatous causes.¹⁴ Neurodegenerative and other neuro-ophthalmic disorders may mimic glaucomatous cupping and visual field defects.^19–22 However, glaucomatous defects typically follow arcuate patterns and respect the horizontal meridian, whereas neuro-ophthalmic defects are often central or atypical and may respect the vertical meridian.²² Neuroimaging, particularly MRI, plays a crucial role in confirming nonglaucomatous optic neuropathies; however, limited access in resource-constrained settings often leads to overreliance on clinical evaluation and optic disk findings, increasing the risk of misdiagnosis.²³ This is further compounded by limited specialist expertise and diagnostic bias from a positive family history and optic disk cupping.^23,24Figure 4 illustrates representative cases highlighting the importance of structured evaluation, red-flag recognition, and timely neuroimaging in preventing avoidable visual morbidity.

Figs 4A and B.

Representative cases illustrating neuro-ophthalmic conditions misdiagnosed as glaucoma. (A) Case 1—Compressive optic neuropathy: (A1) Fundus image showing bilateral eyes with temporal pallor and cupping; (A2) optical coherence tomography retinal nerve fiber layer (OCT RNFL) demonstrating diffuse bilateral RNFL thinning with marked temporal thinning; (A3) Humphrey visual field (HVF) depicting incongruous left homonymous hemianopia respecting the vertical meridian; (A4) MRI of the brain depicting a right temporoparietal lobe glioma; (B) Case 2—Compressive optic neuropathy: (B1) Fundus image showing bilateral eyes with optic disk pallor (LE > RE); (B2) OCT RNFL demonstrating thinning in the right eye; (B3) HVF depicting bitemporal hemianopia; (B4) MRI of the brain depicting a pituitary macroadenoma with supra- and parasellar extension

Several neuro-ophthalmic conditions closely mimic glaucoma. Compressive optic neuropathy due to intracranial lesions typically presents with progressive vision loss and disk cupping, with MRI being diagnostic.¹¹ Ischemic optic neuropathy, although classically acute, may be misdiagnosed due to overlapping RNFL thinning and cupping patterns.²⁵ Demyelinating optic neuropathies present with variable visual loss and require OCT and neuroimaging for differentiation.²² Hereditary optic neuropathies such as Leber hereditary optic neuropathy (LHON) and dominant optic atrophy, as well as nutritional and toxic optic neuropathies, may also show cupping and RNFL thinning but can be distinguished by clinical context, systemic evaluation, and targeted investigations.^26,27

To improve diagnosis and patient outcomes, comprehensive clinical evaluation, IOP measurements, and optic disk assessment should consider pallor, cup-to-disk ratio, and thinning patterns of the retinal nerve fiber layer. Visual field analysis should be performed to distinguish patterns indicative of glaucomatous optic neuropathy from those of nonglaucomatous optic neuropathy.²² However, routine neurological imaging is unnecessary in all cases suspected of glaucoma. The South East Asia Glaucoma Interest Group (SEAGIG) recommends that neuroradiological imaging be performed selectively in normal-tension glaucoma when atypical features are present, such as optic disk pallor disproportionate to the amount of cupping, unusual visual field defects, neurological signs or symptoms, or age at onset of <50 years. This aids in the early clinical detection and accurate classification of nonglaucomatous optic neuropathies misdiagnosed as glaucoma.²⁸

Even such targeted imaging is not feasible in resource-constrained medical environments due to a lack of infrastructure and the costs of imaging. This raises the urgent need for cost-effective screening strategies that can identify patients who are at high risk of misdiagnosis.

Multimodality imaging techniques such as optical coherence tomography, fundus fluorescein angiography, and automated perimetry should be used to improve diagnostic efficiency.²²

General ophthalmologists should undergo additional clinical training in distinguishing glaucomatous from nonglaucomatous optic neuropathies. Increasing awareness about neuro-ophthalmic conditions such as compressive optic neuropathies, ischemic optic neuropathies, and optic neuritis could facilitate early diagnosis, better referral systems, and appropriate management.

In this context, emerging technologies such as artificial intelligence (AI)-assisted fundus imaging and geometric deep learning for diagnosing glaucoma may provide a cost-effective alternative in resource-constrained medical environments.²⁹ In the future, AI could play a supportive role in enhancing diagnostic accuracy rather than replacing clinical judgment. AI could offer decision support in resource-limited settings where access to neuroimaging and specialist expertise is often limited by highlighting cases that deviate from typical glaucomatous patterns and require further workup. Even in advanced centers, AI can function as a second reviewer, enhancing quality assurance and helping reduce diagnostic oversights in busy clinical environments.²⁹

Stronger teleophthalmology networks would further enable neuro-ophthalmology specialists to perform remote assessments in resource-limited medical settings where neuroimaging facilities are not available.³⁰ It may be possible to improve diagnostic yield through the use of other lower-cost screening modalities, such as portable fundus cameras or visual field analyzers, so that fewer patients require expensive imaging.

There is an avoidable and unnecessary economic burden on healthcare systems and patients who are mistakenly diagnosed with glaucoma and undergo long-term treatment with IOP-lowering drugs while failing to receive appropriate therapy for their actual disease. A structured clinical algorithm incorporating red-flag assessment, multimodality imaging, and selective neuroimaging can help differentiate glaucomatous optic neuropathy from neuro-ophthalmic mimickers and guide appropriate management (Fig. 5).

Fig. 5.

Clinical algorithm for differentiating glaucomatous optic neuropathy from neuro-ophthalmic mimickers

Clinical Takeaways

Misdiagnosis of glaucoma can be minimized by recognizing key red-flag features, including younger age at presentation, optic disk pallor disproportionate to cupping, atypical visual field defects (especially those respecting the vertical meridian), and poor correlation between IOP and disease severity. Visual field analysis and OCT-based structural assessment should be routinely used to identify discordance patterns. Selective neuroimaging should be strongly considered in suspicious cases. Adoption of a structured diagnostic approach, as proposed in this study, can facilitate early identification of neuro-ophthalmic mimickers, reduce unnecessary treatment, and improve patient outcomes, particularly in resource-constrained settings.

Limitations

The present study has some limitations. First, the sample size was small, comprising 53 patients with only 17 confirmed cases of misdiagnosis, and thus may not reflect larger populations or different healthcare settings. However, the study offers valuable real-life insights into diagnostic pitfalls noted in a tertiary care setting. Second, the inherent shortcomings of a retrospective, cross-sectional design are evident in terms of selection and information bias, since data were obtained from medical records rather than from prospective standardized assessments. Inclusion of only those patients who underwent comprehensive ophthalmic and neuroimaging evaluations helped minimize the potential for bias. Third, the level of training of the initial diagnosing physician could not be reliably ascertained and is therefore another limitation of this study. Future multicenter prospective studies with larger cohorts are needed to validate these findings and formulate structured diagnostic protocols to achieve early differentiation between glaucomatous and nonglaucomatous optic neuropathies, especially in resource-constrained settings.

Conclusion

The findings support the need for updated guidelines on incorporating structured neuro-ophthalmic evaluations into routine glaucoma assessments, especially in patients with atypical disease progression, unilateral involvement, or rapidly deteriorating visual fields despite adequately controlled IOP. Strengthening telemedicine networks in areas where trained ophthalmologists are unavailable can facilitate expert consultations. This study identifies a growing demand for advanced diagnostic methodologies in differentiating neuro-ophthalmic disorders that masquerade as glaucoma, particularly in resource-poor medical settings with limited access to specialist services and neuroimaging facilities. The study showed that a significant number of patients diagnosed with glaucoma may have neurological disorders requiring alternative management approaches; hence, a multimodality screening strategy could help reduce the misdiagnosis rate through the introduction of cost-effective diagnostic technologies and improvements in clinical training programs and healthcare systems, ultimately leading to better patient outcomes. Low-cost diagnostic alternatives, including AI-assisted analysis of fundus imaging, should be developed for use in early screening efforts. Furthermore, general ophthalmologists and primary care physicians should be made aware of neuro-ophthalmic disorders presenting as glaucoma to facilitate earlier and more accurate referral. Meanwhile, MRI remains essential for confirming nonglaucomatous optic neuropathies, while OCT and visual field testing remain central to glaucoma diagnosis. With innovative clinical strategies, resource-constrained medical environments may be able to bridge the gap between accurate diagnosis and accessible care for all patients at risk of vision loss.

Acknowledgment

The authors acknowledge the support of the Department of Ophthalmology, All India Institute of Medical Sciences, Bhopal, in facilitating this study.

Author's Contribution

Priti Singh conceptualized and designed the study, supervised the work, interpreted the findings, and drafted the manuscript. Samendra Karkhur and Rama TS Duddumpudi contributed to data acquisition, data analysis, literature review, and critical revision of the manuscript. Vidhya Verma, Saroj Gupta, and Arushi Beri contributed to clinical data collection, data validation, interpretation of findings, and critical revision of the manuscript. All authors reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work.

AI Uses Declaration

Generative artificial intelligence, specifically ChatGPT by OpenAI, was used only for language refinement, grammatical correction, and formatting assistance during manuscript preparation. Artificial intelligence was not used for study design, patient evaluation, data collection, statistical analysis, generation of results, or interpretation of the findings. All AI-assisted content was critically reviewed and revised by the authors, who take full responsibility for the accuracy and integrity of the final manuscript.

Data Availability Declaration

The data generated and/or analyzed during the current study are not publicly available because they contain potentially identifiable clinical information. De-identified data may be made available from the corresponding author upon reasonable request, subject to institutional and ethical approval.