Artifacts and Anatomic Variations in Optical Coherence Tomography

Utility of ganglion cells for the evaluation of anterior visual pathway pathology: a review

The management of optic neuropathy is fundamental to neuro-ophthalmic practice. Following the invention of the ophthalmoscope, clinicians, for a century or more, relied upon fundus examination in the evaluation of optic neuropathy. However, the advent of optical coherence tomography, based on the principle of backscattering of light and interferometry, has revolutionized the analysis of optic nerve and retinal disorders. Optical coherence tomography has proven of particular value in the measurement, at the micron level, of the peripapillary retinal nerve fibre layer and the ganglion cell layer. These measurements have proven critical in the differential diagnosis and monitoring of optic neuropathy. Specifically, thinning of the peripapillary nerve fibre layer provides evidence of axonal loss affecting any sector of the optic nerve. Thinning of the macular ganglion cell layer, on the other hand, shows a more precise correlation with visual deficits due to retrograde degeneration following optic nerve damage, although limited to central retina. In daily practise, optical coherence tomography is of great value in assessing the diagnosis, prognosis and response to treatment in optic neuropathy. Particular advances have been made, for example, in the assessment of optic neuritis, papilloedema and chiasmal compression which have translated to everyday practice. As with any other imaging technology the clinician must have a clear understanding of acquisition artefacts. A further issue is the relatively limited normative database in sub-populations such as the young and individuals with a refractive error > + 5 or < -5 dioptres.

Variability of relationship between inner-retinal structural changes and visual dysfunction in optic neuropathy

Optical coherence tomography (OCT) displays the retinal nerve fiber layer (RNFL) or macular ganglion cell and inner plexiform layer (GCIPL) thickness below 1st percentile in red color. This finding generally indicates severe inner-retinal structural changes and suggests poor visual function. Nevertheless, some individuals show preserved visual function despite these circumstances. This study aimed to identify the correlation between best-corrected visual acuity (BCVA) and inner-retinal thickness based on OCT parameters in various optic neuropathy patients with extremely low RNFL/GCIPL thickness, and determine the limitation of OCT for predicting visual function in these patients. 131 patients were included in the study. The mean BCVA in logMAR was 0.55 ± 0.70 with a broad range from - 0.18 to 3.00. Among the OCT parameters, temporal GCIPL (r = - 0.412) and average GCIPL (r = - 0.366) exhibited the higher correlations with BCVA. Etiological comparisons of optic neuropathies revealed significantly lower BCVA in LHON (all p < 0.05). Idiopathic optic neuritis (ON) and MOGAD exhibited better and narrower BCVA distributions compared to the other optic neuropathies. OCT had limited utility in reflecting BCVA, notwithstanding significant inner-retinal thinning after optic nerve injuries. Caution is needed in interpreting OCT findings, especially as they relate to the etiology of optic neuropathy.

Retinal OCT biomarkers and their association with cognitive function-clinical and AI approaches

Retinal optical coherence tomography (OCT) biomarkers have the potential to serve as early, noninvasive, and cost-effective markers for identifying individuals at risk for cognitive impairments and neurodegenerative diseases. They may also aid in monitoring disease progression and evaluating the effectiveness of interventions targeting cognitive decline. The association between retinal OCT biomarkers and cognitive performance has been demonstrated in several studies, and their importance in cognitive assessment is increasingly being recognized. Machine learning (ML) is a branch of artificial intelligence (AI) with an exponential number of applications in the medical field, particularly its deep learning (DL) subset, which is widely used for the analysis of medical images. These techniques efficiently deal with novel biomarkers when their outcome for the applications of interest is unclear, e.g., for diagnosis, prognosis prediction, disease staging, or any other relevance to clinical practice. However, using AI-based tools for medical purposes must be approached with caution, despite the many efforts to address the black-box nature of such approaches, especially due to the general underperformance in datasets other than those used for their development. Retinal OCT biomarkers are promising as potential indicators for decline in cognitive function. The underlying mechanisms are currently being explored to gain deeper insights into this relationship linking retinal health and cognitive function. Insights from neurovascular coupling and retinal microvascular changes play an important role. Further research is needed to establish the validity and utility of retinal OCT biomarkers as early indicators of cognitive decline and neurodegenerative diseases in routine clinical practice. Retinal OCT biomarkers could then provide a new avenue for early detection, monitoring and intervention in cognitive impairment with the potential to improve patient care and outcomes.

[Retinal optical coherence tomography biomarkers and their association with cognitive functions : Clinical and artificial intelligence approaches. German version]

Retinal optical coherence tomography (OCT) biomarkers have the potential to serve as early, noninvasive, and cost-effective markers for identifying individuals at risk for cognitive impairments and neurodegenerative diseases. They may also aid in monitoring disease progression and evaluating the effectiveness of interventions targeting cognitive decline. The association between retinal OCT biomarkers and cognitive performance has been demonstrated in several studies, and their importance in cognitive assessment is increasingly being recognized. Machine learning (ML) is a branch of artificial intelligence (AI) with an exponential number of applications in the medical field, particularly its deep learning (DL) subset, which is widely used for the analysis of medical images. These techniques efficiently deal with novel biomarkers when their outcome for the applications of interest are unclear, e.g., for the diagnosis, prognosis prediction and disease staging. However, using AI-based tools for medical purposes must be approached with caution, despite the many efforts to address the black-box nature of such approaches, especially due to the general underperformance in datasets other than those used for their development. Retinal OCT biomarkers are promising as potential indicators for decline in cognitive function. The underlying mechanisms are currently being explored to gain deeper insights into this relationship linking retinal health and cognitive function. Insights from neurovascular coupling and retinal microvascular changes play an important role. Further research is needed to establish the validity and utility of retinal OCT biomarkers as early indicators of cognitive decline and neurodegenerative diseases in routine clinical practice. Retinal OCT biomarkers could then provide a new avenue for early detection, monitoring and intervention in cognitive impairment with the potential to improve patient care and outcomes.

Deep learning-based identification of eyes at risk for glaucoma surgery

To develop and evaluate the performance of a deep learning model (DLM) that predicts eyes at high risk of surgical intervention for uncontrolled glaucoma based on multimodal data from an initial ophthalmology visit. Longitudinal, observational, retrospective study. 4898 unique eyes from 4038 adult glaucoma or glaucoma-suspect patients who underwent surgery for uncontrolled glaucoma (trabeculectomy, tube shunt, xen, or diode surgery) between 2013 and 2021, or did not undergo glaucoma surgery but had 3 or more ophthalmology visits. We constructed a DLM to predict the occurrence of glaucoma surgery within various time horizons from a baseline visit. Model inputs included spatially oriented visual field (VF) and optical coherence tomography (OCT) data as well as clinical and demographic features. Separate DLMs with the same architecture were trained to predict the occurrence of surgery within 3 months, within 3-6 months, within 6 months-1 year, within 1-2 years, within 2-3 years, within 3-4 years, and within 4-5 years from the baseline visit. Included eyes were randomly split into 60%, 20%, and 20% for training, validation, and testing. DLM performance was measured using area under the receiver operating characteristic curve (AUC) and precision-recall curve (PRC). Shapley additive explanations (SHAP) were utilized to assess the importance of different features. Model prediction of surgery for uncontrolled glaucoma within 3 months had the best AUC of 0.92 (95% CI 0.88, 0.96). DLMs achieved clinically useful AUC values (> 0.8) for all models that predicted the occurrence of surgery within 3 years. According to SHAP analysis, all 7 models placed intraocular pressure (IOP) within the five most important features in predicting the occurrence of glaucoma surgery. Mean deviation (MD) and average retinal nerve fiber layer (RNFL) thickness were listed among the top 5 most important features by 6 of the 7 models. DLMs can successfully identify eyes requiring surgery for uncontrolled glaucoma within specific time horizons. Predictive performance decreases as the time horizon for forecasting surgery increases. Implementing prediction models in a clinical setting may help identify patients that should be referred to a glaucoma specialist for surgical evaluation.

Ocular Optical Coherence Tomography in the Evaluation of Sellar and Parasellar Masses: A Review

Compression of the anterior visual pathways by sellar and parasellar masses can produce irreversible and devastating visual loss. Optical coherence tomography (OCT) is a noninvasive high-resolution ocular imaging modality routinely used in ophthalmology clinics for qualitative and quantitative analysis of optic nerve and retinal structures, including the retinal ganglion cells. By demonstrating structural loss of the retinal ganglion cells whose axons form the optic nerve before decussating in the optic chiasm, OCT imaging of the optic nerve and retina provides an excellent tool for detection and monitoring of compressive optic neuropathies and chiasmopathies due to sellar and parasellar masses. Recent studies have highlighted the role of OCT imaging in the diagnosis, follow-up, and prognostication of the visual outcomes in patients with chiasmal compression. OCT parameters of optic nerve and macular scans such as peripapillary retinal nerve fiber layer thickness and macular ganglion cell thickness are correlated with the degree of visual loss; additionally, OCT can detect clinically significant optic nerve and chiasmal compression before visual field loss is revealed on automated perimetry. Preoperative values of OCT optic nerve and macular parameters represent a prognostic tool for postoperative visual outcome. This review provides a qualitative analysis of the current applications of OCT imaging of the retina and optic nerve in patients with anterior visual pathway compression from sellar and parasellar masses. We also review the role of new technologies such as OCT-angiography, which could improve the prognostic ability of OCT to predict postoperative visual function.

A Myopic Normative Database for Retinal Nerve Fiber Layer Thickness using Optical Coherence Tomography

PRCIS

The purpose of this study was to determine changes in OCT color codes after applying a myopic normative database. The diagnostic performance of the retinal nerve fiber layer analysis improved with the use of this database.

PURPOSE

To evaluate the pRNFL color codes based on a newly generated myopic normative database in comparison to the built-in normative database.

METHODS

A total of 371 subjects were included in this validation study in an attempt to generate a myopic normative database. Eighty myopic glaucomatous and 80 myopic healthy eyes were evaluated to determine the diagnostic performance of this database. The distribution of the color codes was investigated among the groups with reference to the built-in and myopic normative databases, and the two databases were compared in terms of abnormal color code frequency. The diagnostic performance of the myopic database was presented with sensitivity, specificity and area under the receiver operating characteristics curve (AUROC) values.

RESULTS

The agreement between the databases decreased with increasing myopia degree. The distribution of the color codes of the built-in software significantly differed among the study groups in all sectors (P=0.009 for the temporal sector and P<0.001 for the remaining sectors). When the myopic database was used, there were no longer significant differences among the groups for the temporosuperior, temporoinferior, temporal and nasal sectors (P=0.561, 0.299, 0.201, and 0.089 respectively). After applying the myopic normative database, the specificity of the pRNFL color codes increased from 70.1% to 90.2%, and the AUROC value from 0.851 to 0.945.

CONCLUSIONS

The use of a myopic normative database for pRNFL using SD-OCT significantly decreased differences among myopia severity groups, and may help to more reliably assess glaucoma in myopic eyes.

Evaluation of Average Retinal Nerve Fiber Layer Measurement in Eyes with Refractive Errors

SIGNIFICANCE

Optical coherence tomography (OCT) measurements of peripapillary retinal nerve fiber layer (pRNFL) play an important role in the diagnosis of glaucoma and optic atrophy. However, the interpretation of these measurements in patients with refractive errors, especially of a high degree, presents great difficulties. Optical coherence tomography instruments from most manufacturers do not take into account the effect of refractive errors, especially of a high degree, on quantitative measurements of pRNFL.

PURPOSE

The aim of this study was to develop a simple and easy method for evaluation of average pRNFL in eyes with refractive errors.

METHODS

Average pRNFL was measured by Cirrus HD-OCT (Carl Zeiss Meditec Inc., Dublin, CA) in 183 healthy White subjects (183 eyes) older than 40 years, with an axial length of the eye from 22.5 to 24.5 mm and spherical equivalent of refraction from -1.63 to 2.0 D.

RESULTS

For an average pRNFL, normative database of eyes with refraction close to emmetropia was constructed. The calculated first and fifth percentiles for age groups 41 to 50, 51 to 60, 61 to 70, and 71 to 85 years were 81 and 83, 79 and 81, 78 and 80, and 76 and 79 μm, respectively. Littmann-Bennett formula was modified to calculate a table containing first and fifth percentiles for eyes with axial lengths of 19 to 30 mm in the same age groups.

CONCLUSIONS

For the correct interpretation of the measurements of pRNFL in patients with refractive errors, an original table was proposed, which provides a quick assessment of the results obtained on the Cirrus HD-OCT device. The proposed new formulas make it easy to calculate a similar table for any optical coherence tomography device using existing databases or after collecting a normative database of eyes with refraction close to emmetropia.

Optical coherence tomography's current clinical medical and dental applications: a review

Optical coherence tomography (OCT) is a non-invasive investigative technique that is used to obtain high-resolution three-dimensional (3D) images of biological structures. This method is useful in diagnosing diseases of specific organs like the eye, where a direct biopsy cannot be conducted. Since its inception, significant advancements have been made in its technology. Apart from its initial application in ophthalmology for retinal imaging, substantial technological innovations in OCT brought by the research community have enabled its utilization beyond its original scope and allowed its application in many new clinical areas. This review presents a summary of the clinical applications of OCT in the field of medicine (ophthalmology, cardiology, otology, and dermatology) and dentistry (tissue imaging, detection of caries, analysis of dental polymer composite restorations, imaging of root canals, and diagnosis of oral cancer). In addition, potential advantages and disadvantages of OCT are also discussed.