Visual fields and OCT role in diagnosis of glaucoma

Automatic retinal image analysis methods using colour fundus images for screening glaucomatous optic neuropathy

OBJECTIVES

Train an automatic retinal image analysis (ARIA) method to screen glaucomatous optic neuropathy (GON) on non-mydriatic retinal images labelled with the additional results of optical coherence tomography (OCT) and assess different models for the GON classification.

METHODS

All the images were obtained from the hospital for training and 10-fold cross-validation. Two methods were used to improve the classification performance: (1) using images labelled with the additional results of OCT as the reference standard and (2) generating models using retinal features from the entire images, the region of interest (ROI) of the optic disc, and the ROI of the macula, and the combination of all the features.

RESULTS

Overall, we collected 1338 images with paired OCT scans. In 10-fold validation, ARIA achieved sensitivities of 92.2 %, 92.7% and 85.7%, specificities of 88.8%, 86.7% and 80.2% and accuracies of 90.6%, 89.9% and 83.1% using the retinal features from the entire images, the ROI of the optic disc and the ROI of the macula, respectively. We found the model combining all the features has the best classification performance and obtained a sensitivity of 92.5%, a specificity of 92.1% and an accuracy of 92.4%, which is significantly different from other models (p<0.001).

CONCLUSION

We used two methods to improve the classification performance and found the best model to detect glaucoma on colour fundus retinal images. It can become a cost-effective and relatively more accurate glaucoma screening tool than conventional methods.

Glaucoma detection using image processing techniques: A literature review

The term glaucoma refers to a group of heterogeneous diseases that cause the degeneration of retinal ganglion cells (RGCs). The degeneration of RGCs leads to two main issues: (i) structural changes to the optic nerve head as well as the nerve fiber layer, and (ii) simultaneous functional failure of the visual field. These two effects of glaucoma may lead to peripheral vision loss and, if the condition is left to progress it may eventually lead to blindness. No cure for glaucoma exists apart from early detection and treatment by optometrists and ophthalmologists. The degeneration of RGCs is normally detected from retinal images which are assessed by an expert. These retinal images also provide other vital information about the health of an eye. Thus, it is essential to develop automated techniques for extracting this information. The rapid development of digital images and computer vision techniques have increased the potential for analysis of eye health from images. This paper surveys current approaches to detect glaucoma from 2D and 3D images; both the limitations and possible future directions are highlighted. This study also describes the datasets used for retinal analysis along with existing evaluation algorithms. The main topics covered by this study may be enumerated as follows.

Technology and the Glaucoma Suspect

Purpose

To determine and compare the diagnostic performance of spectral-domain optical coherence tomography (SD-OCT), stereoscopic disc photographs, and automated perimetry as assessed by a group of glaucoma specialists in differentiating individuals with early glaucoma from suspects.

Methods

Forty-six eyes (46 patients) with suspicious optic nerves had previously undergone SD-OCT scans, 24-2 visual fields (VFs), and optic disc photographs. The average VF mean deviation was −1.97 ± 2.09 (SD) dB. Four glaucoma specialists examined the 138 individual diagnostic tests and classified the patient as likely glaucomatous or nonglaucomatous based on the results of a single test. The diagnostic performances of each of the three tests were compared to a previously determined reference standard, based on the consensus of a separate panel of four glaucoma specialists who examined all three tests together.

Results

Among the four specialists, the interobserver agreement across the three diagnostic tests was poor for VF and photos, with kappa (κ) values of 0.13 and 0.16, respectively, and moderate for OCT, with κ value of 0.40. Using panel consensus as reference standard, OCT had the highest discriminative ability, with an area under the curve (AUC) of 0.99 (95% 0.96–1.0) compared to photograph AUC 0.85 (95% 0.73–0.96) and VF AUC 0.86 (95% 0.76–0.96), suggestive of closer performance to that of a group of glaucoma specialists.

Conclusions

Compared to VF and disc photography, SD-OCT, when used alone, had better internal agreement as well as better agreement with the consensus of clinicians using all available data. Future studies should evaluate best practices for SD-OCT interpretation.

Over-diagnosis and over-treatment in Ophthalmology: A review of the literature

OBJECTIVE

To determine if the pernicious effects of over-diagnosis and over-treatment have been studied in the most common ocular diseases.

MATERIAL AND METHODS

Bibliographic search conducted in PubMed.

RESULTS

The search algorithm used retrieved 29 articles related with the topic. Most of them address the issue tangentially. Only 4 of them address the problem directly. One of them focuses on the problem of over-diagnosis and over-treatment in idiopathic intracranial hypertension. The remaining 3 focus on the problem of over-diagnosis and over-treatment in glaucoma.

CONCLUSION

The ophthalmology community has thought very little about the detrimental effects of over-diagnosis and over-treatment.