The ISNT Rule: How Often Does It Apply to Disc Photographs and Retinal Nerve Fiber Layer Measurements in the Normal Population?

Poon, Yi-Chieh; Valle, David Solá-Del; Turalba, Angela; Falkenstein, Iryna; Horsley, Michael B.; Kim, Julie H.; Song, Brian J.; Takusagawa, Hana L.; Wang, Kaidi; Chen, Teresa C.

doi:10.1016/j.ajo.2017.09.018

Cited by 24 publications

(44 citation statements)

References 44 publications

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“…Recent studies have attempted to answer this question and observed that, contrary to clinical wisdom, the rule is more often than not disobeyed even in healthy eyes. 5 Our results echo this sentiment in eyes with emmetropia or low myopia, showing that the problem may be compounded in eyes suffering from higher myopia. This finding supports another recent study published on a large myopic cohort.…”

Section: Thickness Of Papillary and Peripapillary Nerve Fiberssupporting

confidence: 80%

“…This rule is often violated in the healthy eye for both neuroretinal rim and peripapillary RNFL measures; however, variations such as IST and IS are obeyed in a majority of patients. 5 In myopia, concordance with the ISNT rule may be even less prevalent, with a recent study reporting concordance of only 12% with the ISNT rule for RNFL thickness. 6 This agrees with other studies that have shown changes in the position of peak thickness of the peripapillary RNFL in myopia.…”

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confidence: 99%

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Geometry of the Retinal Nerve Fibers From Emmetropia Through to High Myopia at Both the Temporal Raphe and Optic Nerve

Bedggood

Mukherjee

Nguyen

et al. 2019

Invest. Ophthalmol. Vis. Sci.

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Citation: Bedggood P, Mukherjee S, Nguyen BN, Turpin A, McKendrick AM. Geometry of the retinal nerve fibers from emmetropia through to high myopia at both the temporal raphe and optic nerve. Invest Ophthalmol Vis Sci. 2019;60:4896-4903. https://doi.org/10.1167/iovs.19-27539 PURPOSE. The geometry of retinal nerve fibers may be altered with myopia, a known risk factor for glaucoma. Recent developments in high resolution imaging have enabled direct visualization of nerve fiber bundles at the temporal raphe with clinical hardware, providing evidence that this area is sensitive to glaucomatous damage. Here, we test the hypothesis that nerve fiber geometry is altered by myopia, both at the temporal raphe and surrounding the optic nerve head.METHODS. Seventy-eight healthy individuals participated, with refractive errors distributed between emmetropia and high myopia (þ0 to À13 DS). Custom high-density OCT scans were used to visualize RFNL bundle trajectory at the temporal raphe. A standard clinical OCT protocol was used to assess papillary minimum rim width (MRW) and peripapillary retinal nerve fiber layer (RNFL) thickness. RESULTS.Measures of raphe shape-including position, orientation, and width-did not depend significantly on axial length. In 7.5% of subjects, the raphe was rotated sufficiently that inversion of structure-function mapping to visual field space is predicted in the nasal step region. Low concordance to ISNT and related rules was observed in myopia (e.g., for RNFL, 8% of high axial myopes compared with 67% of emmetropes). Greater robustness to refractive error was observed for the IT rule.CONCLUSIONS. High density OCT scans enabled visualization of marked interindividual variation in temporal raphe geometry; however, these variations were not well predicted by degree of myopia as represented by axial length. That said, degree of myopia was associated with abnormal thickness profiles for the papillary and peripapillary nerve fiber layer.

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Section: Thickness Of Papillary and Peripapillary Nerve Fiberssupporting

confidence: 80%

mentioning

confidence: 99%

Geometry of the Retinal Nerve Fibers From Emmetropia Through to High Myopia at Both the Temporal Raphe and Optic Nerve

Bedggood

Mukherjee

Nguyen

et al. 2019

Invest. Ophthalmol. Vis. Sci.

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“…The selected features for OD and OD-cup identification were: cup-to-disc ratio (CDR), neuroretinal rim (NRR) area, blood vessel area and the ISNT rule. More details about CDR, NRR, and ISNT rule can be found in the works of Hu et al, Mabuchi et al, Jonas et al and Poon et al [41][42][43][44]). Moreover, the SVM, the function kernel RBF, and the Artificial Neural Network (ANN) were used as classifications.…”

Section: Methods Using Features Extraction For Glaucoma Diagnosismentioning

confidence: 99%

Machine learning applied to retinal image processing for glaucoma detection: review and perspective

et al. 2020

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Introduction: This is a systematic review on the main algorithms using machine learning (ML) in retinal image processing for glaucoma diagnosis and detection. ML has proven to be a significant tool for the development of computer aided technology. Furthermore, secondary research has been widely conducted over the years for ophthalmologists. Such aspects indicate the importance of ML in the context of retinal image processing. Methods:The publications that were chosen to compose this review were gathered from Scopus, PubMed, IEEEXplore and Science Direct databases. Then, the papers published between 2014 and 2019 were selected . Researches that used the segmented optic disc method were excluded. Moreover, only the methods which applied the classification process were considered. The systematic analysis was performed in such studies and, thereupon, the results were summarized.Discussion: Based on architectures used for ML in retinal image processing, some studies applied feature extraction and dimensionality reduction to detect and isolate important parts of the analyzed image. Differently, other works utilized a deep convolutional network. Based on the evaluated researches, the main difference between the architectures is the number of images demanded for processing and the high computational cost required to use deep learning techniques.Conclusions: All the analyzed publications indicated it was possible to develop an automated system for glaucoma diagnosis. The disease severity and its high occurrence rates justify the researches which have been carried out. Recent computational techniques, such as deep learning, have shown to be promising technologies in fundus imaging. Although such a technique requires an extensive database and high computational costs, the studies show that the data augmentation and transfer learning techniques have been applied as an alternative way to optimize and reduce networks training.

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“…The sudden or abrupt diminishing of NRR thickness indicates the presence of a notch. Notch is major sign of glaucoma, even though the CDR may be within normal limits [11][12][13]. ISNT rule is generally true for most of the ODs.…”

Section: Fig 1 Anatomy Of Eye Showing Optic Nerve Headmentioning

confidence: 99%

“…3 shows a healthy optic disc, having ISNT rule intact. The latest study by Linda Yi-Chieh Poon, et al, proposes to use IST and IS rule instead of ISNT rule, for better glaucoma diagnosis [13].…”

Section: Fig 1 Anatomy Of Eye Showing Optic Nerve Headmentioning

confidence: 99%

Recent Automated Glaucoma Detection Techniques using Color Fundus Images

2019

IJITEE

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One of the areas in which C-DAC, Mohali is actively engaged, is development of AI powered fundus imaging system providing insight into several severe eye diseases. Glaucoma, one of the most hazardous ocular disease, continues to affect and burden a large section of our population. Neuropathy of optic nerve cells is the prime cause of glaucoma and is the second leading cause of blindness worldwide. It doesn’t manifest itself and is often termed as the silent thief of eye sight. The damage caused by glaucoma is irreversible. Therefore, it is imperative to detect glaucoma at an early stage. The medical literature related to glaucoma indicates that glaucoma detection is a complex process and depends on combination of several parameters. The conventional methods of hand-crafted feature extraction are tedious, time consuming and require human intervention. Even though many such systems have recently shown promising results, but these systems require extensive feature engineering and have limited representation power owing to varied morphology of the optic nerve head. Most of the proposed systems have targeted the parameter cup to disc ratio (CDR) for detection of glaucoma, but that may not be the best approach for building efficient, robust and accurate automated system for glaucoma diagnosis. This paper advocates the use of hybrid approach of manual feature crafting with deep learning. It holds promise of improving the accuracy of glaucoma diagnosis through the automated techniques. It is further proposed that if diagnosis based on CDR remains inconclusive other methods of diagnosis should be adopted to come to a certain conclusion.

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The ISNT Rule: How Often Does It Apply to Disc Photographs and Retinal Nerve Fiber Layer Measurements in the Normal Population?

Cited by 24 publications

References 44 publications

Geometry of the Retinal Nerve Fibers From Emmetropia Through to High Myopia at Both the Temporal Raphe and Optic Nerve

Geometry of the Retinal Nerve Fibers From Emmetropia Through to High Myopia at Both the Temporal Raphe and Optic Nerve

Machine learning applied to retinal image processing for glaucoma detection: review and perspective

Recent Automated Glaucoma Detection Techniques using Color Fundus Images

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