Accuracy of machine learning for differentiation between optic neuropathies and pseudopapilledema

Abstract

Background This study is to evaluate the accuracy of machine learning for differentiation between optic neuropathies, pseudopapilledema (PPE) and normals. Methods Two hundred and ninety-five images of optic neuropathies, 295 images of PPE, and 779 control images were used. Pseudopapilledema was defined as follows: cases with elevated optic nerve head and blurred disc margin, with normal visual acuity (> 0.8 Snellen visual acuity), visual field, color vision, and pupillary reflex. The optic neuropathy group included cases of ischemic optic neuropathy (177), optic neuritis (48), diabetic optic neuropathy (17), papilledema (22), and retinal disorders (31). We compared four machine learning classifiers (our model, GoogleNet Inception v3, 19-layer Very Deep Convolution Network from Visual Geometry group (VGG), and 50-layer Deep Residual Learning (ResNet)). Accuracy and area under receiver operating characteristic curve (AUROC) were analyzed. Results The accuracy of machine learning classifiers ranged from 95.89 to 98.63% (our model: 95.89%, Inception V3: 96.45%, ResNet: 98.63%, and VGG: 96.80%). A high AUROC score was noted in both ResNet and VGG (0.999). Conclusions Machine learning techniques can be combined with fundus photography as an effective approach to distinguish between PPE and elevated optic disc associated with optic neuropathies.

Portable hardware & software technologies for addressing ophthalmic health disparities: A systematic review

“…ML has also been used to accurately differentiate between optic neuropathies and pseudopapiledema by using fundus photography. 76 …”

The use of deep learning technology for the detection of optic neuropathy

“…Due to the lack of ophthalmologists in many departments, it is often necessary to use machine learning to evaluate optic disc edema. Ahn et al (46) used machine learning to distinguish between patients with optic neuropathy, pseudopapilledema (PPE), and normal subjects. A model was designed and compared with the 3 most commonly used machine learning classifiers, GoogleNet Inception V3, 19 layers of super deep convolution network from visual geometry group, and 50 layers of deep residual learning (ResNet).…”

“…As the structure of the optic disc is different between the nasal and temporal sides, the direction of the optic disc and whether the pathological lesion site is unilateral or bilateral are vitally important clinical data which can affect the diagnosis and determine the relative imaging and/or systematic evaluation. Several studies have shown that DLSs can accurately recognize the right or left eye in photos with optic discs (44,46). Furthermore, it can distinguish optic disc edema from normal optic disc with an average accuracy of 93%, and distinguish real optic disc swelling from pseudo-swelling with an accuracy of about 95%.…”

Diagnostik von Erkrankungen des Sehnervenkopfes in Zeiten von künstlicher Intelligenz und Big Data

“…Mit einem ML-Modell konnten Ahn et al. zwischen geschwollenen Sehnervenköpfen aufgrund unterschiedlicher Optikusneuropathien, Pseudopapillenödemen und gesunden Sehnervenköpfen unterscheiden [ 42 ]. Zusätzlich umgingen die genannten Autoren das Problem eines kleinen Datensatzes.…”