OCTAI: Smartphone-based Optical Coherence Tomography Image Analysis System

2021 ◽  
Author(s):  
Adrit Rao ◽  
Harvey A. Fishman
Keyword(s):  
Optical Coherence Tomography ◽  
Image Analysis ◽  
Deep Learning ◽  
Early Stage ◽  
Real Life ◽  
Learning System ◽  
Optical Coherence Tomography Image ◽  
Image Analysis System ◽  
Optical Coherence ◽  
Oct Image

Identifying diseases in Optical Coherence Tomography (OCT) images using Deep Learning models and methods is emerging as a powerful technique to enhance clinical diagnosis. Identifying macular diseases in the eye at an early stage and preventing misdiagnosis is crucial. The current methods developed for OCT image analysis have not yet been integrated into an accessible form-factor that can be utilized in a real-life scenario by Ophthalmologists. Additionally, current methods do not employ robust multiple metric feedback. This paper proposes a highly accurate smartphone-based Deep Learning system, OCTAI, that allows a user to take an OCT picture and receive real-time feedback through on-device inference. OCTAI analyzes the input OCT image in three different ways: (1) full image analysis, (2) quadrant based analysis, and (3) disease detection based analysis. With these three analysis methods, along with an Ophthalmologist's interpretation, a robust diagnosis can potentially be made. The ultimate goal of OCTAI is to assist Ophthalmologists in making a diagnosis through a digital second opinion and enabling them to cross-check their diagnosis before making a decision based on purely manual analysis of OCT images. OCTAI has the potential to allow Ophthalmologists to improve their diagnosis and may reduce misdiagnosis rates, leading to faster treatment of diseases.

scholarly journals Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images

2020 ◽  
pp. bjophthalmol-2020-317825
Author(s):  
Yonghao Li ◽  
Weibo Feng ◽  
Xiujuan Zhao ◽  
Bingqian Liu ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
High Myopia ◽  
Large Scale ◽  
Learning System ◽  
Youden Index ◽  
Optical Coherence ◽  
Test Dataset ◽  
Independent Test ◽  
Independent Test Dataset

Background/aimsTo apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images.MethodsIn this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index.ResultsIn the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps.ConclusionsWe used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

Optical coherence tomography image analysis of polymer surface layers in sound-absorbing fibrous composite materials

2017 ◽  
Vol 63 ◽  
pp. 194-203 ◽  
Author(s):  
Eulalia Gliścińska ◽  
Dominik Sankowski ◽  
Izabella Krucińska ◽  
Jarosław Gocławski ◽  
Marina Michalak ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Image Analysis ◽  
Composite Materials ◽  
Fibrous Composite ◽  
Polymer Surface ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Surface Layers ◽  
Tomography Image

Discriminating glaucomatous and compressive optic neuropathy on spectral-domain optical coherence tomography with deep learning classifier

2020 ◽  
Vol 104 (12) ◽  
pp. 1717-1723 ◽  
Author(s):  
Jinho Lee ◽  
Jin-Soo Kim ◽  
Haeng Jin Lee ◽  
Seong-Joon Kim ◽  
Young Kook Kim ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Optic Neuropathy ◽  
Spectral Domain ◽  
Optical Coherence ◽  
Compressive Optic Neuropathy ◽  
Diagnostic Parameters ◽  
Learning Classifier ◽  
Oct Image ◽  
Sd Oct

Background/aimsTo assess the performance of a deep learning classifier for differentiation of glaucomatous optic neuropathy (GON) from compressive optic neuropathy (CON) based on ganglion cell–inner plexiform layer (GCIPL) and retinal nerve fibre layer (RNFL) spectral-domain optical coherence tomography (SD-OCT).MethodsEighty SD-OCT image sets from 80 eyes of 80 patients with GON along with 81 SD-OCT image sets from 54 eyes of 54 patients with CON were compiled for the study. The bottleneck features extracted from the GCIPL thickness map, GCIPL deviation map, RNFL thickness map and RNFL deviation map were used as predictors for the deep learning classifier. The area under the receiver operating characteristic curve (AUC) was calculated to validate the diagnostic performance. The AUC with the deep learning classifier was compared with those for conventional diagnostic parameters including temporal raphe sign, SD-OCT thickness profile and standard automated perimetry.ResultsThe deep learning system achieved an AUC of 0.990 (95% CI 0.982 to 0.999) with a sensitivity of 97.9% and a specificity of 92.6% in a fivefold cross-validation testing, which was significantly larger than the AUCs with the other parameters: 0.804 (95% CI 0.737 to 0.872) with temporal raphe sign, 0.815 (95% CI 0.734 to 0.896) with superonasal GCIPL and 0.776 (95% CI 0.691 to 0.860) with superior GCIPL thicknesses (all p<0.001).ConclusionThe deep learning classifier can outperform the conventional diagnostic parameters for discrimination of GON and CON on SD-OCT.

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