Clinically relevant deep learning for detection and quantification of geographic atrophy from optical coherence tomography: a model development and external validation study

BackgroundThe ability of deep learning (DL) algorithms to identify eyes with neovascular age-related macular degeneration (nAMD) from optical coherence tomography (OCT) scans has been previously established. We herewith evaluate the ability of a DL model, showing excellent performance on a Korean data set, to generalse onto an American data set despite ethnic differences. In addition, expert graders were surveyed to verify if the DL model was appropriately identifying lesions indicative of nAMD on the OCT scans.MethodsModel development data set—12 247 OCT scans from South Korea; external validation data set—91 509 OCT scans from Washington, USA. In both data sets, normal eyes or eyes with nAMD were included. After internal testing, the algorithm was sent to the University of Washington, USA, for external validation. Area under the receiver operating characteristic curve (AUC) and precision–recall curve (AUPRC) were calculated. For model explanation, saliency maps were generated using Guided GradCAM.ResultsOn external validation, AUC and AUPRC remained high at 0.952 (95% CI 0.942 to 0.962) and 0.891 (95% CI 0.875 to 0.908) at the individual level. Saliency maps showed that in normal OCT scans, the fovea was the main area of interest; in nAMD OCT scans, the appropriate pathological features were areas of model interest. Survey of 10 retina specialists confirmed this.ConclusionOur DL algorithm exhibited high performance for nAMD identification in a Korean population, and generalised well to an ethnically distinct, American population. The model correctly focused on the differences within the macular area to extract features associated with nAMD.

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Deep learning to detect optical coherence tomography-derived diabetic macular edema from retinal photographs: a multicenter validation study

Ophthalmology Retina ◽

10.1016/j.oret.2021.12.021 ◽

2022 ◽

Author(s):

Xinle Liu ◽

Tayyeba K. Ali ◽

Preeti Singh ◽

Ami Shah ◽

Scott Mayer McKinney ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Macular Edema ◽

Diabetic Macular Edema ◽

Validation Study ◽

Optical Coherence ◽

Retinal Photographs ◽

Multicenter Validation

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Deep Learning Model Based on Optical Coherence Tomography for Automated Detection of Pathologic Myopia: Prediction Model Development Study (Preprint)

10.2196/preprints.34056 ◽

2021 ◽

Author(s):

So Jin Park ◽

Tae Hoon Ko ◽

Chan Kee Park ◽

Yong Chan Kim ◽

In Young Choi

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Characteristic Curve ◽

Model Development ◽

Learning Model ◽

Pathologic Myopia ◽

Optical Coherence ◽

Timely Manner ◽

Model Based ◽

Deep Learning Model

BACKGROUND Pathologic myopia is a disease that causes vision impairment and blindness. Therefore, it is essential to diagnose it in a timely manner. However, there is no standardized definition for pathologic myopia, and the interpretation of pathologic myopia by optical coherence tomography is subjective and requires considerable time and money. Therefore, there is a need for a diagnostic tool that can diagnose pathologic myopia in patients automatically and in a timely manner. OBJECTIVE The purpose of this study was to develop an algorithm that uses optical coherence tomography (OCT) to automatically diagnose patients with pathologic myopia who require treatment. METHODS This study was conducted using patient data from patients who underwent optical coherence tomography tests at the Ophthalmology Department of Incheon St. Mary's Hospital and Seoul St. Mary's Hospital from January 2012 to May 2020. To automatically diagnose pathologic myopia, a deep learning model was developed using 3D optical coherence tomography images. A model was developed using transfer learning based on four pre-trained convolutional neural networks (ResNet18, ResNext50, EfficientNetB0, EfficientNetB4). The performance of each model was evaluated and compared based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). RESULTS Four models developed using test datasets were evaluated and compared. The model based on EfficientNetB4 showed the best performance (95% accuracy, 93% sensitivity, 96% specificity, and 98% AUROC). CONCLUSIONS In our study, we developed a deep learning model that can automatically diagnose pathologic myopia without segmentation of 3D optical coherence tomography images. Our deep learning model based on EfficientNetB4 demonstrated excellent performance in identifying pathologic myopia.

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Deep learning based models to study the effect of glaucoma genes on angle dysgenesis in-vivo

10.1101/2021.12.18.21267894 ◽

2021 ◽

Author(s):

Viney Gupta ◽

Shweta Birla ◽

Toshit Varshney ◽

Bindu I Somarajan ◽

Shikha Gupta ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Trabecular Meshwork ◽

External Validation ◽

Anterior Segment ◽

Gene Mutations ◽

Validation Dataset ◽

Healthy Controls ◽

Optical Coherence ◽

Learning Models

Abstract Objective: To predict the presence of Angle Dysgenesis on Anterior Segment Optical Coherence Tomography (ADoA) using deep learning and to correlate ADoA with mutations in known glaucoma genes. Design: A cross-sectional observational study. Participants: Eight hundred, high definition anterior segment optical coherence tomography (ASOCT) B-scans were included, out of which 340 images (One scan per eye) were used to build the machine learning (ML) model and the rest were used for validation of ADoA. Out of 340 images, 170 scans included PCG (n=27), JOAG (n=86) and POAG (n=57) eyes and the rest were controls. The genetic validation dataset consisted of another 393 images of patients with known mutations compared with 320 images of healthy controls Methods: ADoA was defined as the absence of Schlemm's canal(SC), the presence of extensive hyper-reflectivity over the region of trabecular meshwork or a hyper-reflective membrane (HM) over the region of the trabecular meshwork. Deep learning was used to classify a given ASOCT image as either having angle dysgenesis or not. ADoA was then specifically looked for, on ASOCT images of patients with mutations in the known genes for glaucoma (MYOC, CYP1B1, FOXC1 and LTBP2). Main Outcome measures: Using Deep learning to identify ADoA in patients with known gene mutations. Results: Our three optimized deep learning models showed an accuracy > 95%, specificity >97% and sensitivity >96% in detecting angle dysgenesis on ASOCT in the internal test dataset. The area under receiver operating characteristic (AUROC) curve, based on the external validation cohort were 0.91 (95% CI, 0.88 to 0.95), 0.80 (95% CI, 0.75 to 0.86) and 0.86 (95% CI, 0.80 to 0.91) for the three models. Amongst the patients with known gene mutations, ADoA was observed among all the patients with MYOC mutations, as it was also observed among those with CYP1B1, FOXC1 and with LTBP2 mutations compared to only 5% of those healthy controls (with no glaucoma mutations). Conclusions: Three deep learning models were developed for a consensus-based outcome to objectively identify ADoA among glaucoma patients. All patients with MYOC mutations had ADoA as predicted by the models.

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640 THE ARGOS PROJECT: FIRST DEEP LEARNING ALGORITHM FOR DETECTION OF BARRETT’S NEOPLASIA OUTPERFORMS CONVENTIONAL COMPUTER AIDED DETECTION SYSTEMS IN A MULTI-STEP TRANING AND EXTERNAL VALIDATION STUDY

Gastrointestinal Endoscopy ◽

10.1016/j.gie.2019.04.094 ◽

2019 ◽

Vol 89 (6) ◽

pp. AB99 ◽

Cited By ~ 1

Author(s):

Jeroen de Groof ◽

Maarten R. Struyvenberg ◽

Joost van der Putten ◽

Fons van der Sommen ◽

Wouter Curvers ◽

...

Keyword(s):

Deep Learning ◽

Validation Study ◽

Learning Algorithm ◽

External Validation ◽

Computer Aided Detection ◽

Detection Systems ◽

Deep Learning Algorithm ◽

External Validation Study ◽

Computer Aided ◽

Conventional Computer

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Development and Validation of a Deep Learning System for Diagnosing Glaucoma Using Optical Coherence Tomography

Journal of Clinical Medicine ◽

10.3390/jcm9072167 ◽

2020 ◽

Vol 9 (7) ◽

pp. 2167

Author(s):

Ko Eun Kim ◽

Joon Mo Kim ◽

Ji Eun Song ◽

Changwon Kee ◽

Jong Chul Han ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

External Validation ◽

Learning System ◽

Validation Dataset ◽

Inner Plexiform Layer ◽

Optical Coherence ◽

Diagnostic Ability ◽

Rnfl Thickness ◽

Validation Set

This study aimed to develop and validate a deep learning system for diagnosing glaucoma using optical coherence tomography (OCT). A training set of 1822 eyes (332 control, 1490 glaucoma) with 7288 OCT images, an internal validation set of 425 eyes (104 control, 321 glaucoma) with 1700 images, and an external validation set of 355 eyes (108 control, 247 glaucoma) with 1420 images were included. Deviation and thickness maps of retinal nerve fiber layer (RNFL) and ganglion cell–inner plexiform layer (GCIPL) analyses were used to develop the deep learning system for glaucoma diagnosis based on the visual geometry group deep convolutional neural network (VGG-19) model. The diagnostic abilities of deep learning models using different OCT maps were evaluated, and the best model was compared with the diagnostic results produced by two glaucoma specialists. The glaucoma-diagnostic ability was highest when the deep learning system used the RNFL thickness map alone (area under the receiver operating characteristic curve (AUROC) 0.987), followed by the RNFL deviation map (AUROC 0.974), the GCIPL thickness map (AUROC 0.966), and the GCIPL deviation map (AUROC 0.903). Among combination sets, use of the RNFL and GCIPL deviation map showed the highest diagnostic ability, showing similar results when tested via an external validation dataset. The inclusion of the axial length did not significantly affect the diagnostic performance of the deep learning system. The location of glaucomatous damage showed generally high level of agreement between the heatmap and the diagnosis of glaucoma specialists, with 90.0% agreement when using the RNFL thickness map and 88.0% when using the GCIPL thickness map. In conclusion, our deep learning system showed high glaucoma-diagnostic abilities using OCT thickness and deviation maps. It also showed detection patterns similar to those of glaucoma specialists, showing promising results for future clinical application as an interpretable computer-aided diagnosis.

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