Using Deep Learning in Automated Detection of Graft Detachment in Descemet Membrane Endothelial Keratoplasty: A Pilot Study

Treder, Maximilian; Lauermann, Jost Lennart; Alnawaiseh, Maged; Eter, Nicole

doi:10.1097/ico.0000000000001776

Cited by 46 publications

(35 citation statements)

References 24 publications

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“…We tested the algorithm using 5-fold cross-validation on the dataset containing 11,340 images, maintaining the proportion of samples of each class per fold. This testing process trained 5 distinct algorithms with 9180 (80%) of these images (3420 healthy; 3060 early-stage FECD 2700 late-stage FECD), each holding off a discrete validation block of 2160 (20%) images (720 healthy; 720 early-stage FECD; 720 late-stage FECD) [ 32 , 38 , 39 ]. Mean parameters were calculated from 5 test runs on the corresponding held-off data by comparing the model’s predictions against the ground truth as determined by cornea specialists.…”

Section: Methodsmentioning

confidence: 99%

“…The models are neural networks that are constructed of an input layer (which receives, for example, the OCT image), followed by multiple layers of nonlinear transformations to produce an output (e.g., FECD present or absent) [ 28 ]. So far, few studies have focused specifically on automated detection of corneal disease using AS-OCT compared to retinal diseases and glaucoma [ 32 ]. To the best of our knowledge, there is no report on the use of deep learning for diagnosing FECD.…”

Section: Introductionmentioning

confidence: 99%

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Automated diagnosis and staging of Fuchs’ endothelial cell corneal dystrophy using deep learning

et al. 2020

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Background To describe the diagnostic performance of a deep learning algorithm in discriminating early-stage Fuchs’ endothelial corneal dystrophy (FECD) without clinically evident corneal edema from healthy and late-stage FECD eyes using high-definition optical coherence tomography (HD-OCT). Methods In this observational case-control study, 104 eyes (53 FECD eyes and 51 healthy controls) received HD-OCT imaging (Envisu R2210, Bioptigen, Buffalo Grove, IL, USA) using a 6 mm radial scan pattern centered on the corneal vertex. FECD was clinically categorized into early (without corneal edema) and late-stage (with corneal edema). A total of 18,720 anterior segment optical coherence tomography (AS-OCT) images (9180 healthy; 5400 early-stage FECD; 4140 late-stage FECD) of 104 eyes (81 patients) were used to develop and validate a deep learning classification network to differentiate early-stage FECD eyes from healthy eyes and those with clinical edema. Using 5-fold cross-validation on the dataset containing 11,340 OCT images (63 eyes), the network was trained with 80% of these images (3420 healthy; 3060 early-stage FECD; 2700 late-stage FECD), then tested with 20% (720 healthy; 720 early-stage FECD; 720 late-stage FECD). Thereafter, a final model was trained with the entire dataset consisting the 11,340 images and validated with a remaining 7380 images of unseen AS-OCT scans of 41 eyes (5040 healthy; 1620 early-stage FECD 720 late-stage FECD). Visualization of learned features was done, and area under curve (AUC), specificity, and sensitivity of the prediction outputs for healthy, early and late-stage FECD were computed. Results The final model achieved an AUC of 0.997 ± 0.005 with 91% sensitivity and 97% specificity in detecting early-FECD; an AUC of 0.974 ± 0.005 with a specificity of 92% and a sensitivity up to 100% in detecting late-stage FECD; and an AUC of 0.998 ± 0.001 with a specificity 98% and a sensitivity of 99% in discriminating healthy corneas from all FECD. Conclusion Deep learning algorithm is an accurate autonomous novel diagnostic tool of FECD with very high sensitivity and specificity that can be used to grade FECD severity with high accuracy.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated diagnosis and staging of Fuchs’ endothelial cell corneal dystrophy using deep learning

et al. 2020

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“…Prior image analysis work within the realm of DMEK detachment has only focused on binary classification; i.e. whether detachment is present or not 34 and whether rebubbling was performed. 35 We believe our detachment model is of clinical value as it provides quantitative measures about length and location of graft detachment.…”

Section: Discussionmentioning

confidence: 99%

Quantifying Graft Detachment after Descemet's Membrane Endothelial Keratoplasty with Deep Convolutional Neural Networks

Heslinga

Alberti

Pluim

et al. 2020

Trans. Vis. Sci. Tech.

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We developed a method to automatically locate and quantify graft detachment after Descemet's membrane endothelial keratoplasty (DMEK) in anterior segment optical coherence tomography (AS-OCT) scans. Methods: A total of 1280 AS-OCT B-scans were annotated by a DMEK expert. Using the annotations, a deep learning pipeline was developed to localize scleral spur, center the AS-OCT B-scans and segment the detached graft sections. Detachment segmentation model performance was evaluated per B-scan by comparing (1) length of detachment and (2) horizontal projection of the detached sections with the expert annotations. Horizontal projections were used to construct graft detachment maps. All final evaluations were done on a test set that was set apart during training of the models. A second DMEK expert annotated the test set to determine interrater performance. Results: Mean scleral spur localization error was 0.155 mm, whereas the interrater difference was 0.090 mm. The estimated graft detachment lengths were in 69% of the cases within a 10-pixel (∼150 μm) difference from the ground truth (77% for the second DMEK expert). Dice scores for the horizontal projections of all B-scans with detachments were 0.896 and 0.880 for our model and the second DMEK expert, respectively. Conclusions: Our deep learning model can be used to automatically and instantly localize graft detachment in AS-OCT B-scans. Horizontal detachment projections can be determined with the same accuracy as a human DMEK expert, allowing for the construction of accurate graft detachment maps. Translational Relevance: Automated localization and quantification of graft detachment can support DMEK research and standardize clinical decision-making.

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“…The identification of a larger residual interface fluid volume by the automated graph searching approach is associated with early graft dislocation [57]. On the postoperative period, the graft dislocation can also be objectively quantified using a convolutional neural network [58].…”

Section: Corneal Surgerymentioning

confidence: 99%

Artificial Intelligence in Corneal Diagnosis: Where Are we?

Lopes

Eliasy

Ambrósio

2019

Curr Ophthalmol Rep

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Purpose of Review In this paper, we prospectively review some artificial intelligence (AI) techniques and models used to enhance clinical decisions for patients with corneal diseases and conditions. Recent Findings Cornea subspeciality was a pioneer in aggregating technology to clinical practice. It provided the ability to early diagnose diseases and improve treatments. Currently, we face the challenge of dealing with a tremendous amount of information from complementary multimodal imaging devices. The analysis of such data for enhancing clinical decisions is perfectly suitable for AI. While AI models are rapidly growing in various fields, some are already available and in use by clinicians, for instance to help in refractive surgery screening. Summary AI models represent a boundless method for helping to deal with and avoid the overload of an extraordinary amount of information provided by advances in complementary diagnosis. It is currently ready to use in refractive surgery screening. The challenge is to coordinate multicentre collaborations in order to build good quality and large data collection to train and improve AI models. AI is an instrument to upturn clinical decision power with many possible applications for ophthalmologists.

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Using Deep Learning in Automated Detection of Graft Detachment in Descemet Membrane Endothelial Keratoplasty: A Pilot Study

Cited by 46 publications

References 24 publications

Automated diagnosis and staging of Fuchs’ endothelial cell corneal dystrophy using deep learning

Automated diagnosis and staging of Fuchs’ endothelial cell corneal dystrophy using deep learning

Quantifying Graft Detachment after Descemet's Membrane Endothelial Keratoplasty with Deep Convolutional Neural Networks

Artificial Intelligence in Corneal Diagnosis: Where Are we?

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