Automated cornea diagnosis using deep convolutional neural networks based on cornea topography maps

Fassbind, Benjamin; Langenbucher, Achim; Streich, Andreas

doi:10.1038/s41598-023-33793-w

Cited by 8 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The majority of the included studies regarding keratoconus or corneal ectasia diagnosis with AI used data from corneal topography ( 47 , 51 – 53 , 56 , 57 , 67 , 73 , 74 , 77 , 78 , 83 , 91 , 92 , 94 , 100 ), Scheimpflug-based tomography ( 43 , 45 , 49 , 54 , 55 , 59 , 60 , 62 , 64 , 66 , 70 , 76 , 79 , 80 , 84 , 86 – 88 , 90 , 95 , 99 ), or optical coherence tomography (OCT) ( 31 , 46 , 50 , 53 , 68 , 69 , 72 , 80 , 84 ) as the input. For example, de Almeida et al.…”

Section: Resultsmentioning

confidence: 99%

Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review

Goodman,

Zhu

2024

Front. Ophthalmol.

View full text Add to dashboard Cite

IntroductionThe application of artificial intelligence (AI) systems in ophthalmology is rapidly expanding. Early detection and management of keratoconus is important for preventing disease progression and the need for corneal transplant. We review studies regarding the utility of AI in the diagnosis and management of keratoconus and other corneal ectasias.MethodsWe conducted a systematic search for relevant original, English-language research studies in the PubMed, Web of Science, Embase, and Cochrane databases from inception to October 31, 2023, using a combination of the following keywords: artificial intelligence, deep learning, machine learning, keratoconus, and corneal ectasia. Case reports, literature reviews, conference proceedings, and editorials were excluded. We extracted the following data from each eligible study: type of AI, input used for training, output, ground truth or reference, dataset size, availability of algorithm/model, availability of dataset, and major study findings.ResultsNinety-three original research studies were included in this review, with the date of publication ranging from 1994 to 2023. The majority of studies were regarding the use of AI in detecting keratoconus or subclinical keratoconus (n=61). Among studies regarding keratoconus diagnosis, the most common inputs were corneal topography, Scheimpflug-based corneal tomography, and anterior segment-optical coherence tomography. This review also summarized 16 original research studies regarding AI-based assessment of severity and clinical features, 7 studies regarding the prediction of disease progression, and 6 studies regarding the characterization of treatment response. There were only three studies regarding the use of AI in identifying susceptibility genes involved in the etiology and pathogenesis of keratoconus.DiscussionAlgorithms trained on Scheimpflug-based tomography seem promising tools for the early diagnosis of keratoconus that can be particularly applied in low-resource communities. Future studies could investigate the application of AI models trained on multimodal patient information for staging keratoconus severity and tracking disease progression.

show abstract

Section: Resultsmentioning

confidence: 99%

Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review

Goodman,

Zhu

2024

Front. Ophthalmol.

View full text Add to dashboard Cite

show abstract

“…Some research work has been done based on transfer learning for classifying KCN. These studies [14][15][16][17][18][19][20][21][22][23][24] have used different pretrained convolutional neural network (CNN) models such as VGG16, VGG19, InceptionV3, ResNet152, InceptionResNetV2, SqueezeNet, AlexNet, ShuffleNet and MobileNetv2. In Al-Timemy et al, 14 the authors introduced a method called dnsemble of deep transfer learning (EDTL) to detect KCN.…”

Section: Related Workmentioning

confidence: 99%

“…The results showed a sensitivity of 83.3% and specificity of 92.59% for the normal category, sensitivity of 80.64% and specificity of 93.75% for the initial category, sensitivity of 92.59% and specificity of 96.15% for the medium category, and sensitivity of 92.59% and specificity of 98.68% for the severe category. In Fassbind et al , 23 the objective of the study was to predict the most common corneal diseases using CNNs and evaluated with a 95.45% accuracy for two classes and a 93.52% accuracy for five classes. The dataset used in the study consisted of 1940 cornea scans, and there were two classes being classified.…”

Section: Introductionmentioning

confidence: 99%

Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach

Yaraghi,

Khatibi

2024

BMJ Open Ophth

View full text Add to dashboard Cite

ObjectiveOur objective is to develop a novel keratoconus image classification system that leverages multiple pretrained models and a transformer architecture to achieve state-of-the-art performance in detecting keratoconus.Methods and analysisThree pretrained models were used to extract features from the input images. These models have been trained on large datasets and have demonstrated strong performance in various computer vision tasks.The extracted features from the three pretrained models were fused using a feature fusion technique. This fusion aimed to combine the strengths of each model and capture a more comprehensive representation of the input images. The fused features were then used as input to a vision transformer, a powerful architecture that has shown excellent performance in image classification tasks. The vision transformer learnt to classify the input images as either indicative of keratoconus or not.The proposed method was applied to the Shahroud Cohort Eye collection and keratoconus detection dataset. The performance of the model was evaluated using standard evaluation metrics such as accuracy, precision, recall and F1 score.ResultsThe research results demonstrated that the proposed model achieved higher accuracy compared with using each model individually.ConclusionThe findings of this study suggest that the proposed approach can significantly improve the accuracy of image classification models for keratoconus detection. This approach can serve as an effective decision support system alongside physicians, aiding in the diagnosis of keratoconus and potentially reducing the need for invasive procedures such as corneal transplantation in severe cases.

show abstract

“…In contrast, deep learning (DL) methods provide a way of both performing feature extraction and classification of images; thus, reducing the need for hand crafted features. DL methods [9][10][11][12] have previously been applied to corneal topography [9,[13][14][15][16][17] and optical coherence tomography (OCT) imaging [18] for KCN detection.…”

Section: Introductionmentioning

confidence: 99%

A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning

et al. 2023

View full text Add to dashboard Cite

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with 213 eyes examined in Iraq and obtained AUCs of 0.91–0.92 and an accuracy range of 88–92%. The proposed model is a step toward improving the detection of clinical and subclinical forms of KCN.

show abstract

Automated cornea diagnosis using deep convolutional neural networks based on cornea topography maps

Cited by 8 publications

References 28 publications

Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review

Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review

Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach

A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning

Contact Info

Product

Resources

About