Proposing an ensemble learning model based on neural network and fuzzy system for keratoconus diagnosis based on Pentacam measurements

Ghaderi, Maryam; Sharifi, Arash; Pour, Ebrahim Jafarzadeh

doi:10.1007/s10792-021-01963-2

Cited by 9 publications

(8 citation statements)

References 30 publications

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“…In this context, modern topographers, by adding high-resolution AS-OCT to their technology, are able to provide repeatable and reliable corneal, epithelial, and stromal maps, and the variables extracted from them have proven good diagnostic value in the detection of keratoconus 10 and in forme fruste and subclinical keratoconus forms. 7 Thus, considering that previously reported neural networks 3 , 5 , 6 used predominately keratometric and tomographic data, the addition of these new reliable data from combined Placido disc and high-resolution AS-OCT topographers could improve the detection capacity of these classification systems, as we did in the current study. All indexes used to train our ANN ( Table 1 ) showed “excellent” or “outstanding” keratoconus discrimination capacity according to their area under the ROC ( Table 2 ).…”

Section: Discussionmentioning

confidence: 62%

Artificial Neural Network for Automated Keratoconus Detection Using a Combined Placido Disc and Anterior Segment Optical Coherence Tomography Topographer

Alió del Barrio,

Eldanasoury,

Arbelaez

et al. 2024

Trans. Vis. Sci. Tech.

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Purpose To assess the efficacy of an automated program for keratoconus and keratoconus suspect detection based on corneal measurements provided by a combined Placido disc and anterior segment optical coherence tomography (OCT) topographer. Methods In a multicentric cross-sectional study, an artificial neural network (ANN) was created using 6677 eyes from an equal number of patients (classified as 2663 normal eyes, 1616 keratoconus eyes, 210 keratoconus suspect eyes, 1519 myopic postoperative eyes, and 669 abnormal eyes). Each group was randomly divided into a training set (70% of the dataset) and a validation set (the remaining 30%). A multilayer perceptron network with a backpropagation learning algorithm was developed for the study. Indexes used to train the ANN were based on curvature and elevation of both the anterior and posterior corneal surfaces and the new corneal OCT indexes—based on corneal, stromal, and epithelial thicknesses. Results For keratoconus detection, our ANN showed an accuracy of 98.6%, precision of 96%, recall of 97.9%, and F1-score of 96.9%. For keratoconus suspect detection, our ANN showed an accuracy of 98.5%, precision of 83.6%, recall of 69.7%, and F1-score of 76%. Conclusions Compared to previous literature, the addition of new OCT-based epithelial and stromal thickness indexes improves ANN detection capacity of keratoconus suspect eyes. For already stablished keratoconus our ANN detection capacity is excellent, but equivalent to previous evidence without incorporating such new OCT-based indexes. Translational Relevance OCT-based epithelial and stromal thickness indexes improve ANN detection capacity of keratoconus on its early stages.

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Section: Discussionmentioning

confidence: 62%

Artificial Neural Network for Automated Keratoconus Detection Using a Combined Placido Disc and Anterior Segment Optical Coherence Tomography Topographer

Alió del Barrio,

Eldanasoury,

Arbelaez

et al. 2024

Trans. Vis. Sci. Tech.

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“…The model's overall classification accuracy was 97%, highest for stage 4 KCN and lowest for ffKCN. Another study trained an ensemble CNN on tomography measurements to differentiate between normal eyes and early, moderate, and advanced KCN with a staging accuracy of 98% [42]. Two studies used only topography images to detect and stage KCN [43 ▪ ,44].…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence and corneal diseases

Kang¹,

Ballouz²,

Woodward

2022

Current Opinion in Ophthalmology

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Purpose of reviewArtificial intelligence has advanced rapidly in recent years and has provided powerful tools to aid with the diagnosis, management, and treatment of ophthalmic diseases. This article aims to review the most current clinical artificial intelligence applications in anterior segment diseases, with an emphasis on microbial keratitis, keratoconus, dry eye syndrome, and Fuchs endothelial dystrophy.Recent findingsMost current artificial intelligence approaches have focused on developing deep learning algorithms based on various imaging modalities. Algorithms have been developed to detect and differentiate microbial keratitis classes and quantify microbial keratitis features. Artificial intelligence may aid with early detection and staging of keratoconus. Many advances have been made to detect, segment, and quantify features of dry eye syndrome and Fuchs. There is significant variability in the reporting of methodology, patient population, and outcome metrics.SummaryArtificial intelligence shows great promise in detecting, diagnosing, grading, and measuring diseases. There is a need for standardization of reporting to improve the transparency, validity, and comparability of algorithms.

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“…The model's overall classification accuracy was 97%, highest for stage 4 KC and lowest for FFKC. Another study trained an ensemble CNN on Pentacam measurements to differentiate between normal eyes and early, moderate, and advanced KC with a staging accuracy of 98.2% (Ghaderi et al, 2021). Other studies have focused on detecting KC progression, though each study had varying definitions of disease progression.…”

Section: Ai Application In Kcmentioning

confidence: 99%

Artificial intelligence-assisted diagnosis of ocular surface diseases

Zhang

Wang²,

Zhang³

et al. 2023

Front. Cell Dev. Biol.

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With the rapid development of computer technology, the application of artificial intelligence (AI) in ophthalmology research has gained prominence in modern medicine. Artificial intelligence-related research in ophthalmology previously focused on the screening and diagnosis of fundus diseases, particularly diabetic retinopathy, age-related macular degeneration, and glaucoma. Since fundus images are relatively fixed, their standards are easy to unify. Artificial intelligence research related to ocular surface diseases has also increased. The main issue with research on ocular surface diseases is that the images involved are complex, with many modalities. Therefore, this review aims to summarize current artificial intelligence research and technologies used to diagnose ocular surface diseases such as pterygium, keratoconus, infectious keratitis, and dry eye to identify mature artificial intelligence models that are suitable for research of ocular surface diseases and potential algorithms that may be used in the future.

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Proposing an ensemble learning model based on neural network and fuzzy system for keratoconus diagnosis based on Pentacam measurements

Cited by 9 publications

References 30 publications

Artificial Neural Network for Automated Keratoconus Detection Using a Combined Placido Disc and Anterior Segment Optical Coherence Tomography Topographer

Artificial Neural Network for Automated Keratoconus Detection Using a Combined Placido Disc and Anterior Segment Optical Coherence Tomography Topographer

Artificial intelligence and corneal diseases

Artificial intelligence-assisted diagnosis of ocular surface diseases

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