Current state and future prospects of artificial intelligence in ophthalmology: a review

Hogarty, Daniel T; Mackey, David A.; Hewitt, Alex W.

doi:10.1111/ceo.13381

Cited by 138 publications

(98 citation statements)

References 74 publications

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“…As the morphological and hemodynamic parameters of the bulbar conjunctival vessels could potentially be used to access the status of ocular surface diseases, large scale clinical studies will need to be conducted to comprehensively characterize the relations between these quantitative parameters of human bulbar conjunctival microvasculature and differences of ocular diseases. What is more, while artificial intelligence has been recognized to be so promising in ophthalmic disease diagnosis with different ophthalmic images [49][50][51], our multi-modal system can potentially offer a technical basis for providing complementary images for multimodal artificial intelligence applications.…”

Section: Discussionmentioning

confidence: 99%

Multi-modal Anterior Eye Imager Combining Ultra-High Resolution OCT and Microvascular Imaging for Structural and Functional Evaluation of the Human Eye

et al. 2020

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To establish complementary information for the diagnosis and evaluation of ocular surface diseases, we developed a multi-modal, non-invasive optical imaging platform by combining ultra-high resolution optical coherence tomography (UHR-OCT) with a microvascular imaging system based on slit-lamp biomicroscopy. Our customized UHR-OCT module achieves an axial resolution of ≈2.9 µm in corneal tissue with a broadband light source and an A-line acquisition rate of 24 kHz with a line array CCD camera. The microvascular imaging module has a lateral resolution of 3.5 µm under maximum magnification of ≈187.5× with an imaging rate of 60 frames/s, which is sufficient to image the conjunctival vessel network and record the movement trajectory of clusters of red blood cells. By combining the imaging optical paths of different modules, our customized multi-modal anterior eye imaging platform is capable of performing real-time cross-sectional UHR-OCT imaging of the anterior eye, conjunctival vessel network imaging, high-resolution conjunctival blood flow videography, fluorescein staining and traditional slit-lamp imaging on a single device. With self-developed software, a conjunctival vessel network image and blood flow videography were further analyzed to acquire quantitative morphological and hemodynamics parameters, including vessel fractal dimensions, blood flow velocity and vessel diameters. The ability of our multi-modal anterior eye imager to provide both structural and functional information for ophthalmic clinical applications was demonstrated on a healthy human subject and a keratitis patient. evaluation of ocular surface diseases, different imaging modalities are usually applied in ophthalmic clinics to provide multi-modal and multi-dimensional images [7,8].Slit-lamp biomicroscopy (SLB) is the gold standard for routine ophthalmic examinations. By shining a thin sheet of light into the eye, an in vivo stereoscopic evaluation of the pathological changes of the cornea, conjunctiva and other anterior segment tissues can be performed by SLB [9]. However, the limited magnification and poor spatial resolution of traditional SLB make it difficult to visualize more detailed pathological structures and perform accurate biometric measurements [10][11][12]. Fluorescein stain could stain stroma while leaving the intact epithelium with a specific fluorescein dye, thereby demarcating the location and area of the epithelial loss in ocular surface diseases, on a relative macro scale as well [13]. Developed in the 1990s, optical coherence tomography (OCT) has revolutionized the field of ophthalmology and vision science with its non-contact, non-invasive and high-resolution nature and become one of the prominent diagnostic tools in offering cross-sectional tissue imaging [14][15][16]. Applications of OCT for in vivo human eye imaging have been widely demonstrated in the literature [17][18][19][20][21][22][23][24][25][26][27][28]. With ever-improving techniques in broadband light sources, ultra-high resolution OCT (UHR-OCT) has achieved m...

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Section: Discussionmentioning

confidence: 99%

Multi-modal Anterior Eye Imager Combining Ultra-High Resolution OCT and Microvascular Imaging for Structural and Functional Evaluation of the Human Eye

et al. 2020

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“…Very soon, patients will routinely be taken a non-mydriatic fundus photograph at the pre-exam room by an ophthalmic technician allowing the accurate recognition of many systemic associated and primary ocular disorders. Image pattern recognition is the basis of this technology, which requires a large number of fundus photographs to learn from (training dataset) as well as a separate database for validation (validation dataset) [12]. This technology may be coupled with imaging diagnostic devices, such as a fundus camera with fluorescein, indocyanine green, and autofluorescence capabilities; SD-OCT, swept soured OCT, OCT-A, corneal topography, visual system aberrometry and wavefront imaging, anterior segment tomography, and ultrasound, among others, for the detection of specific diagnoses.…”

Section: Advances In Diagnosis Of Uveitismentioning

confidence: 99%

Introductory Chapter: Current and Future Trends in the Diagnosis and Management of Uveitis

Rodríguez-García¹,

Foster²

2019

Advances in the Diagnosis and Management of Uveitis

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“…Optical imaging technologies play a vital role in clinical diagnosis and treatment of ophthalmology [1,2]. Computational vision approaches for automatic diagnosis of lens opacity have greatly improved the efficiency of ophthalmologists and the entire treatment chain, providing real benefits for patients [3][4][5][6]. In our previous studies, we applied artificial intelligence methods to the classification of diffuse-light ocular images [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Heterogeneous Slit-Illumination Images Using an Ensemble of Cost-Sensitive Convolutional Neural Networks

Jiang

Wang

et al. 2020

Preprint

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Background: Lens opacity seriously affects the visual development of infants. Slit-illumination images play an irreplaceable role in lens opacity detection; however, these images exhibited varied phenotypes with severe heterogeneity and complexity, particularly among pediatric cataracts. Therefore, it is urgently needed to explore an effective computer-aided method to automatically diagnose heterogeneous lens opacity and to provide appropriate treatment recommendations in a timely manner.Methods: We integrated three different deep learning networks and a cost-sensitive method into an ensemble learning architecture, and then proposed an effective model called ECCNN-Ensemble (ensemble of cost-sensitive convolutional neural networks) for automatic lens opacity detection. A total of 470 slit-illumination images of pediatric cataracts were used for training and comparison between the CCNN-Ensemble model and conventional methods. Finally, we used two external datasets (132 independent test images and 79 Internet-based images) to further evaluate the model’s generalizability and effectiveness.Results: Experimental results and comparative analyses demonstrated that the proposed method was superior to conventional approaches and provided clinically meaningful performance in terms of three grading indices of lens opacity: area (specificity and sensitivity; 92.00% and 92.31%), density (93.85% and 91.43%) and opacity location (95.25% and 89.29%). Furthermore, the comparable performance on the independent testing dataset and the internet-based images verified the effectiveness and generalizability of the model. Finally, we developed and implemented a website-based automatic diagnosis software for pediatric cataract grading diagnosis in ophthalmology clinics. Conclusions: The CCNN-Ensemble method demonstrates higher specificity and sensitivity than conventional methods on multi-source datasets. This study therefore provides a practical strategy for heterogeneous lens opacity diagnosis and has the potential to be applied to the analysis of other medical images.

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Current state and future prospects of artificial intelligence in ophthalmology: a review

Cited by 138 publications

References 74 publications

Multi-modal Anterior Eye Imager Combining Ultra-High Resolution OCT and Microvascular Imaging for Structural and Functional Evaluation of the Human Eye

Multi-modal Anterior Eye Imager Combining Ultra-High Resolution OCT and Microvascular Imaging for Structural and Functional Evaluation of the Human Eye

Introductory Chapter: Current and Future Trends in the Diagnosis and Management of Uveitis

Automatic Classification of Heterogeneous Slit-Illumination Images Using an Ensemble of Cost-Sensitive Convolutional Neural Networks

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Current state and future prospects of artificial intelligence in ophthalmology: a review

Cited by 138 publications

References 74 publications

Multi-modal Anterior Eye Imager Combining Ultra-High Resolution OCT and Microvascular Imaging for Structural and Functional Evaluation of the Human Eye

Multi-modal Anterior Eye Imager Combining Ultra-High Resolution OCT and Microvascular Imaging for Structural and Functional Evaluation of the Human Eye

Introductory Chapter: Current and Future Trends in the Diagnosis and Management of Uveitis

Automatic Classification of Heterogeneous&nbsp;Slit-Illumination Images Using an Ensemble of Cost-Sensitive Convolutional Neural Networks

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Automatic Classification of Heterogeneous Slit-Illumination Images Using an Ensemble of Cost-Sensitive Convolutional Neural Networks