Artificial intelligence applications and cataract management: A systematic review

A cataract is a disease of the 21st century. In highly developed countries, the opacification of the intraocular lens is removed and replaced with an implant. This article discusses the essential aspects related to the construction of implants that imitate the natural lens of the eyeball. The research was carried out owing to X-ray microtomography. Observer design differences in the two lenses' structures contribute to the implant's quality in question. The study aimed to check and compare two types of implants in terms of the design and manufacturing quality of the given lenses. The common element of the implants in question was their material and manufacturer. Lenses of the same material were used to maintain the same optics. The results gave a comparison in terms of the quality of the product in terms of the creation and modelling of the lenses than the properties of the material itself from which they were made.

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“…It has no connecting elements. As it can be expected, the homogeneous surface makes the lens more resistant to mechanical damage [33][34][35].…”

Section: Discussionmentioning

confidence: 79%

Construction aspects of intraocular artificial lenses

Marchewka

Zubrzycki

2022

J. Phys.: Conf. Ser.

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“…Using the scleral spur (SS) as a landmark, it was then possible to quantify the ACA width using parameters previously introduced for the UBM including the angle opening distance (AOD) at 500 μm from the scleral spur, the angle recess area (ARA) and the trabeculo-iris space [ 15 ]. With OCT technology, precise measurements made it possible to measure dynamic changes in the ACA occurring with dark-light variations [ 16 ], quantify ACA morphology modifications after laser peripheral iridotomy [ 17 , 18 ] or phacoemusification [ 19 , 20 ]. While OCT offers non-contact and is less dependent on observer’s training than slit-lamp gonioscopy, it also presents several drawbacks.…”

Section: Methodsmentioning

confidence: 99%

Moving beyond the Slit-Lamp Gonioscopy: Challenges and Future Opportunities

et al. 2021

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After almost a century from its introduction in clinical practice, slit-lamp gonioscopy is still considered the reference standard for evaluating the anterior chamber angle (ACA). Gonioscopy is essential for diagnosing angle closure disease, and ACA features are included in glaucoma’s diagnostics and treatments algorithms. However, shortcomings of slit-lamp gonioscopy include a steep learning curve, lack of agreement between examiners and poor documentation. Thanks to advances in miniaturization and computing, new instruments for digital gonioscopy have been developed and marketed. This narrative review focuses on the Gonioscope GS-1, which permits semi-automated circumferential documentation of the ACA in real-colour photographs. Advantages and disadvantages of GS-1 compared with slit-lamp gonioscopy and other ACA imaging technologies such as optical coherence tomography are discussed. Finally, potential opportunities offered by this device for telemedicine, virtual clinics, and automatic classification with deep learning are presented.

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“…DR, age related macular degeneration (ARMD), retinopathy of prematurity (ROP), glaucoma and cataracts have all been identified as promising disease areas for AI [ 106 ]. A systematic review of AI in the management of people with cataracts found that AI-driven diagnosis was at least comparable, and at times superior to expert clinical diagnosis [ 107 ]. AI has demonstrated utility beyond diagnostic purposes with improved intraoperative lens selection and a subsequently reduced refractive error [ 108 ].…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship

Alam

Anson²,

Meng³

et al. 2022

JCM

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Corneal confocal microscopy (CCM) is a rapid non-invasive in vivo ophthalmic imaging technique that images the cornea. Historically, it was utilised in the diagnosis and clinical management of corneal epithelial and stromal disorders. However, over the past 20 years, CCM has been increasingly used to image sub-basal small nerve fibres in a variety of peripheral neuropathies and central neurodegenerative diseases. CCM has been used to identify subclinical nerve damage and to predict the development of diabetic peripheral neuropathy (DPN). The complex structure of the corneal sub-basal nerve plexus can be readily analysed through nerve segmentation with manual or automated quantification of parameters such as corneal nerve fibre length (CNFL), nerve fibre density (CNFD), and nerve branch density (CNBD). Large quantities of 2D corneal nerve images lend themselves to the application of artificial intelligence (AI)-based deep learning algorithms (DLA). Indeed, DLA have demonstrated performance comparable to manual but superior to automated quantification of corneal nerve morphology. Recently, our end-to-end classification with a 3 class AI model demonstrated high sensitivity and specificity in differentiating healthy volunteers from people with and without peripheral neuropathy. We believe there is significant scope and need to apply AI to help differentiate between peripheral neuropathies and also central neurodegenerative disorders. AI has significant potential to enhance the diagnostic and prognostic utility of CCM in the management of both peripheral and central neurodegenerative diseases.

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Artificial intelligence applications and cataract management: A systematic review

Cited by 20 publications

References 80 publications

Construction aspects of intraocular artificial lenses

Construction aspects of intraocular artificial lenses

Moving beyond the Slit-Lamp Gonioscopy: Challenges and Future Opportunities

Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship

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