Advances in Imaging Technology of Anterior Segment of the Eye

Abstract

Advances in imaging technology and computer science have allowed the development of newer assessment of the anterior segment, including Corvis ST, Brillouin microscopy, ultrahigh-resolution optical coherence tomography, and artificial intelligence. They enable accurate and precise assessment of structural and biomechanical alterations associated with anterior segment disorders. This review will focus on these 4 new techniques, and a brief overview of these modalities will be introduced. The authors will also discuss the recent advances in research regarding these techniques and potential application of these techniques in clinical practice. Many studies on these modalities have reported promising results, indicating the potential for more detailed comprehensive understanding of the anterior segment tissues.

Assessment of corneal biomechanics in anisometropia using Scheimpflug technology

“…Kenia et al ( Kenia et al, 2020 ) found similar correlations between DAR, IR and SE. Vinciguerra screening parameters are a series of newly developed parameters based on an attempt to differentiate normal corneas from keratoconus corneas ( Han et al, 2021 ). Smaller DAR and IR values indicated a softer cornea ( Han et al, 2021 ).…”

“…Vinciguerra screening parameters are a series of newly developed parameters based on an attempt to differentiate normal corneas from keratoconus corneas ( Han et al, 2021 ). Smaller DAR and IR values indicated a softer cornea ( Han et al, 2021 ). However, CCT and bIOP had a greater impact on most corneal deformation parameters ( Table 2 ), which was similar to previous studies ( Sedaghat et al, 2020 ; Yu et al, 2020 ).…”

Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease

“…Advances in ML technology, particularly DL algorithms, have enabled the application of AI in various anterior segment diseases, including DED. AI is expected to be able to integrate the data from medical records, test results, and large population-based studies and provide optimized protocols for the diagnosis and management of DED [ 15 ]. The analysis of data obtained by imaging devices for DED, such as ASP, meibography, tear interferometry, IVCM, and AS-OCT, using AI may enable the precise and accurate assessment of the tests for DED, which would be critical for proper diagnosis and optimal treatment of the disease.…”

“…This diagnostic algorithm showed high sensitivity (86.4%) and specificity (91.7%), suggesting that the integration of OCT corneal epithelial mapping data into a diagnostic algorithm using AI may improve the reliability of the diagnosis of DED [ 117 ]. Although a limited number of studies regarding the application of AI for the analysis of AS-OCT images have been conducted so far, the development of advanced devices, including high-resolution OCT, may enable the assessment of subtle changes in the ocular surface and tear film using AI [ 15 ].…”

“…In ophthalmology, AI has initially been applied for the analysis of fundus photographs and optical coherence tomography (OCT) images; thus, previous studies have mostly focused on the integration of AI into the diagnostic approach of posterior segment diseases, such as diabetic retinopathy, glaucoma, macular degeneration, and retinopathy of prematurity [ 5 , 10 , 11 , 12 , 13 , 14 ]. However, as DL algorithms can be utilized for the analysis of imaging the data of anterior segment structures, such as anterior segment photographs (ASPs), anterior segment OCT (AS-OCT) images, specular microscopy, corneal topography, in vivo confocal microscopy (IVCM), infrared meibography, and tear interferometry [ 2 ], AI is also expected to assist in the diagnosis and monitoring of various anterior segment diseases [ 15 ]. Recently, many studies have been conducted on the application of AI in various anterior segment diseases [ 2 ].…”