Effects of Different Types of Primary Pterygium on Changes in Oculovisual Function

Hilmi, Mohd Radzi; Kamal, Khairidzan Mohd; Ariffin, Azrin Esmady; Norazmar, Nur Ain; Maruziki, Nur Nabilah; Musa, Nur Hidayah; Nasir, Mardhiah Syazwani; Azemin, Mohd Zulfaezal Che; Azami, Muhammad Hanafi; Rahim, Muhammad Afzam Shah Abdul

doi:10.17576/jsm-2020-4902-16

Cited by 6 publications

(11 citation statements)

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“…Previous theories mainly focusing on pterygium size, width, and length are inadequate to explain the changes that may occur over the course of pterygium progression. 11,41,44 The results of this study indicate that the combination of a corneo-pterygium approach and a good understanding of pterygium grades based on the translucence of the growth could help predict the changes induced by pterygium on the corneal surface.…”

Section: Discussionmentioning

confidence: 89%

“…However, any obstruction during corneal healing process could cause corneal irregularity and reductions in visual function. 44 The findings suggest that corneal stabilization occurs because of two reasons: pterygium morphology and mechanical-traction on the anterior corneal surface due to pterygium progression. First, the pterygium morphology could affect the corneal stabilization as pterygium fibrovascular tissue could present as a thick pterygium tissue and this could change the curvature of the anterior corneal surface.…”

Section: Discussionmentioning

confidence: 91%

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Pterygium recurrence and corneal stabilization point after pterygium excision using the controlled partial avulsion fibrin glue technique

2020

Makara J Health Res

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Background: This study aimed to evaluate the pterygium recurrence rate and corneal stabilization point after pterygium excision via the controlled partial avulsion fibrin glue technique using multiple corneal parameters. Methods: One hundred eyes of 100 patients who had undergone primary pterygium excision surgery via the controlled partial avulsion fibrin glue technique were retrospectively reviewed. Corneal stabilization points were determined over four follow-up sessions (i.e., the 1st, 3rd, 6th, and 12th months after surgery) based on changes in Simulated-K, corneal irregularity measurement, shape factor, and toric mean keratometry. Post-operative courses were followed for 12 months after surgery. Recurrence was defined as the regrowth of fibrovascular tissue 1 mm past the corneoscleral limbus. Results: No sign of pterygium recurrence and the corneal stabilization point were observed at the third month post-operation. Significance improvements in all corneal parameters were noted between the 1st and 3rd months (both p < 0.001); however, insignificant changes were noted at the following 6th-and 12th-month visits (both p > 0.05). Conclusion: The controlled partial avulsion fibrin glue technique may improve surgical outcomes with long-term recurrence rates equal to or lower than those previously reported. Corneal surface recovery is completed after the third month of the excision procedure.

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Section: Discussionmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 91%

Pterygium recurrence and corneal stabilization point after pterygium excision using the controlled partial avulsion fibrin glue technique

2020

Makara J Health Res

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“…This retrospective data analysis study used a secondary dataset of 93 samples of pterygium patients from the previous study of Hilmi et al [ 3 ], with an outcome of BCVA magnitude (logMAR). The magnitude of BCVA was calculated by the difference between postsurgery and presurgery BCVA.…”

Section: Methodsmentioning

confidence: 99%

“…Best-corrected visual acuity (BCVA) and contrast sensitivity function are the standard parameters for quantifying visual impairment due to corneal changes in the pterygium [ 3 ]. BCVA is defined as measuring the optimum visual function representing the best vision with the best corrective lenses by using a standard eye chart, Snellen, or logarithm minimum angle of resolution (LogMAR).…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers focused on the size and length of pterygium as an indication of pterygium surgery. However, the severity of visual impairment did not correlate with the two determinants, and the results vary from one author to another [ 3 ]. The contradiction between the studies shows that there is a lack of agreement on specific determinants of pterygium progression that can cause ineffective management, especially for early surgical intervention of pterygium patients.…”

Section: Introductionmentioning

confidence: 99%

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Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique

Jais

Azemin

Hilmi

et al. 2021

The Scientific World Journal

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Introduction. Early detection of visual symptoms in pterygium patients is crucial as the progression of the disease can cause visual disruption and contribute to visual impairment. Best-corrected visual acuity (BCVA) and corneal astigmatism influence the degree of visual impairment due to direct invasion of fibrovascular tissue into the cornea. However, there were different characteristics of pterygium used to evaluate the severity of visual impairment, including fleshiness, size, length, and redness. The innovation of machine learning technology in visual science may contribute to developing a highly accurate predictive analytics model of BCVA outcomes in postsurgery pterygium patients. Aim. To produce an accurate model of BCVA changes of postpterygium surgery according to its morphological characteristics by using the machine learning technique. Methodology. A retrospective of the secondary dataset of 93 samples of pterygium patients with different pterygium attributes was used and imported into four different machine learning algorithms in RapidMiner software to predict the improvement of BCVA after pterygium surgery. Results. The performance of four machine learning techniques were evaluated, and it showed the support vector machine (SVM) model had the highest average accuracy (94.44% ± 5.86%), specificity (100%), and sensitivity (92.14% ± 8.33%). Conclusion. Machine learning algorithms can produce a highly accurate postsurgery classification model of BCVA changes using pterygium characteristics.

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Artificial intelligence assisted pterygium diagnosis: current status and perspectives

Chen,

Wu,

Zhu

et al. 2023

Int J Ophthalmol

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Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment. Early and accurate diagnosis is essential for effective management. Recently, artificial intelligence (AI) has shown promising potential in assisting clinicians with pterygium diagnosis. This paper provides an overview of AI-assisted pterygium diagnosis, including the AI techniques used such as machine learning, deep learning, and computer vision. Furthermore, recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection, classification and segmentation were summarized. The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed. The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis, which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.

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Effects of Different Types of Primary Pterygium on Changes in Oculovisual Function

Cited by 6 publications

References 0 publications

Pterygium recurrence and corneal stabilization point after pterygium excision using the controlled partial avulsion fibrin glue technique

Pterygium recurrence and corneal stabilization point after pterygium excision using the controlled partial avulsion fibrin glue technique

Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique

Artificial intelligence assisted pterygium diagnosis: current status and perspectives

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