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  1. Jameel SK, Aydin S, Ghaeb NH, Majidpour J, Rashid TA, Salih SQ, et al.
    Biomolecules, 2022 Dec 16;12(12).
    PMID: 36551316 DOI: 10.3390/biom12121888
    Corneal diseases are the most common eye disorders. Deep learning techniques are used to perform automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional generative adversarial networks (CGANs), is presented. It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis. The study includes using corneal topography captured using a Pentacam device from patients with corneal diseases. The dataset contained 3448 different corneal images. Furthermore, it shows how an unbalanced dataset affects the performance of classifiers, where the data are balanced using the resampling approach. Finally, the results obtained from CNN networks trained on the balanced dataset are compared to those obtained from CNN networks trained on the imbalanced dataset. For performance, the system estimated the diagnosis accuracy, precision, and F1-score metrics. Lastly, some generated images were shown to an expert for evaluation and to see how well experts could identify the type of image and its condition. The expert recognized the image as useful for medical diagnosis and for determining the severity class according to the shape and values, by generating images based on real cases that could be used as new different stages of illness between healthy and unhealthy patients.
  2. Al-Sharify NT, Nser HY, Ghaeb NH, Al-Sharify ZT, See OH, Weng LY, et al.
    Heliyon, 2024 Aug 30;10(16):e35924.
    PMID: 39224364 DOI: 10.1016/j.heliyon.2024.e35924
    The corneal eye diseases such as Keratoconus cause weakening of the cornea, with this disease the cornea can change in shape. This condition affects between 1 in 3,000 to 1 in 10,000 people. The main reason for the development of such conditions is unknown and can have significant impacts. Over the last decade, with advancements in computerized corneal topography assessments, researchers have increasingly expressed interest in corneal topography for research as well as clinical activities. Up till now, several aspheric numerical models have been developed as well as proposed to define the complex shape of the cornea. A commonly used term for characterizing the asphericity in an eye is the Q value, a common indicator of the aspherical degree of the cornea. It is one of the critical parameters in the mathematical description model of the cornea as it represents the cornea's shape and the eye's characteristics. Due to the utmost importance of this Q value of the cornea, a couple of studies have attempted to explore this parameter and its distribution, merely in terms of its influence on the human eye's optical properties. The corneal Q value is an important factor that needs to be determined to treat for any refractive errors as corneal degeneration are disease that can lead to potential problems with the structure of the cornea. This study aims to highlight the need to understand Q value of the cornea as this can essentially assist with personalising corneal refractive surgeries and implantation of intraocular lenses. Therefore, the relevance of corneal Q value must be studied in association with different patients, especially ones who have been diagnosed with cataracts, brain tumours, or even COVID-19. To address this issue, this paper first carries out a literature review on the optics of the cornea, the relevance of corneal Q value in ophthalmic practice and studies corneal degenerations and its causes. Thereafter, a detailed review of several noteworthy relevant research studies examining the Q value of the cornea is performed. To do so, an elaborate database is created, which presents a list of different research works examined in this study and provides key evidence derived from these studies. This includes listing details on the age, gender, ethnicity of the eyes assessed, the control variables, the technology used in the study, and even more. The database also delivers important findings and conclusions noted in each study assessed. Next, this paper analyses and discusses the magnitude of corneal Q value in various scenarios and the influence of different parameters on corneal Q value. To design visual optical products as well as to enhance the understanding of the optical properties of an eye, future studies could consider the database and work presented in this study as useful references. In addition, the work can be used to make informed decisions in clinical practice for designing visual optical products as well as to enhance the understanding of the optical properties of an Eye.
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