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

Mohd Radzi Hilmi / Khairidzan Mohd Kamal / Mohd Zulfaezal Che Azemin

Makara Journal of Health Research, Vol 24, Iss 2, Pp 140-

2020 Volume 146

Abstract: 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 ... ...

Abstract	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.
Keywords	cornea ; fibrin tissue adhesive ; pterygium ; recurrence ; Medicine (General) ; R5-920
Subject code	500
Language	English
Publishing date	2020-08-01T00:00:00Z
Publisher	Universitas Indonesia
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings.

Che Azemin, Mohd Zulfaezal / Hassan, Radhiana / Mohd Tamrin, Mohd Izzuddin / Md Ali, Mohd Adli

International journal of biomedical imaging

2020 Volume 2020, Page(s) 8828855

Abstract: The key component in deep learning research is the availability of training data sets. With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed ... ...

Abstract	The key component in deep learning research is the availability of training data sets. With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable. We aimed to use thousands of readily available chest radiograph images with clinical findings associated with COVID-19 as a training data set, mutually exclusive from the images with confirmed COVID-19 cases, which will be used as the testing data set. We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. The performance of the model in terms of area under the receiver operating curve, sensitivity, specificity, and accuracy was 0.82, 77.3%, 71.8%, and 71.9%, respectively. The strength of this study lies in the use of labels that have a strong clinical association with COVID-19 cases and the use of mutually exclusive publicly available data for training, validation, and testing.
Keywords	covid19
Language	English
Publishing date	2020-08-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	2196721-0
ISSN	1687-4196 ; 1687-4188
ISSN (online)	1687-4196
ISSN	1687-4188
DOI	10.1155/2020/8828855
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique.

Jais, Fatin Nabihah / Che Azemin, Mohd Zulfaezal / Hilmi, Mohd Radzi / Mohd Tamrin, Mohd Izzuddin / Kamal, Khairidzan Mohd

TheScientificWorldJournal

2021 Volume 2021, Page(s) 6211006

Abstract: 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 ... ...

Abstract	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. 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.
MeSH term(s)	Algorithms ; Female ; Humans ; Machine Learning ; Male ; Middle Aged ; Pterygium/physiopathology ; Pterygium/surgery ; Retrospective Studies ; Support Vector Machine ; Visual Acuity
Language	English
Publishing date	2021-11-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	2075968-X
ISSN	1537-744X ; 1537-744X
ISSN (online)	1537-744X
ISSN	1537-744X
DOI	10.1155/2021/6211006
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Article ; Online: The validity and reliability of the Arabic version of the ocular surface disease index (OSDI) questionnaire in a sample of the Gazan population: a study from Palestine.

Aljarousha, Mohammed / Badarudin, Noor Ezailina / Che Azemin, Mohd Zulfaezal / Aljeesh, Yousef / Amer, Abuimara / Abdul Rahim, Muhammad Afzam Shah

International ophthalmology

2022 Volume 43, Issue 4, Page(s) 1303–1316

Abstract: Purpose: To develop an Arabic version of OSDI for the Gazan population.: Methods: A cross-sectional observational study was conducted using a convenience sample technique. The translation procedure included five stages: forward translation, revision ... ...

Abstract	Purpose: To develop an Arabic version of OSDI for the Gazan population. Methods: A cross-sectional observational study was conducted using a convenience sample technique. The translation procedure included five stages: forward translation, revision of translation, backward translation, refinement of translation, and a final test of the pre-final version. The final sets of questionnaires were constructed using an online JotForm platform. The online platform was chosen to automatically calculate the questionnaire's final overall score. Overall, 260 participants were instructed to fill out the English and the Arab-OSDI version twice to conduct the reliability of the translated version and repeatability evaluation. Results: The mean age of the participants was 33.45 ± 11.74 years old. Cronbach's alpha for all items was greater than 0.80, except for the "blurred vision" and "deteriorating vision" items (0.77 and 0.74, respectively). The mean overall score difference between the English-OSDI and Arab-OSDI was 0.86 based on the Bland-Altman chart. For repeatability, no significant difference in the overall scores between the two repeats of the Arab-OSDI (p = 0.632). The Arab-OSDI overall score (sessions 1 and 2) has a clinical difference (bias) of 0.21. Using the varimax rotation method, only three factors (ocular symptoms, vision-related function, and environmental triggers) had eigenvalues greater than one in the structure of the Arab-OSDI. Conclusion: The Arab-OSDI is an appropriate, reliable, and repeatable tool for the determination of dry eye symptoms, ocular discomfort, and quality of life in the Gazan population. This version could remove the language barrier in answering OSDI items more easily.
MeSH term(s)	Humans ; Young Adult ; Adult ; Middle Aged ; Quality of Life ; Cross-Sectional Studies ; Reproducibility of Results ; Arabs ; Surveys and Questionnaires
Language	English
Publishing date	2022-09-26
Publishing country	Netherlands
Document type	Observational Study ; Journal Article
ZDB-ID	800087-6
ISSN	1573-2630 ; 0165-5701
ISSN (online)	1573-2630
ISSN	0165-5701
DOI	10.1007/s10792-022-02528-7
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Article ; Online: Cross-cultural translation and validation of the Malay version of the Swanson, Nolan, and Pelham Parent Rating Scale of attention deficit hyperactivity disorders symptoms among Malaysian probands: A preliminary study.

Jusoh, Masnira / Dzulkarnain, Ahmad Aidil Arafat / Rahmat, Sarah / Musa, Ramli / Che Azemin, Mohd Zulfaezal

Asia-Pacific psychiatry : official journal of the Pacific Rim College of Psychiatrists

2020 Volume 13, Issue 2, Page(s) e12414

Abstract: The aim of this study is to evaluate the psychometric properties of the Malay version of the Swanson, Nolan, and Pelham Parent Rating Scale of attention deficit hyperactivity disorders (ADHD) symptoms (M-SNAP-IV). For this purpose, the SNAP-IV scale was ... ...

Abstract	The aim of this study is to evaluate the psychometric properties of the Malay version of the Swanson, Nolan, and Pelham Parent Rating Scale of attention deficit hyperactivity disorders (ADHD) symptoms (M-SNAP-IV). For this purpose, the SNAP-IV scale was translated into the Malay language and was pilot-tested on 91 parents of children aged 8 to 11 years (ADHD [n = 36] and non-ADHD children [n = 55]). The findings depicted that the M-SNAP-IV has excellent content validity, internal consistency, and test-retest reliability. The M-SNAP-IV is a valid and reliable screening tool to detect ADHD symptoms in children and has the advantages to assess the specific presentation of ADHD.
MeSH term(s)	Attention Deficit Disorder with Hyperactivity/diagnosis ; Child ; Cross-Cultural Comparison ; Humans ; Language ; Malaysia ; Parents ; Reproducibility of Results
Language	English
Publishing date	2020-08-19
Publishing country	Australia
Document type	Journal Article
ZDB-ID	2506332-7
ISSN	1758-5872 ; 1758-5864
ISSN (online)	1758-5872
ISSN	1758-5864
DOI	10.1111/appy.12414
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Article ; Online: Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique

Fatin Nabihah Jais / Mohd Zulfaezal Che Azemin / Mohd Radzi Hilmi / Mohd Izzuddin Mohd Tamrin / Khairidzan Mohd Kamal

The Scientific World Journal, Vol

2021 Volume 2021

Abstract: 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 ...

Abstract	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.
Keywords	Technology ; T ; Medicine ; R ; Science ; Q
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings

Che Azemin, Mohd Zulfaezal / Hassan, Radhiana / Mohd Tamrin, Mohd Izzuddin / Md Ali, Mohd Adli

International journal of biomedical imaging

Abstract: The key component in deep learning research is the availability of training data sets With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed ... ...

Abstract	The key component in deep learning research is the availability of training data sets With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable We aimed to use thousands of readily available chest radiograph images with clinical findings associated with COVID-19 as a training data set, mutually exclusive from the images with confirmed COVID-19 cases, which will be used as the testing data set We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images The performance of the model in terms of area under the receiver operating curve, sensitivity, specificity, and accuracy was 0 82, 77 3%, 71 8%, and 71 9%, respectively The strength of this study lies in the use of labels that have a strong clinical association with COVID-19 cases and the use of mutually exclusive publicly available data for training, validation, and testing
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #733116
Database	COVID19

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Article: Comparison of Dry Eye Parameters between Diabetics and Non-Diabetics in District of Kuantan, Pahang.

Aljarousha, Mohammed / Badarudin, Noor Ezailina / Che Azemin, Mohd Zulfaezal

The Malaysian journal of medical sciences : MJMS

2016 Volume 23, Issue 3, Page(s) 72–77

Abstract: Introduction: Diabetes may affect the human body's systems and organs, including the eye. Diabetic retinopathy is the 5th leading cause of blindness globally. Diabetic subjects demonstrated dry eye symptoms that were also supported by the low values of ... ...

Abstract	Introduction: Diabetes may affect the human body's systems and organs, including the eye. Diabetic retinopathy is the 5th leading cause of blindness globally. Diabetic subjects demonstrated dry eye symptoms that were also supported by the low values of the clinical tests. Purpose: This study aimed to compare the dry eye symptoms and signs between diabetics and non-diabetics and tear functions between diabetic subjects with and without dry eye. Methods: This retrospective study was based on the observation of 643 medical files. Using a convenience sampling method, 88 subjects were found to report diabetes mellitus. The information extracted from the files included: date of first examination, age at first visit, gender, past ocular history, systemic disease, symptoms of dry eye disease and details of clinical diagnostic signs. Non-contact lens wearers were excluded. A group of 88, age and gender matched, control subjects were included for this comparison study. Results: The percentage of dry eye symptoms was higher in diabetic subjects (15.9%) compared with non-diabetic subjects (13.6%; p<0.001). The percentage of dry eye symptoms was also higher in diabetics with dry eye (63%) than in diabetics without dry eye (36.9%; p<0.001). Tear break up time was significantly different between diabetics and non-diabetics (p<0.001) and between diabetics with and without dry eye (p=0.046). The corneal staining was significantly different between diabetic subjects with and without dry eye (p=0.028). Conclusion: Dry eye symptoms were significantly associated with diabetics. Tear break up time was significantly shorter in diabetics with dry eye compared to diabetics without dry eye.
Language	English
Publishing date	2016-06-29
Publishing country	Malaysia
Document type	Journal Article
ZDB-ID	2197205-9
ISSN	2180-4303 ; 1394-195X
ISSN (online)	2180-4303
ISSN	1394-195X
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Article ; Online: Utilization of Anterior Segment Optical Coherence Tomography Enhanced High Resolution Corneal In Measuring Pterygium Thickness

Mohd Radzi Hilmi / Khairidzan Mohd Kamal / Mohd Zulfaezal Che Azemin / Azrin Esmady Ariffin

Sains Medika, Vol 9, Iss 2, Pp 67-

2018 Volume 72

Abstract: Introduction: As various pterygium morphologies have been advocated as contributing factor on corneal astigmatism, little support in the literature available in establishing techniques in measuring pterygium thickness as clinical indicator. Objective: ... ...

Abstract	Introduction: As various pterygium morphologies have been advocated as contributing factor on corneal astigmatism, little support in the literature available in establishing techniques in measuring pterygium thickness as clinical indicator. Objective: The aim of this study was to describe a quantitative method in determining pterygium thickness using anterior segment optical coherence tomography (AS-OCT). Methods: Anterior segment imaging was performed using enhanced high resolution cornea (EHRC) of Visante™ AS-OCT in 120 primary pterygium eyes. Prior to imaging, corneal topography assessment was performed on each pterygium eye in order to identify its topographic location. Based on topography mapping, three meridians (in degrees) were selected as close as possible to the pterygium border, which signify the demarcation of pterygium from the cornea. Reliability testing between intra and inter-observer of AS-OCT imaging modality was examined using intraclass correlation and scatter plot. Results: The overall (n = 120) mean and standard deviation of pterygium thickness EHRC of AS-OCT modality were 0.48 ± 0.10 mm (confidence interval: 0.45 – 0.50). EHRC of AS-OCT also showed excellent intra and intergrader reliability in measuring pterygium thickness with intraclass correlation of 0.997 (confidence interval: 0.994 – 0.998). Conclusions: EHRC of AS-OCT imaging modality is a better choice in assessing pterygium compared to traditional slit-lamp biomicroscopy. This tool is applicable for future work related to better understanding on the role thickness in pterygium morphology, its progression and prediction of induced corneal astigmatism and visual impairment due to pterygium.
Keywords	pterygium ; anterior segment oct ; as-oct ; morphology ; thickness ; reliability ; Medicine (General) ; R5-920
Language	English
Publishing date	2018-12-01T00:00:00Z
Publisher	Universitas Sultan Agung Semarang
Document type	Article ; Online
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Article ; Online: Prediction of Changes in Visual Acuity and Contrast Sensitivity Function by Tissue Redness after Pterygium Surgery.

Hilmi, Mohd Radzi / Che Azemin, Mohd Zulfaezal / Mohd Kamal, Khairidzan / Mohd Tamrin, Mohd Izzuddin / Abdul Gaffur, Norfazrina / Tengku Sembok, Tengku Mohd

Current eye research

2017 Volume 42, Issue 6, Page(s) 852–856

Abstract: Purpose: The goal of this study was to predict visual acuity (VA) and contrast sensitivity function (CSF) with tissue redness grading after pterygium surgery.: Materials and methods: A total of 67 primary pterygium participants were selected from ... ...

Abstract	Purpose: The goal of this study was to predict visual acuity (VA) and contrast sensitivity function (CSF) with tissue redness grading after pterygium surgery. Materials and methods: A total of 67 primary pterygium participants were selected from patients who visited an ophthalmology clinic. We developed a semi-automated computer program to measure the pterygium fibrovascular redness from digital pterygium images. The final outcome of this software is a continuous scale grading of 1 (minimum redness) to 3 (maximum redness). The region of interest (ROI) was selected manually using the software. Reliability was determined by repeat grading of all 67 images, and its association with CSF and VA was examined. Results: The mean and standard deviation of redness of the pterygium fibrovascular images was 1.88 ± 0.55. Intra-grader and inter-grader reliability estimates were high with intraclass correlation ranging from 0.97 to 0.98. The new grading was positively associated with CSF (p < 0.01) and VA (p < 0.01). The redness grading was able to predict 25% and 23% of the variance in the CSF and the VA, respectively. Conclusions: The new grading of pterygium fibrovascular redness can be reliably measured from digital images and showed a good correlation with CSF and VA. The redness grading can be used in addition to the existing pterygium grading.
Language	English
Publishing date	2017-06
Publishing country	England
Document type	Journal Article
ZDB-ID	82079-9
ISSN	1460-2202 ; 0271-3683
ISSN (online)	1460-2202
ISSN	0271-3683
DOI	10.1080/02713683.2016.1250277
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