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All Journal Medicina E-Jurnal Medika Udayana Jurnal Pengabdian Kepada Masyarakat (Indonesian Journal of Community Engagement) Universa Medicina ISM (Intisari Sains Medis) : Jurnal Kedokteran Bioscientia Medicina : Journal of Biomedicine and Translational Research Bioscientia Medicina : Journal of Biomedicine and Translational Research Public Health and Preventive Medicine Archive Journal of Global Pharma Technology Oftalmologi: Jurnal Kesehatan Mata Indonesia Ophthalmologica Indonesiana
Ariesanti Tri Handayani, Ariesanti Tri
Departement Of Ophthalmology, Universitas Udayana
Biochemistry, Genetics & Molecular Biology Decision Sciences, Operations Research & Management Education Health Professions Immunology & microbiology Library & Information Science Medicine & Pharmacology Neuroscience Nursing Public Health

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Journal : Universa Medicina

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Classification of diabetic retinopathy using ensemble convolutional neural network architectures Hendrawan, Kevin; Handayani, Ariesanti Tri; Andayani, Ari; Titiek, Ernawati; Gumelar, Agustinus Bimo
Universa Medicina Vol. 43 No. 2 (2024)
Publisher : Faculty of Medicine, Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18051/UnivMed.2024.v43.188-194

Abstract

Background Diabetic retinopathy (DR) constitutes a primary cause of blindness across all age groups. Ophthalmologists examine fundus images (FI) to detect and classify stages of DR. Development of deep learning can help clinicians to attain a larger volume in screening and diagnosing diabetic retinopathy, thereby decreasing the burden of visual impairment caused by DR. This study aimed to classify DR using ensemble convolutional neural networks (CNN) architectures. Methods We used data from the Indian Diabetic Retinopathy Image Dataset which consist of typical diabetic retinopathy lesions at pixel level. The dataset contains typical diabetic retinopathy structures as well as normal retinal structures and is divided into three parts: segmentation, classification, and location. There are 516 original color fundus images in the classification used as training set (413 images) and testing set (103 images). We used ensemble CNN architectures to classify diabetic retinopathy as no DR, mild non-proliferative DR (NPDR), moderate NPDR, severe NPDR and proliferative DR (PDR) based on fundus image. Results In this study we successfully created a model with ensemble CNNs to detect DR based on fundus images with area-under-the-curve, sensitivity, and specificity of 0.88, 0.89, and 0.90, respectively, which is on par with the most modern methods. Conclusion Based on the results, this model performs quite well in early detection of diabetic retinopathy and can be used to develop a more accurate model for detecting and classifying diabetic retinopathy. This model can also be used in assisting mass screening at lower cost without reducing diagnostic effectiveness.
Factors that influence refractive errors in premature infants Surasmiati, Ni Made Ayu; Handayani, Ariesanti Tri; Sutyawan, I Wayan Eka
Universa Medicina Vol. 43 No. 1 (2024)
Publisher : Faculty of Medicine, Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18051/UnivMed.2024.v43.44-50

Abstract

Background The prevalence of refractive errors is reported to be higher in children born preterm. Factors such as gestational age, birth weight, and retinopathy of prematurity status, have a significant impact on the refractive development in preterm infants. Prematurity and low birth weight affect the development of organ systems in infants, including the eyes. In addition to immature retinas, other eye conditions, such as refractive status, are also observed. This study aimed to determine the risk factors of refractive status, specifically refractive errors (spherical equivalent, astigmatism, and anisometropia) in premature infants at a tertiary hospital in Bali. MethodsA cross-sectional study was conducted involving 53 premature infants. This study collected samples from January to August 2023 at the Neonatal Intensive Care Unit of Prof. dr. IGNG Ngoerah General Hospital. Data regarding gender, gestational age, birth weight, retinal condition, spherical equivalent, and refractive disorders were collected. The relationship between risk factors and spherical equivalent, astigmatism, and anisometropia were analyzed using multiple regression analysis with statistical significance set at p<0.05. ResultsHypermetropia is the most common finding in premature infants, followed by myopia and astigmatism. The prevalence of myopia (9.4%) and astigmatism (5.7%) is also more common among newborns of gestational age ≤30 weeks (p=0.024). Chronological age was significantly associated with spherical equivalent (β=0.424; p=0.019). ConclusionIn premature infants, chronological age was the risk factor of spherical equivalent. Other risk factors were not associated with the prevalence of refractive errors among premature infants.
Co-Authors Agustinus Bimo Gumelar Anak Agung Ayu Putri Prematura Sri Anasary Anak Agung Ayu Sukartini Djelantik Anak Agung Mas Putrawati Triningrat Angelo Doniho Anthea Casey Ari Andayani Cokorda Istri Dewiyani Pemayun Dharmi Lestari, Ni Putu Gde Ngurah Idraguna Pinatih Giovani Faustine Harumi Purwa Prahesthy, Harumi Purwa Hendrawan, Kevin I Gusti Ngurah Agung Trisnu Kamajaya I Putu Budhiastra I Wayan Gede Jayanegara Ida Bagus Putra Manuaba Ivane Jessica Buddyman Juliari, I Gusti Ayu Made Luh Gede Anggasari Dewi Made Agus Kusumadjaja Maharani, Pande Nyoman Anila Putri Ni Made Ari Suryathi Ni Made Ayu Surasmiati Noviantari, I Gusti Agung Ratna PRISCILLA DWIANGGITA Putra Manuaba Putu Adhi Surya Pradana Putu Yuliawati Ratnasari, Ni Luh Made Novi Siska Takarai SUDIBIA, NI KADEK PUSPA MEGA PUTRI Sutyawan, I Wayan Eka Titiek, Ernawati Triningrat, A A Mas Putrawati Triningrat, Anak Agung Mas Putrawati Utari, Ni Made Laksmi WATU, THEDIUS Wedayanti, Pande Made Gita