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All Journal PROSIDING SEMINAR NASIONAL BALI MEDICAL JOURNAL (BMJ) Medicina E-Jurnal Medika Udayana Jurnal Keperawatan Anak Jurnal Keperawatan Soedirman (The Soedirman Journal of Nursing) Universa Medicina International Journal of Retina ISM (Intisari Sains Medis) : Jurnal Kedokteran Jurnal Kedokteran Universitas Palangka Raya Journal of Holistics and Health Sciences (JHHS) Journal of Social Research POLICY, LAW, NOTARY AND REGULATORY ISSUES (POLRI) IJRETINA - International Journal of Retina Prosiding Seminar Nasional dan Call For Paper Kebidanan Universitas Ngudi Waluyo PEDAMAS (Pengabdian Kepada Masyarakat) Ophthalmologica Indonesiana International Journal of Psychology and Health Science Jurnal Jurisprudence
Ari Andayani
Program Studi Pendidikan Dokter, Fakultas Kedokteran, Universitas Udayana, Denpasar, Indonesia
Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Economics, Econometrics & Finance Education Energy Engineering Health Professions Immunology & microbiology Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences

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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.
Diabetic retinopathy: pathogenesis, pathophysiology, and treatment Yudistira, Yudistira; Hendrawan, Kevin Anggakusuma; Andayani, Ari; Suryathi, Ni Made Ari
Universa Medicina Vol. 44 No. 2 (2025)
Publisher : Faculty of Medicine, Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18051/UnivMed.2025.v44.270-284

Abstract

Diabetic retinopathy (DR) is the most common microvascular complication of diabetes mellitus (DM) and one of the leading causes of vision impairment worldwide. Prolonged hyperglycemia initiates a cascade of molecular events including chronic inflammation, oxidative stress, advanced glycation end products, and the activation of plasma kallikrein and protein kinase C signaling pathways, which leads to endothelial damage and pericyte loss. The resulting endothelial barrier dysfunction promotes serum leakage and retinal edema, while advanced disease stages are characterized by ischemia-driven retinal neovascularization mediated by elevated intraocular vascular endothelial growth factor (VEGF) levels. Current therapeutic strategies for diabetic retinopathy include laser therapy, intravitreal administration of anti-VEGF agents or corticosteroids, and vitreoretinal surgery. Despite their efficacy, a number of patients experience suboptimal responses. Consequently, novel therapeutic approaches are under investigation, including alternative anti-angiogenic agents, gene therapies, and visual cycle modulators currently undergoing clinical trials. A comprehensive understanding of the pathogenesis and pathophysiology of diabetic retinopathy is essential to improve existing treatment modalities and address current limitations in patient outcomes. In this review, we systematically searched and analyzed articles published in English from 2014 to 2024 using PubMed, ScienceDirect, SpringerLink, and Google Scholar. Relevant search terms included “diabetic retinopathy,” “pathophysiology,” “pathogenesis,” “treatment,” and “diabetic macular edema.” This review presents recent insights into the pathogenesis of diabetic retinopathy, including oxidative stress, inflammation, and neurodegeneration, followed by an overview of its pathophysiology such as microvascular dysfunction and neovascularization. Finally, current and emerging treatment modalities, encompassing both pharmacological and surgical approaches, are discussed. This structured approach provides essential background to understand the complexity of diabetic retinopathy and recent advances in its management.
Co-Authors Agrasidi, Putu Anindya Agus Kurniawan Agus Kurniawan Agus Kurniawan Agustiawan, Referano Agustinus Bimo Gumelar Amicipta Sanjaya Anak Agung Mas Putrawati Triningrat ANASARY, A. A. AYU PUTRI PREMATURA SRI Anasary, Anak Agung Ayu Putri Prematura Sri Andayani, Gitalisa andriyani, fajar Angelina S Tallo Anggrawati, Anggrawati Ari Suryathi, Ni Made Arief S. Kartasasmita Ariesanti Tri Handayani, Ariesanti Tri Arismaya, Anggun Mita Baiq Hilda Astriana, Baiq Hilda Bella Safitri Bobby Kristianto Bora, Elaria Dharma, Andhika Guna Djatikusumo, Ari Elvioza Elvioza, Elvioza Firmansyah, M fitriani, indra Harley, Ohisa Hendrawan, Kevin Hendrawan, Kevin Anggakusuma Heni Setyowati I Gde Raka Widiana I Gusti Ayu Ratna Suryaningrum I Made Gede Widnyana I Putu Budhiastra, I Putu I Wayan Gede Jayanegara I Wayan Losen Adnyana Ichsan, Andi M Ida Bagus Putra Manuaba Iskandar, Erwin Jayawarsa, A.A. Ketut Juliari, I Gusti Ayu Made Juliari, IGAM Komang Putra Tridiyoga Kristianto, Bobby Liviyanti, Liviyanti Lulyana, Lulyana Made Agus Kusumadjaja Made Ayu Hapsari Dwi Ambarini Maria Rumperiai, Juliana Martin Hertanto Minarni, May Moneca Diah Listiyaningsih N. M. Lienderi-Wati Nadia Artha Dewi Nadiah Nadiah Ni Made Ari Suryathi Ni Made Ayu Surasmiati Ni Made Laksmi Utari Ninik Christiani Noviantari, I Gusti Agung Ratna Nurlaili, Ilfi nurliah, nurliah nurlistiani, umi Nyoman Novita Rismawati, Nyoman Novita P. Budhiastra Pande Putu Adityo Ananta Ardika Pantjawati, Ni Luh Diah Pemayun, Cok Istri Dewiyani Pitanatri, Nyoman Brahmani Priscilla Christina Natan Putrawati Triningrat, Anak Agung Mas Putu Budhiastra Putu Budhiastra Retnaningtyas, Erma Retno Palupi Ririn Sulistyowati rizmi sliviana, zelda Rohana Satya Nugraha Sembiring, Sindy Boru Siska Siska Siti Haryani Sjahreza, Emil Sucilasmi Sukartini Djelantik, Anak Agung Ayu Sukidja, Sukidja Sukmawati, Nurindah supraptini, hemi Suryanadi, Ni Made Suryawijaya, Ernes Erlyana Susilo, Teguh Sutyawan, I Wayan Eka Titiek, Ernawati Tridiyoga, Komang Putra Triningrat, A.A. Mas Putrawati Triningrat, Anak Agung Mas Putrawati Tya Lestari Umbu Lapu, Ervan Suryanti Wahyu Setya Ningsih Wibawa, I Made Dwi Surya Widayati, Wijayati, Made Paramita Winly, Winly Yamba kodi, Elvira Yudistira Yudistira Yuliarta, Marliana Yuliawati, Putu Yustiarini, Ima