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All Journal Jurnal Kedokteran Brawijaya Jurnal Teknologi Informasi dan Ilmu Komputer MNJ (Malang Neurology Journal) Majalah Kesehatan FKUB Indonesian Journal of Rheumatology International Journal of Retina Pediatric Sciences Journal JKKI : Jurnal Kedokteran dan Kesehatan Indonesia IJRETINA - International Journal of Retina Ophthalmologica Indonesiana
Nadia Artha Dewi
Department Of Ophthalmology, Faculty Of Medicine Universitas Brawijaya, Saiful Anwar General Hospital, Malang, Indonesia
Biochemistry, Genetics & Molecular Biology Computer Science & IT Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Neuroscience Public Health

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Evaluasi Performasi Ruang Warna pada Klasifikasi Diabetic Retinophaty Menggunakan Convolution Neural Network Dewi, Candra; Santoso, Andri; Indriati, Indriati; Dewi, Nadia Artha; Arbawa, Yoke Kusuma
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 3: Juni 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021834459

Abstract

Semakin meningkatnya jumlah penderita diabetes menjadi salah satu faktor penyebab semakin tingginya penderita penyakit diabetic retinophaty. Salah satu citra yang digunakan oleh dokter mata untuk mengidentifikasi diabetic retinophaty adalah foto retina. Dalam penelitian ini dilakukan pengenalan penyakit diabetic retinophaty secara otomatis menggunakan citra fundus retina dan algoritme Convolutional Neural Network (CNN) yang merupakan variasi dari algoritme Deep Learning. Kendala yang ditemukan dalam proses pengenalan adalah warna retina yang cenderung merah kekuningan sehingga ruang warna RGB tidak menghasilkan akurasi yang optimal. Oleh karena itu, dalam penelitian ini dilakukan pengujian pada berbagai ruang warna untuk mendapatkan hasil yang lebih baik. Dari hasil uji coba menggunakan 1000 data pada ruang warna RGB, HSI, YUV dan L*a*b* memberikan hasil yang kurang optimal pada data seimbang dimana akurasi terbaik masih dibawah 50%. Namun pada data tidak seimbang menghasilkan akurasi yang cukup tinggi yaitu 83,53% pada ruang warna YUV dengan pengujian pada data latih dan akurasi 74,40% dengan data uji pada semua ruang warna. AbstractIncreasing the number of people with diabetes is one of the factors causing the high number of people with diabetic retinopathy. One of the images used by ophthalmologists to identify diabetic retinopathy is a retinal photo. In this research, the identification of diabetic retinopathy is done automatically using retinal fundus images and the Convolutional Neural Network (CNN) algorithm, which is a variation of the Deep Learning algorithm. The obstacle found in the recognition process is the color of the retina which tends to be yellowish red so that the RGB color space does not produce optimal accuracy. Therefore, in this research, various color spaces were tested to get better results. From the results of trials using 1000 images data in the color space of RGB, HSI, YUV and L * a * b * give suboptimal results on balanced data where the best accuracy is still below 50%. However, the unbalanced data gives a fairly high accuracy of 83.53% with training data on the YUV color space and 74,40% with testing data on all color spaces.
Predicting Cardiovascular Risk in People With Diabetic Retinopathy : Study Using Framingham Risk Score Harahap, Vicia Belladona Carolina; Metita, Mirza; Dewi, Nadia Artha
Majalah Oftalmologi Indonesia Vol 49 No S2 (2023): Supplement Edition
Publisher : The Indonesian Ophthalmologists Association (IOA, Perhimpunan Dokter Spesialis Mata Indonesia (Perdami))

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35749/xqa8m965

Abstract

Introduction: People with type 2 diabetes have an increase risk of cardiovascular disease (CVD). Framingham risk score is used to determine 10 years the risk of CVD in asymptomatic patients for primary prevention. Up to now, there have been no similar studies regarding the risk of CVD in diabetic retinopathy (DR) patients in Indonesia. Therefore, we would like to investigate the corelation between DR and CVD risk through the population using Framingham risk score. Methods: This research is a Population-Based Cross-sectional Study. The data was taken from the villages in Malang Regency. Participant with diabetes aged > 40 was recruited. The available data was then processed according to the variables and assessed using Framingham Risk Score. The scores were classified as high, intermediate, and low risk. Results: From 953 samples there were 155 respondents with Diabetes Mellitus (DM), 23 of them experience DR. There were more female participants than men in DM group without DR (73.9%) and DR group (73.9%). A significant correlation between diabetic retinopathy and CVD risk was found (r=0.407, p=0.001). The risk of CVD in 10 years is low in diabetic patients without retinopathy ( p=0.001), but in patients with diabetic retinopathy, the risk of CVD in 10 years is intermediate-high (p=0.001). Conclusion: There is corelation between Diabetic Retinopathy and risk for cardiovascular disease in intermediate-high category using Framingham risk score.
Potential performance of artificial intelligence in diagnosis of hypertensive retinopathy: A systematic review and meta analysis from current evidence Prabowo, Edwin Damar; Dewi, Nadia Artha
JKKI : Jurnal Kedokteran dan Kesehatan Indonesia JKKI, Vol 16, No 3, (2025)
Publisher : Faculty of Medicine, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/JKKI.Vol16.Iss3.art9

Abstract

Hypertensive retinopathy (HR) is a retinal condition caused by chronic hypertension that often progresses silently and usually detected incidentally, making early diagnosis essential to prevent further progression. While current evaluations rely on ophthalmologists or retinal specialists, artificial intelligence (AI) offers a promising, time-efficient, and resource-effective alternative for HR diagnosis. This study aims to provide a robust and reliable evaluation of the diagnostic potential of AI for hypertensive retinopathy through a comprehensive systematic review and network meta-analysis, which has not been previously reported. An extensive literature search was conducted across six databases, including PubMed, ScienceDirect, SpringerLink, Taylor & Francis Online, ProQuest, and Sage Journals. The inclusion criteria were diagnostic studies published within the past decade that involved both healthy populations and patients with hypertensive retinopathy, in which the diagnosis of hypertensive retinopathy was performed by artificial intelligence and compared with that of ophthalmologists. The primary outcomes of interest included the sensitivity, specificity, and accuracy of AI in diagnosing HR from fundus image photography. Quality assessment was performed using the quality assessment of diagnostic accuracy studies (QUADAS) tool, and meta-analysis was conducted using STATA. From 1,530 articles screened, 10 studies were included in this analysis, comprising a total of 38,761 fundus images. A total of 38,761 fundus images from 10 studies were included in this analysis. The results indicated that AI demonstrated high sensitivity [90% (95% CI: 86% – 93%)] and specificity [95% (95% CI: 92% – 97%)] with p-value of 0.001. Based on these findings, AI shows promise as a future diagnostic option for HR due to its high sensitivity and specificity.
PERBANDINGAN TEKANAN INTRAOKULAR ANAK DENGAN DOWN SYNDROME DAN ANAK TANPA DOWN SYNDROME DI KOTA MALANG Putra, Dani Haritsah; Dewi, Nadia Artha; Anandita, Nanda Wahyu; Wulandari, Lely Retno
Majalah Kesehatan Vol. 12 No. 4 (2025): Majalah Kesehatan
Publisher : Faculty of Medicine Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/majalahkesehatan.2025.012.04.5

Abstract

Pengukuran tekanan intraokular (TIO) merupakan pemeriksaan untuk menilai kondisi dinamika aqueous humor. TIO dikatakan normal apabila terjadi keseimbangan antara produksi dan outflow dari aqueous humor. Adapun peningkatan TIO dapat merusak struktur bola mata, terutama saraf optik dan sel ganglion retina, serta menjadi faktor risiko penting untuk perkembangan glaukoma. Anak-anak dengan Down syndrome (ADS) memiliki kedalaman bilik mata depan yang lebih dangkal dibandingkan anak-anak tanpa Down syndrome (ATDS) sehingga dapat menyebabkan peningkatan TIO. Oleh karena itu, penelitian ini bertujuan untuk mengetahui perbedaan analisis hasil pengukuran TIO terhadap ATDS dengan ADS. Penelitian ini merupakan penelitian observasional dengan pengambilan data secara cross-sectional terhadap 22 subjek ADS dan 44 subjek ATDS berusia 0-18 tahun yang didapat dari hasil pemeriksaan mata di RSUD dr. Saiful Anwar Kota Malang dan beberapa panti asuhan yang tersebar di Kota Malang. Pada penelitian ini didapatkan bahwa tidak terdapat perbedaan TIO yang signifikan antara ATDS dan ADS pada okuli dekstra (p = 0,065) dan okuli sinistra (p = 0,153). Sehingga dapat disimpulkan bahwa tidak terdapat perbedaan TIO yang signifikan antara ATDS dan ADS, serta tidak didapatkan perbedaan TIO berdasarkan perbandingan intraokular, usia, dan jenis kelamin pada masing-masing kelompok ATDS dan ADS sebagai secondary outcome pada penelitian ini.
Co-Authors Agustiawan, Referano Andayani, Gitalisa Andhika Yudistira Arbawa, Yoke Kusuma Ari Andayani Arief S. Kartasasmita Athaya Febriantyo Purnomo Bramantya Surya Pratama Bramantya Surya Pratama Candra Dewi Cesarius Singgih Wahono Dharma, Andhika Guna Djatikusumo, Ari Elvioza Elvioza, Elvioza Fenti Kusumawardhani Hidayah Firmansyah, M Harahap, Vicia Belladona Carolina Harley, Ohisa Herman Bagus Trianto, Herman Bagus Hidayat Suyuti Ichsan, Andi M Indha Dwi Kartikasari Indriati Indriati Iskandar, Erwin Karim, Muhammad Rico Abdul Kartikasari, Indha Dwi Lely Retno W Linda Susanti Martin Hertanto Metita, Mirza Metita, Mirza Mirza Metita Mirza Metita Nasution, Ali Napiah Ovi Sofia Prabowo, Edwin Damar Putra, Dani Haritsah Putri, Mutiara Kristiani Rahmasari, Herisa Rizqillah, Aliyah Faiza Rofa Husnul Khuluqi Rulli Rosandi Safaruddin Refa Safaruddin Refa Safaruddin Refa Saldianovitta, Rahajeng Anugrahing Santoso, Andri Sembiring, Sindy Boru Seravina Adila Izzati Seskoati Prayitnaningsih Shahdevi Nandar Kurniawan, Shahdevi Nandar Sjahreza, Emil Sofia, Ovi Susanto Nugroho Susilo, Teguh Taufiq Nur Budaya Teddy Kristiyan Wahono, Cesarius Singgih Wahyu Anandita, Nanda Wino Vrieda Vierlia Yuniasih, Kristina Yurike Mandrasari Yustiarini, Ima