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Abstrak :
Latar Belakang: CT scan orbita merupakan modalitas radiologi yang mudah dan efisien untuk menilai adanya penebalan otot ekstraokular pada penderita oftalmopati Graves. Penebalan otot ekstraokular memiliki korelasi dengan masing-masing derajat oftalmopati Graves. Di Indonesia, belum ada korelasi antara ketebalan otot ekstraokular dengan derajat oftalmopati Graves menurut klasifikasi NOSPECS. Tujuan: Mendapatkan nilai korelasi antara ketebalan otot ekstraokular pada CT scan orbita dengan derajat oftalmopati Graves. Metode: Penelitian ini menggunakan desain potong lintang dengan metode consecutive sampling. Sampel penelitian berjumlah 89 orbita yang berasal dari 50 pasien penderita oftalmopati Graves yang telah menjalani pemeriksaan CT scan orbita di Departemen Radiologi RSUPN Cipto Mangunkusumo periode Januari 2012 hingga Desember 2016. Penelitian dilakukan sejak Februari hingga Maret 2017. Pengukuran ketebalan otot ekstraokular pada CT scan orbita dilakukan setelah meninjau ulang derajat oftalmopati Graves melalui hasil pemeriksaan oftalmologi. Hasil: Terdapat perbedaan bermakna di antara rerata ketebalan otot ekstraokular menurut derajat oftalmopati Graves (p<0,05). Uji korelasi Spearman didapatkan korelasi yang bermakna dan nilai r yang bervariasi di antara ketebalan otot ekstraokular dengan derajat oftalmopati Graves. Nilai r=0,43 untuk rektus medial, r=0,37 untuk rektus lateral, r=0,49 untuk rektus superior, r=0,45 untuk rektus inferior dan r=0,57 untuk ketebalan total ekstraokular. Kesimpulan: Terdapat korelasi positif sedang antara ketebalan otot ekstraokular pada CT scan orbita dengan derajat oftalmopati Graves.

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Background: CT scan is an easy and efficient radiological modality to measure extraocular enlargement in the patient with Graves' ophthalmopathy disease. Extraocular muscles enlargements were had correlated with each grade of Graves' ophthalmopathy. In Indonesia, there is not yet a study about correlation between extraocular muscles diameter in orbital CT scan with Graves' ophthalmopathy severity based on NOCPECS classification. Purpose: To obtain the correlation values between extraocular muscles diameter in orbital CT scan with Graves' ophthalmopathy severity. Method: This study used a cross sectional design. Eighty nine samples from fifty patients with Graves' opthalmopathy were chosen using consecutive sampling from patients that underwent orbital CT scan at the Radiology Departement of the Indonesia University's Faculty of Medicine' Cipto Mangunkusumo Hospital from time periode January 2012 until December 2016. This study was done from February until March 2017. The measurement of extraocular muscles diameter in orbital CT scan was performed after had reviewed Graves' ophthalmopathy severity from ophthalmology examination on medical record. Results: There are significantly differences between extraocular muscles diameter mean with Graves' ophthalmopathy severity (p<0,05). Spearman correlation test between extraocular muscles diameter with Graves' ophthalmopathy grading shows significant correlation with varied r values, r=0,43 for rectus medial, r=0,37 for rectus lateral, r=0,49 for rektus superior, r=0,45 for rectus inferior and r=0,57 for total diameters of extraocular muscles. Conclusion: There is a moderate positive correlation between extraocular muscles diameter in orbital CT scan with Graves' ophthalmopathy severity.

Abstrak :
Latar belakang: Perubahan okular pasien oftalmopati Graves (OG) tidak pernah mengalami remisi sempurna pasca tatalaksana berdampak negatif pada psikososial pasien. Kuesioner Graves Ophthalmopathy Quality of Life (GO-QoL) versi Bahasa Indonesia belum tervalidasi sehingga belum bisa mengevaluasi kualitas hidup pasien yang menjadi indikator dalam tatalaksana pasien OG. Tujuan: Menyajikan kuesioner GO-QoL versi Bahasa Indonesia yang sahih dan andal dan mengetahui hubungan kualitas hidup pasien dengan aktivitas klinis dan derajat keparahan OG. Metode: Proses validasi melalui adaptasi transkultural dengan desain potong lintang. Validitas dinilai dengan content validity index (CVI) dan reliabilitas dinilai dengan Cronbach's alpha. Hasil: Kuesioner GO-QoL versi Bahasa Indonesia memiliki content validity index (CVI) mencapai 1,00. Nilai Cronbach’s alpha  subskala fungsi penglihatan 0,971; subskala tampilan 0,993; total 0,986. Kualitas hidup pasien OG di subskala tampilan dan keseluruhan  memiliki hubungan bermakna dengan clinical activity score (p<0,05) dan derajat keparahan (p<0,001). Kesimpulan: GO-QoL versi Bahasa Indonesia validitas dan reliabilitas sangat baik. Aktivitas klinis OG yang aktif dan semakin tinggi derajat keparahan memperburuk kualitas hidup pasien pada subskala tampilan dan keseluruhan. ......Background: Graves' ophthalmopathy (GO) ocular abnormalities persisted even after treatment, negatively impacting the patient's psychological and social health. The Indonesian Graves' Ophthalmopathy Quality of Life (GO-QoL) Questionnaire has not been validated, hence it cannot measure patient quality of life, which is crucial to GO treatment. Objective: Providing a reliable Indonesian GO-QoL questionnaire and identifying an association between patient quality of life and clinical activity and severity of GO. Method: The process of questionnaire validation involves transcultural adaptation and cross-sectional design. The content validity index (CVI) and Cronbach's alpha assessed validity and reliability, respectively. Result: Content validity index (CVI) was 1.00 for the Indonesian GO-QoL questionnaire. Cronbach's alpha visual function subscale value was 0.971, while the appearance subscale value was 0.993, and the total score was 0.986. The appearance subscale and total score of OG patients' quality of life had a significant association with the clinical activity score (p<0.05) and disease severity (p<0.001). Conclusion: The Indonesian version of GO-QoL has good validity and reliability. Both the active clinical activity of OG and the severity of the disease decreased the patient's appearance and general quality of life.

Abstrak :
Penyakit mata perlu pendeteksian dan diagnosis yang tepat mengingat peran organ mata yang penting dalam kehidupan. Salah satu cara mendeteksi penyakit mata yang menyebabkan kebutaan adalah melalui ophthalmoscopy, dengan hasil pemeriksaan berupa citra fundus. Penelitian ini menggunakan metode Convolution Neural Network (CNN) dengan arsitektur CO-ResNet. Data yang digunakan dalam penelitian ini diambil dari online database yang berisi data multi-kelas penyakit mata. Preprocessing crop center dan resize digunakan dalam penelitian ini agar ukuran data citra dapat dijadikan input model. Fungsi optimasi untuk meminimalkan loss function ketika melatih model yang digunakan dalam penelitian ini adalah fungsi Adam dengan setting hyperparameter learning rate, epoch, 𝛽1 , dan 𝛽2 . Fungsi loss yang digunakan untuk masalah pengklasifikasian multikelas dalam penelitian ini adalah categorical cross entropy. Hasil penelitian menunjukan nilai yang diperoleh dengan training loss terkecil sebesar 0,4066 dan validation loss terkecil sebesar 0,4950. Sementara itu, nilai training accuracy terbaik sebesar 87% dan validation accuracy terbaik sebesar 79%. Setelah melalui proses training, dilakukan proses testing untuk mengevaluasi kinerja model. Hasil testing terbaik yang didapat dengan nilai testing accuracy sebesar 75,25%, precision sebesar 75,6%, recall sebesar 75,4%, dan F1-score sebesar 75,4%. Secara keseluruhan, metode CO- ResNet bekerja dengan cukup baik dalam mengklasifikasi dan mendeteksi penyakit mata. ......Eye diseases need proper detection and diagnosis considering the important role of eye organs in life. One way to detect eye diseases that cause blindness is through ophthalmoscopy, with the results of the examination being an image of the fundus. This research uses the Convolution Neural Network (CNN) method with CO-ResNet architecture. The data used in this study were taken from an online database containing data on multi-class eye diseases. Preprocessing crop center and resize are used in this study so that the size of the image data can be used as model input. The optimization function to minimize the loss function when training the model used in this study is the Adam function with the hyperparameters setting are learning rate, epoch, 𝛽1, and 𝛽2. The loss function used for the multiclass classification problem in this study is categorical cross entropy. The results showed that the value obtained with the smallest training loss was 0.4066 and the smallest validation loss was 0.4950. Meanwhile, the best training accuracy value is 87% and the best validation accuracy is 79%. After going through the training process, a testing process is carried out to evaluate the performance of the model. The best testing results were obtained with testing accuracy values of 75.25%, precision of 75.6%, recall of 75.4%, and F1-score of 75.4%. Overall, the CO-ResNet method works quite well in classifying and detecting eye diseases.