Turkiye Klinikleri Journal of Ophthalmology

.: ORIGINAL RESEARCH
Determination of Optimum Hyperparameters in Diagnosis of Strabismus Using Artificial Intelligence Model: Cross-Sectional Study
Yapay Zekâ Modeli ile Şaşılığın Saptanmasında Optimum Hiperparametrelerin Belirlenmesi: Kesitsel Araştırma
Ersin AKBULUTa , Furkan KIRIKb, Havvanur BAYRAKTARc, Ayşe Rümeysa MOHAMMEDd , Betül TUĞCUb
aClinic of Ophthalmology, Sultanbeyli State Hospital, İstanbul, Türkiye
bDepartment of Ophthalmology, Bezmiâlem Vakıf University Faculty of Medicine, İstanbul, Türkiye
cClinic of Ophthalmology, Diyarbakır Gazi Yaşargil Training and Research Hospital, Diyarbakır, Türkiye
d Distributed and Collaborative Virtual Environments Research Lab, University of Ottawa, Ottawa, Canada
Turkiye Klinikleri J Ophthalmol. 2023;32(3):176-83
doi: 10.5336/ophthal.2023-96253
Article Language: EN
Full Text
ABSTRACT
 Objective: This study aims to analyze the effectiveness of the internet-based, free Teachable Machine (TM) platform, which does not entail code knowledge, in detecting the presence and types of strabismus in the optimum hyperparameters. Material and Methods: The images obtained from the patients who presented to our clinic with the complaint of ocular deviation were analyzed, and 523 [176 esotropia (ET), 195 exotropia (XT), and 152 orthophoria (ORTHO)] images were included in this study. After the images were uploaded to the TM platform, 6 different batch sizes and 9 different learning rates were tested using the grid search method, with the number of epochs fixed at 4,000 to determine the optimum hyperparameter. Results: The highest overall test accuracy was 0.887, and the hyperparameters from which this accuracy was obtained were 200 for the number of epochs, 256 for the batch size, and 0.0005 for the learning rate. In the TM model trained with these parameters, accuracy values of ET: 0.96, ORTHO: 0.78 and XT: 0.9 were obtained in the subgroups, respectively. Conclusion: To achieve optimal accuracy at the stage of development of the artificial intelligence model, users should determine the appropriate hyperparameter values depending on the size of the available dataset and the complexity of the data. The results we obtained by determining the optimum hyperparameters have revealed that the presence of strabismus can be detected with high accuracy using TM, an internet-based, free deep learning platform that does not entail having code knowledge.

Keywords: Strabismus; artificial intelligence; deep learning; machine learning
ÖZET
 Amaç: Bu çalışmada kod bilgisi gerektirmeyen, internet tabanlı, ücretsiz ''Teachable Machine'' (TM) platformunun optimum hiperparametrelerde şaşılık varlığı ve tiplerini saptamadaki etkinliğinin değerlendirilmesi amaçlanmaktadır. Gereç ve Yöntemler: Kliniğimize gözde kayma şikâyeti ile başvuran hastalardan elde edilen görüntüler analiz edilmiş olup, çalışma kriterlerine uygun olan 523 [176 ezotropya (ET), 195 ekzotropya (XT), 152 ortoforik (ORTO)] görüntü çalışmaya dâhil edilmiştir. Görüntüler TM platformuna yüklendikten sonra optimum hiperparametrelerin belirlenmesi için döngü sayısı 4.000'e sabitlenerek 6 farklı küme boyutu ve 9 farklı öğrenme oranı ızgara arama metodu ile test edildi. Test edilen hiperparametrelerde elde edilen doğruluk ve hata değerleri kaydedildi. Bulgular: En yüksek genel test doğruluğu 0,887 olarak saptanmış olup bu doğruluğun elde edildiği hiperparametreler döngü sayısı için 200, küme boyutu için 256 ve öğrenme oranı için 0,0005 olarak tespit edilmiştir. Bu parametrelerle eğitilen TM modelinde alt gruplarda sırasıyla ET: 0,96, ORTO: 0,78 ve XT: 0,9 doğruluk elde edilmiştir. Sonuç: Yapay zekâ modelinin geliştirilmesi aşamasında optimum doğruluğu elde edebilmek için mevcut veri setinin büyüklüğü ve verilerin karmaşıklığına bağlı olarak kullanıcılar uygun hiperparametre değerlerini belirlemelidir. Optimum hiperparametreler belirlenerek elde ettiğimiz sonuçlar şaşılık varlığının kod bilgisi gerektirmeyen, internet tabanlı, ücretsiz derin öğrenme platformu olan TM ile yüksek doğrulukta saptanabileceğini göstermektedir.

Anahtar Kelimeler: Şaşılık; yapay zekâ; derin öğrenme; makine öğrenmesi
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