Turkiye Klinikleri Journal of Ophthalmology

.: ORIGINAL RESEARCH
Performance of Artificial Intelligence in the Detection of Retinopathy of Prematurity Based on Clinical Risk Factors and Ophthalmic Examination Findings: A Observational Research-Descriptive Research
Klinik Risk Faktörleri ve Oftalmik Muayene Bulgularına Dayalı Prematüre Retinopatisinin Tespitinde Yapay Zekanın Performansı: Gözlemsel Araştırma-Tanımlayıcı Araştırma
Eşay KIRAN YENİCEa , Caner KARAb , Dilek ULUBAŞ IŞIKc , İstemi Han ÇELİKc , Mustafa YENİCEd , Çağatay Berke ERDAŞe
aUniversity of Health Sciences Faculty of Medicine, Ankara City Hospital-Bilkent, Department of Ophthalmology, Ankara, Türkiye
bUniversity of Health Sciences Faculty of Medicine, Ankara City Hospital-Etlik, Department of Ophthalmology, Ankara, Türkiye
cUniversity of Health Sciences Faculty of Medicine, Ankara City Hospital-Etlik, Department of Child Health and Diseases, Department of Neonatology, Ankara, Türkiye
dPrivate Engineer, Ankara, Türkiye
eBaşkent University Faculty of Engineering, Department of Computer Engineering, Department of Computer Software, Ankara, Türkiye
Turkiye Klinikleri J Ophthalmol. 2025;34(4):193-200
doi: 10.5336/ophthal.2025-111254
Article Language: EN
Full Text
ABSTRACT
 Objective: To assess the predictive value of risk factors and ophthalmic examination findings for the development of retinopathy of prematurity (ROP) using artificial intelligence (AI) models. Material and Methods: A total of 453 premature infants between 22-33 weeks of gestation screened for ROP were evaluated retrospectively. The infants' perinatal risk factors (multiple births, small for gestational age, neonatal sepsis, etc) and ophthalmic examination findings were recorded. Random Forest model were trained with 10-fold cross validation using these variables to predict ROP. Accuracy, specificity, receiver operating characteristic curve, and area under the curve metrics were used to evaluate algorithm performance. Results: The model trained on all variables achieved 85% accuracy and 90% specificity in predicting ROP. On the other hand, the model trained on gestational age (GA), birth weight (BW) and perinatal risk factors achieved higher accuracy (87%) and specificity (90%) in predicting ROP compared to the model trained on GA and BW alone (76% accuracy and 82% specificity). When each variable was evaluated individually, the most effective factors were found to be total days on oxygen, GA, multiple birth and BW, respectively. In addition, the model was able to detect infants with stage II ROP (90% accuracy, 96% specificity) and zone III ROP (93% accuracy, 99% specificity) with higher accuracy and specificity. Conclusion: In addition to prematurity, exposure to perinatal risk factors is important in the development of ROP, and the evaluation of the effect of these factors using AI may support ROP specialists in the clinical management of infants.

Keywords: Artificial intelligence; retinopathy of prematurity; preterm; machine learning
ÖZET
 Amaç: Bu çalışmanın amacı, yapay zekâ (YZ) modelleri kullanılarak prematüre retinopatisi [retinopathy of prematurity (ROP)] gelişiminde risk faktörleri ve oftalmik muayene bulgularının öngörücü değerinin değerlendirilmesidir. Gereç ve Yöntemler: ROP açısından tarama yapılan, 22-33. gebelik haftaları arasında doğan 453 prematüre yenidoğanın muayene bulguları geriye dönük değerlendirildi. Yenidoğanlara ait perinatal risk faktörleri (çoklu doğum, gestasyonel yaşa göre küçük doğum, neonatal sepsis vb.) ve oftalmolojik muayene bulguları kaydedildi. Bu değişkenler kullanılarak 10 katlı çapraz doğrulama ile ROP'u tahmin etmek için Rastgele Orman modeli eğitildi. Algoritma performansını değerlendirmek için doğruluk, özgüllük, alıcı işletim karakteristiği eğrisi ve eğrinin altındaki alan ölçümleri kullanıldı. Bulgular: Tüm değişkenler kullanılarak eğitilen modelin, ROP'u tahmin etmede %85 doğruluk ve %90 özgüllüğe ulaştığı görüldü. Gebelik yaşı (GY), doğum ağırlığı (DA) ve perinatal risk faktörleri ile eğitilen modelin, yalnızca GY ve DA ile eğitilen modele kıyasla (%76 doğruluk ve %82 özgüllük) ROP'u tahmin etmede daha yüksek doğruluk (%87) ve özgüllük (%90) elde ettiği tespit edildi. Her değişken tek tek değerlendirildiğinde en etkili faktörlerin sırasıyla toplam oksijen günü, GY, çoklu doğum ve DA olduğu bulundu. Ayrıca, modelin evre II ROP'lu (%90 doğruluk, %96 özgüllük) ve bölge III ROP'lu (%93 doğruluk, %99 özgüllük) bebekleri daha yüksek doğruluk ve özgüllükle tespit edebildiği görüldü. Sonuç: Prematüritenin yanı sıra perinatal risk faktörlerine maruziyet ROP gelişiminde önemlidir ve bu faktörlerin etkisinin YZ kullanılarak değerlendirilmesi, ROP uzmanlarına yenidoğanların klinik takibinde destek sağlayabilir.

Anahtar Kelimeler: Yapay zekâ; prematüre retinopatisi; prematüre; makine öğrenimi
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