TY - JOUR
T1 - Integration of Optical Coherence Tomography Images and Real-Life Clinical Data for Deep Learning Modeling
T2 - A Unified Approach in Prognostication of Diabetic Macular Edema
AU - Atik, Muhammed Enes
AU - Kocak, İbrahim
AU - Sayin, Nihat
AU - Bayramoglu, Sadik Etka
AU - Ozyigit, Ahmet
N1 - Publisher Copyright:
© 2024 Wiley-VCH GmbH.
PY - 2024
Y1 - 2024
N2 - The primary ocular effect of diabetes is diabetic retinopathy (DR), which is associated with diabetic microangiopathy. Diabetic macular edema (DME) can cause vision loss for people with DR. For this reason, deciding on the appropriate treatment and follow-up has a critical role in terms of curing the disease. Current artificial intelligence (AI) approaches focus on OCT images and may ignore clinical, laboratory, and demographic information obtained by the specialist. This study presents a novel deep learning (DL) framework for evaluating the visual outcome of the TREX anti-VEGF intravitreal injection regimen. DL models are trained to extract deep features from OCT and ILM topographic images and the obtained deep features are combined with patients' demographic, clinical, and laboratory findings to predict the direction of the treatment process. When the ResNet-18 network is used, the proposed DL framework is able to predict the prognosis status of patients with the highest accuracy.
AB - The primary ocular effect of diabetes is diabetic retinopathy (DR), which is associated with diabetic microangiopathy. Diabetic macular edema (DME) can cause vision loss for people with DR. For this reason, deciding on the appropriate treatment and follow-up has a critical role in terms of curing the disease. Current artificial intelligence (AI) approaches focus on OCT images and may ignore clinical, laboratory, and demographic information obtained by the specialist. This study presents a novel deep learning (DL) framework for evaluating the visual outcome of the TREX anti-VEGF intravitreal injection regimen. DL models are trained to extract deep features from OCT and ILM topographic images and the obtained deep features are combined with patients' demographic, clinical, and laboratory findings to predict the direction of the treatment process. When the ResNet-18 network is used, the proposed DL framework is able to predict the prognosis status of patients with the highest accuracy.
KW - clinical data
KW - deep learning
KW - diabetic macular edema
KW - diabetic retinopathy
KW - OCT images
UR - http://www.scopus.com/inward/record.url?scp=85213731914&partnerID=8YFLogxK
U2 - 10.1002/jbio.202400315
DO - 10.1002/jbio.202400315
M3 - Article
AN - SCOPUS:85213731914
SN - 1864-063X
JO - Journal of Biophotonics
JF - Journal of Biophotonics
ER -