Purpose: To identify salient imaging features to support human-based differential diagnosis between subretinal hemorrhage(SH) due to choroidal neovascularization(CNV)onset and SH without CNV (simple bleeding,SB)in pathologic myopia eyes using a machine learning(ML)-based stepwise approach. Materials and methods: Four different methods for feature extraction were applied:GradCAM visualization,reverse engineering,image processing and human graders' measurements.GradCAM was performed on a deep learning(DL)model derived from Inception-ResNet-v2 trained with OCT B-scan images. Reverse engineering consisted in merging U-Net architecture with a deconvolutional network.Image processing consisted in the application of a local adaptive threshold. Available OCT B-scan images were divided in two groups: the first group was classified by graders before knowing the results of feature extraction; the second(different images)was classified after familiarization with the results of feature extraction. Results: Forty-seven(47)and 37 eyes were included in CNV group and SB group respectively.CNV eyes showed higher baseline central macular thickness(p=0.036).Image processing evidenced in CNV eyes an inhomogeneity of the subretinal material and an interruption of the Bruch membrane at the margins of the SH area.Graders' classification performance improved from an accuracy of 76.9% without guidance to 83.3% with the guidance of the 3 methods(p=0.02). DL accuracy in the task was 86.0%. Conclusions: Artificial intelligence helps identifying imaging biomarkers suggestive of CNV in the context of SH in myopia,improving human ability to perform differential diagnosis on unprocessed baseline OCT B-scan images. DL can accurately distinguish between the 2 causes of SH.

Artificial intelligence's role in differentiating the origin for subretinal bleeding in pathologic myopia / Crincoli, Emanuele; Servillo, Andrea; Catania, Fiammetta; Sacconi, Riccardo; Mularoni, Cecilia; Battista, Marco; Querques, Lea; Parravano, Mariacristina; Costanzo, Eliana; Polito, Maria Sole; Bandello, Francesco; Querques, Giuseppe. - In: RETINA. - ISSN 0275-004X. - (2023). [10.1097/IAE.0000000000003884]

Artificial intelligence's role in differentiating the origin for subretinal bleeding in pathologic myopia

Servillo, Andrea
Secondo
;
Sacconi, Riccardo;Mularoni, Cecilia;Battista, Marco;Bandello, Francesco
Penultimo
;
Querques, Giuseppe
Ultimo
2023-01-01

Abstract

Purpose: To identify salient imaging features to support human-based differential diagnosis between subretinal hemorrhage(SH) due to choroidal neovascularization(CNV)onset and SH without CNV (simple bleeding,SB)in pathologic myopia eyes using a machine learning(ML)-based stepwise approach. Materials and methods: Four different methods for feature extraction were applied:GradCAM visualization,reverse engineering,image processing and human graders' measurements.GradCAM was performed on a deep learning(DL)model derived from Inception-ResNet-v2 trained with OCT B-scan images. Reverse engineering consisted in merging U-Net architecture with a deconvolutional network.Image processing consisted in the application of a local adaptive threshold. Available OCT B-scan images were divided in two groups: the first group was classified by graders before knowing the results of feature extraction; the second(different images)was classified after familiarization with the results of feature extraction. Results: Forty-seven(47)and 37 eyes were included in CNV group and SB group respectively.CNV eyes showed higher baseline central macular thickness(p=0.036).Image processing evidenced in CNV eyes an inhomogeneity of the subretinal material and an interruption of the Bruch membrane at the margins of the SH area.Graders' classification performance improved from an accuracy of 76.9% without guidance to 83.3% with the guidance of the 3 methods(p=0.02). DL accuracy in the task was 86.0%. Conclusions: Artificial intelligence helps identifying imaging biomarkers suggestive of CNV in the context of SH in myopia,improving human ability to perform differential diagnosis on unprocessed baseline OCT B-scan images. DL can accurately distinguish between the 2 causes of SH.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11768/148799
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