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Performance of a Machine-Learning Computational Image Analysis Algorithm in Retinal Fluid Quantification for Patients
Background And Objective:
The objective is to validate an automated artificial intelligence model in detecting and quantifying fluid in diabetic macular edema (DME) and retinal vein occlusion (RVO) optical coherence tomography images.
Patients And Methods:
DME (n = 100) and RVO (n = 100) images of adult patients were reviewed. The performance of machine-learning (ML) computational image analysis algorithm was evaluated against consensus manual grading. Main outcomes were accuracy and sensitivity for detection and Pearson's correlation coefficients for quantification.
Results:
The ML algorithm had a high accuracy and sensitivity in both DME (intraretinal fluid [IRF]: 0.92, 0.97; subretinal fluid [SRF]: 0.93, 1.00) and RVO (IRF: 0.94, 0.99; SRF: 0.93, 1.00). It had moderate-high correlation in quantifying fluid in DME (total retinal fluid: 0.88; IRF: 0.88; SRF: 0.97) and RVO (total retinal fluid: 0.83; IRF: 0.76; SRF: 0.64).
Conclusion:
The ML algorithm is highly accurate and sensitive in detecting fluid in DME and RVO optical coherence tomography images and effectively quantifies IRF and SRF in both disease states, particularly in images with low to moderate fluid burden. [.
