Artificial Intelligence in Fluorescence Lifetime Imaging Ophthalmoscopy (FLIO) Data Analysis-Toward Retinal Metabolic Diagnostics.

The purpose of this study was to investigate the possibility of implementing an artificial intelligence (AI) approach for the analysis of fluorescence lifetime imaging ophthalmoscopy (FLIO) data even with small data. FLIO data, including the fluorescence intensity and mean fluorescence lifetime (τm) of two spectral channels, as well as OCT-A data from 26 non-smokers and 28 smokers without systemic and ocular diseases were used. The analysis was performed with support vector machines (SVMs), a well-known AI method for small datasets, and compared with the results of convolutional neural networks (CNNs) and autoencoder networks. The SVM was the only tested AI method, which was able to distinguish τm between non-smokers and heavy smokers. The accuracy was about 80%. OCT-A data did not show significant differences. The feasibility and usefulness of the AI in analyzing FLIO and OCT-A data without any apparent retinal diseases were demonstrated. Although further studies with larger datasets are necessary to validate the results, the results greatly suggest that AI could be useful in analyzing FLIO-data even from healthy subjects without retinal disease and even with small datasets. AI-assisted FLIO is expected to greatly advance early retinal diagnosis.

Impact of cigarette smoking on fluorescence lifetime of ocular fundus.

Cigarette smoking is known to adversely affect cellular metabolism and is a risk factor for various retinal diseases. Fluorescence lifetime imaging ophthalmoscopy (FLIO) has the potential to detect metabolic changes in the ocular fundus. Aim of this study was to analyze the influence of cigarette smoking on fluorescence lifetime (FLT) of healthy eyes using FLIO. Twenty-six non-smokers and 28 smokers aged between 20 and 37 years without systemic and ocular diseases were investigated by FLIO (excitation: 473 nm, emission: short spectral channel (SSC) 498-560 nm, long spectral channel (LSC) 560-720 nm). The FLT at the ETDRS grid regions were analyzed and compared. In SSC, the mean FLT (τm) of smokers was significantly longer in the ETDRS inner ring region, whereas the τm in LSC was significantly shorter in the outer ring. For the long component (τ2), smokers with pack year < 7.11 showed significantly shorter τ2 in SSC than non-smokers and the smokers with pack year ≥ 7.11. There were no significant differences in retinal thickness. The lack of obvious structural differences implies that the observed FLT changes are likely related to smoking-induced metabolic changes. These results suggest that FLIO may be useful in assessing retinal conditions related to lifestyle and systemic metabolic status.