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

• Published in: Diagnostics (Basel) Vol. 14; no. 4; p. 431
• Main Authors: Thiemann, Natalie, Sonntag, Svenja Rebecca, Kreikenbohm, Marie, Böhmerle, Giulia, Stagge, Jessica, Grisanti, Salvatore, Martinetz, Thomas, Miura, Yoko
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.02.2024
• Subjects: Artificial intelligence; Clinical medicine; Data analysis; Datasets; Diabetes; Diabetic retinopathy; Diagnostic imaging; Fluorescence; fluorescence lifetime imaging ophthalmoscopy; Machine learning; Medical imaging equipment; Medical research; Medicine, Experimental; Metabolism; Neural networks; Ophthalmology; Retina; retinal metabolism; small data; smoking; support vector machine; Support vector machines; Germany; retinal metabolism; small data; support vector machine; fluorescence lifetime imaging ophthalmoscopy; artificial intelligence; smoking
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics14040431

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.