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

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)...