Interlenghi, M.; Sborgia, G.; Venturi, A.; Sardone, R.; Pastore, V.; Boscia, G.; Landini, L.; Scotti, G.; Niro, A.; Moscara, F.;
et al. A Radiomic-Based Machine Learning System to Diagnose Age-Related Macular Degeneration from Ultra-Widefield Fundus Retinography. Diagnostics 2023, 13, 2965.
https://doi.org/10.3390/diagnostics13182965
AMA Style
Interlenghi M, Sborgia G, Venturi A, Sardone R, Pastore V, Boscia G, Landini L, Scotti G, Niro A, Moscara F,
et al. A Radiomic-Based Machine Learning System to Diagnose Age-Related Macular Degeneration from Ultra-Widefield Fundus Retinography. Diagnostics. 2023; 13(18):2965.
https://doi.org/10.3390/diagnostics13182965
Chicago/Turabian Style
Interlenghi, Matteo, Giancarlo Sborgia, Alessandro Venturi, Rodolfo Sardone, Valentina Pastore, Giacomo Boscia, Luca Landini, Giacomo Scotti, Alfredo Niro, Federico Moscara,
and et al. 2023. "A Radiomic-Based Machine Learning System to Diagnose Age-Related Macular Degeneration from Ultra-Widefield Fundus Retinography" Diagnostics 13, no. 18: 2965.
https://doi.org/10.3390/diagnostics13182965
APA Style
Interlenghi, M., Sborgia, G., Venturi, A., Sardone, R., Pastore, V., Boscia, G., Landini, L., Scotti, G., Niro, A., Moscara, F., Bandi, L., Salvatore, C., & Castiglioni, I.
(2023). A Radiomic-Based Machine Learning System to Diagnose Age-Related Macular Degeneration from Ultra-Widefield Fundus Retinography. Diagnostics, 13(18), 2965.
https://doi.org/10.3390/diagnostics13182965
