Interlenghi, Matteo, Sborgia, Giancarlo, Venturi, Alessandro, Sardone, Rodolfo 
ORCID: 0000-0003-1383-1850, Pastore, Valentina, Boscia, Giacomo, Landini, Luca, Scotti, Giacomo, Niro, Alfredo, Moscara, Federico et al (show 3 more authors)
(2023)
A Radiomic-Based Machine Learning System to Diagnose Age-Related Macular Degeneration from Ultra-Widefield Fundus Retinography.
DIAGNOSTICS, 13 (18).
2965-.
|
PDF
A Radiomic-Based Machine Learning System to Diagnose Age-Related Macular Degeneration from Ultra-Widefield Fundus Retinograp.pdf - Open Access published version Download (11MB) | Preview |
Abstract
The present study was conducted to investigate the potential of radiomics to develop an explainable AI-based system to be applied to ultra-widefield fundus retinographies (UWF-FRTs) with the objective of predicting the presence of the early signs of Age-related Macular Degeneration (AMD) and stratifying subjects with low- versus high-risk of AMD. The ultimate aim was to provide clinicians with an automatic classifier and a signature of objective quantitative image biomarkers of AMD. The use of Machine Learning (ML) and radiomics was based on intensity and texture analysis in the macular region, detected by a Deep Learning (DL)-based macular detector. Two-hundred and twenty six UWF-FRTs were retrospectively collected from two centres and manually annotated to train and test the algorithms. Notably, the combination of the ML-based radiomics model and the DL-based macular detector reported 93% sensitivity and 74% specificity when applied to the data of the centre used for external testing, capturing explainable features associated with drusen or pigmentary abnormalities. In comparison to the human operator's annotations, the system yielded a 0.79 Cohen <i>κ</i>, demonstrating substantial concordance. To our knowledge, these results are the first provided by a radiomic approach for AMD supporting the suitability of an explainable feature extraction method combined with ML for UWF-FRT.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | age-related macular degeneration (AMD), ultra-widefield (UWF), fundus retinography (FRT), artificial intelligence (AI), machine learning (ML), radiomics, deep learning (DL), detection, image segmentation, explainability |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 13 Dec 2023 11:46 |
| Last Modified: | 13 Dec 2023 11:46 |
| DOI: | 10.3390/diagnostics13182965 |
| Open Access URL: | https://doi.org/10.3390/diagnostics13182965 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3177305 |
Dimensions
Dimensions
