1. Classification Performance for COVID Patient Prognosis from Automatic AI Segmentation—A Single-Center Study
- Author
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Riccardo Biondi, Nico Curti, Francesca Coppola, Enrico Giampieri, Giulio Vara, Michele Bartoletti, Arrigo Cattabriga, Maria Adriana Cocozza, Federica Ciccarese, Caterina De Benedittis, Laura Cercenelli, Barbara Bortolani, Emanuela Marcelli, Luisa Pierotti, Lidia Strigari, Pierluigi Viale, Rita Golfieri, and Gastone Castellani
- Subjects
radiomics ,artificial intelligence ,machine and deep learning ,medical imaging ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
Background: COVID assessment can be performed using the recently developed individual risk score (prediction of severe respiratory failure in hospitalized patients with SARS-COV2 infection, PREDI-CO score) based on High Resolution Computed Tomography. In this study, we evaluated the possibility of automatizing this estimation using semi-supervised AI-based Radiomics, leveraging the possibility of performing non-supervised segmentation of ground-glass areas. Methods: We collected 92 from patients treated in the IRCCS Sant’Orsola-Malpighi Policlinic and public databases; each lung was segmented using a pre-trained AI method; ground-glass opacity was identified using a novel, non-supervised approach; radiomic measurements were collected and used to predict clinically relevant scores, with particular focus on mortality and the PREDI-CO score. We compared the prediction obtained through different machine learning approaches. Results: All the methods obtained a well-balanced accuracy (70%) on the PREDI-CO score but did not obtain satisfying results on other clinical characteristics due to unbalance between the classes. Conclusions: Semi-supervised segmentation, implemented using a combination of non-supervised segmentation and feature extraction, seems to be a viable approach for patient stratification and could be leveraged to train more complex models. This would be useful in a high-demand situation similar to the current pandemic to support gold-standard segmentation for AI training.
- Published
- 2021
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