Classification Performance for COVID Patient Prognosis from Automatic AI Segmentation—A Single-Center Study

Applied Sciences

Gastone Castellani¹  Luisa Pierotti²  Lidia Strigari²  Arrigo Cattabriga³  Rita Golfieri³  Giulio Vara³  Caterina De Benedittis³  Federica Ciccarese³  Maria Adriana Cocozza³  Francesca Coppola³  Pierluigi Viale⁴  Michele Bartoletti⁴  Barbara Bortolani⁵  Emanuela Marcelli⁵  Nico Curti⁵  Riccardo Biondi⁵  Enrico Giampieri⁵  Laura Cercenelli⁵ 

[1] Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy;Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Massarenti 9, S. Orsola-Malpighi Hospital, 40138 Bologna, Italy;Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy;Infectious Diseases Unit, IRCCS Sant’ Orsola-Malpighi Teaching Hospital, 40138 Bologna, Italy;eDIMESLab, Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy;

关键词: radiomics;    artificial intelligence;    machine and deep learning;    medical imaging;   

DOI  :  10.3390/app11125438

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown