###### 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. Keywords:radiomics; artificial intelligence; machine and deep learning; medical imaging +
Footnote †: journal: Medical  
reaction (rt-PCR) and the gene sequencing of sputum, throat swab and lower respiratory tract secretion [1; 2]. These tests have several limitations: a limited testing capacity related to insufficient kits or laboratory supplies; a long reporting time, varying from 6 to 48 h; a great variability in sensitivity, ranging from 37% to 71%. To circumvent these limitations, imaging has emerged as an important tool to guide diagnosis, especially in cases of clinical-laboratory discordances. Imaging protocols directive from public health authorities are heterogeneous: chest radiography is widely used, although it is not accurate in mild or early COVID-19 infection; there is an improving interest in bedside lung ultrasound, but limited experiences are reported in the literature [3]. Among imaging modalities, Computed Tomography (CT) is the most sensitive (60-98%) acquisition technique, but it has low specificity in the early stage of the disease [4]. For this reason, World Health Organization (WHO) and most radiologic societies do not recommend performing screening CT (WHO characterizes COVID-19 as a pandemic--11 March 2020). High-Resolution Computed Tomography (HRCT) proved nonetheless to be a valuable aid to the clinical and epidemiological management of affected patients, especially during shortages of the necessary reagents, long reporting time, and the high operator-dependency [5; 6; 7]. There have been controversial publications about the role of chest CT imaging analysis in the diagnosis and management of COVID-19 [8]. The identification of healthy lung chest CTs from pneumonia cases has been deeply investigated in literature [9; 10], but the COVID-19 pandemic has posed the non-trivial problem of classifying different pulmonary diseases. Patchy shadows or ground-glass opacities (GGOs) and consolidations (CSs) are not exclusive of COVID-19 but might be also caused by pulmonary edema, bacterial infection, other viral infection, or alveolar hemorrhage [11]. An initial prospective made by [PERSON] et al. [12] on chest CT scans of patients affected by COVID-19 has shown that the examined subjects have a bilateral GGO and CS. The same medical results were also confirmed by other authors [13; 14], who posed the basis for the next quantification studies allowing a better characterization of the COVID-19 features.