1. AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients
- Author
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Palmisano, A, Vignale, D, Boccia, E, Nonis, A, Gnasso, C, Leone, R, Montagna, M, Nicoletti, V, Bianchi, A, Brusamolino, S, Dorizza, A, Moraschini, M, Veettil, R, Cereda, A, Toselli, M, Giannini, F, Loffi, M, Patelli, G, Monello, A, Iannopollo, G, Ippolito, D, Mancini, E, Pontone, G, Vignali, L, Scarnecchia, E, Iannacone, M, Baffoni, L, Sperandio, M, de Carlini, C, Sironi, S, Rapezzi, C, Antiga, L, Jagher, V, Di Serio, C, Furlanello, C, Tacchetti, C, Esposito, A, Palmisano A., Vignale D., Boccia E., Nonis A., Gnasso C., Leone R., Montagna M., Nicoletti V., Bianchi A. G., Brusamolino S., Dorizza A., Moraschini M., Veettil R., Cereda A., Toselli M., Giannini F., Loffi M., Patelli G., Monello A., Iannopollo G., Ippolito D., Mancini E. M., Pontone G., Vignali L., Scarnecchia E., Iannacone M., Baffoni L., Sperandio M., de Carlini C. C., Sironi S., Rapezzi C., Antiga L., Jagher V., Di Serio C., Furlanello C., Tacchetti C., Esposito A., Palmisano, A, Vignale, D, Boccia, E, Nonis, A, Gnasso, C, Leone, R, Montagna, M, Nicoletti, V, Bianchi, A, Brusamolino, S, Dorizza, A, Moraschini, M, Veettil, R, Cereda, A, Toselli, M, Giannini, F, Loffi, M, Patelli, G, Monello, A, Iannopollo, G, Ippolito, D, Mancini, E, Pontone, G, Vignali, L, Scarnecchia, E, Iannacone, M, Baffoni, L, Sperandio, M, de Carlini, C, Sironi, S, Rapezzi, C, Antiga, L, Jagher, V, Di Serio, C, Furlanello, C, Tacchetti, C, Esposito, A, Palmisano A., Vignale D., Boccia E., Nonis A., Gnasso C., Leone R., Montagna M., Nicoletti V., Bianchi A. G., Brusamolino S., Dorizza A., Moraschini M., Veettil R., Cereda A., Toselli M., Giannini F., Loffi M., Patelli G., Monello A., Iannopollo G., Ippolito D., Mancini E. M., Pontone G., Vignali L., Scarnecchia E., Iannacone M., Baffoni L., Sperandio M., de Carlini C. C., Sironi S., Rapezzi C., Antiga L., Jagher V., Di Serio C., Furlanello C., Tacchetti C., and Esposito A.
- Abstract
Purpose: To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients’ risk stratification. Material and Methods: In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled. In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival. A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web–mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020). Results: The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816–0.867) on wave 1 and was used to build a 0–100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402–0.8766). Conclusions: AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis.
- Published
- 2022