Performance of an AI algorithm during the different phases of the COVID pandemics: what can we learn from the AI and vice versa.
Michele Catalano, Chandra Bortolotto, Giovanna Nicora, Marina Francesca Achilli, Alessio Consonni, Lidia Ruongo, Giovanni Callea, Antonio Lo Tito, Carla Biasibetti, Antonella Donatelli, Sara Cutti, Federico Comotto, Giulia Maria Stella, Angelo Corsico, Stefano Perlini, Riccardo Bellazzi, Raffaele Bruno, Andrea Filippi, Lorenzo Preda
Author Information
Michele Catalano: Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy and Radiology Department, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Chandra Bortolotto: Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy and Radiology Department, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Giovanna Nicora: Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
Marina Francesca Achilli: Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy and Radiology Department, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Alessio Consonni: Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy and Radiology Department, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Lidia Ruongo: Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy and Radiology Department, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Giovanni Callea: Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy and Radiology Department, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Antonio Lo Tito: Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy and Radiology Department, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Carla Biasibetti: Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy and Radiology Department, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Antonella Donatelli: Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy and Radiology Department, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Sara Cutti: Medical Direction, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Federico Comotto: Reply S.p.A., Corso Francia, 110, Turin, Italy.
Giulia Maria Stella: Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy and Dept. of Respiratory Diseases Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Angelo Corsico: Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy and Dept. of Respiratory Diseases Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Stefano Perlini: Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy and Dept. of Emergency Department, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Riccardo Bellazzi: Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
Raffaele Bruno: Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy and Infectious Diseases Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Andrea Filippi: Radiation Oncology Unit, University of Pavia, Pavia, Italy and Infectious Diseases Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Lorenzo Preda: Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy and Radiology Department, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Background: Artificial intelligence (AI) has proved to be of great value in diagnosing and managing infection. ALFABETO (ALL-FAster-BEtter-TOgether) is a tool created to support healthcare professionals in the triage, mainly in optimizing hospital admissions. Methods: The AI was trained during the pandemic's "first wave" (February-April 2020). Our aim was to assess the performance during the "third wave" of the pandemics (February-April 2021) and evaluate its evolution. The neural network proposed behavior (hospitalization vs home care) was compared with what was actually done. If there were discrepancies between ALFABETO's predictions and clinicians' decisions, the disease's progression was monitored. Clinical course was defined as "favorable/mild" if patients could be managed at home or in spoke centers and "unfavorable/severe" if patients need to be managed in a hub center. Results: ALFABETO showed accuracy of 76%, AUROC of 83%; specificity was 78% and recall 74%. ALFABETO also showed high precision (88%). 81 hospitalized patients were incorrectly predicted to be in "home care" class. Among those "home-cared" by the AI and "hospitalized" by the clinicians, 3 out of 4 misclassified patients (76.5%) showed a favorable/mild clinical course. ALFABETO's performance matched the reports in literature. Conclusions: The discrepancies mostly occurred when the AI predicted patients could stay at home but clinicians hospitalized them; these cases could be handled in spoke centers rather than hubs, and the discrepancies may aid clinicians in patient selection. The interaction between AI and human experience has the potential to improve both AI performance and our comprehension of pandemic management.
