Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloMachine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy
Anno di pubblicazione2021
FormatoElettronico
Autore/iIsabella Castiglioni 1,2, Davide Ippolito 3, Matteo Interlenghi 2, Caterina Beatrice Monti 4, Christian Salvatore 5,6, Simone Schiaffino 7, Annalisa Polidori 8, Davide Gandola 3, Cristina Messa 9,10, Francesco Sardanelli 4,7
Affiliazioni autori1. Department of Physics, Università degli Studi di Milano-Bicocca, Piazza della Scienza 3, 20126, Milan, Italy. 2. Institute of Biomedical Imaging and Physiology, National Research Council, 20090, Segrate, Milan, Italy. 3. Department of Radiology, San Gerardo Hospital, Via Pergolesi 33, 20900, Monza, Italy. 4. Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133, Milan, Italy. 5. Scuola Universitaria Superiore IUSS Pavia, Piazza della Vittoria 15, 27100, Pavia, Italy. salvatore@deeptracetech.com. 6. DeepTrace Technologies S.R.L., Via Conservatorio 17, 20122, Milan, Italy. salvatore@deeptracetech.com. 7. Department of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097, San Donato Milanese, Milan, Italy. 8. DeepTrace Technologies S.R.L., Via Conservatorio 17, 20122, Milan, Italy. 9. School of Medicine and Surgery, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126, Milan, Italy. 10. Fondazione Tecnomed, Università degli Studi di Milano-Bicocca, Palazzina Ciclotrone, Via Pergolesi 33, 20900, Monza, Italy.
Autori CNR e affiliazioni
  • MATTEO INTERLENGHI
  • ISABELLA CASTIGLIONI
Lingua/e
  • inglese
AbstractBackground: We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. Methods: We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. Results: At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74-0.81), 0.82 specificity (95% CI 0.78-0.85), and 0.89 area under the curve (AUC) (95% CI 0.86-0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72-0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73-0.87), and 0.81 AUC (95% CI 0.73-0.87). Radiologists' reading obtained 0.63 sensitivity (95% CI 0.52-0.74) and 0.78 specificity (95% CI 0.61-0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52-0.74) and 0.86 specificity (95% CI 0.71-0.95) in Centre 2. Conclusions: This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da7
Pagine a-
Pagine totali10
RivistaEuropean radiology experimental Online
Attiva dal 2017
Editore: Springer Open
Paese di pubblicazione: Svizzera
Lingua: inglese
ISSN: 2509-9280
Titolo chiave: European radiology experimental Online
Titolo proprio: European radiology experimental.
Numero volume della rivista5
Fascicolo della rivista1
DOI10.1186/s41747-020-00203-z.
Verificato da referee-
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • PubMed (Codice:33527198)
Parole chiaveArtificial intelligence; COVID-19; Neural networks (computer); Sensitivity and specificity; X-rays.
Link (URL, URI)-
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione17/12/2020
Note/Altre informazioni-
Strutture CNR
  • IBFM — Istituto di bioimmagini e fisiologia molecolare
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati