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Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment.
Fusco, Roberta; Grassi, Roberta; Granata, Vincenza; Setola, Sergio Venanzio; Grassi, Francesca; Cozzi, Diletta; Pecori, Biagio; Izzo, Francesco; Petrillo, Antonella.
  • Fusco R; IGEA SpA Medical Division-Oncology, Via Casarea 65, Casalnuovo di Napoli, 80013 Naples, Italy.
  • Grassi R; Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, Italy.
  • Granata V; Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy.
  • Setola SV; Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131 Naples, Italy.
  • Grassi F; Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131 Naples, Italy.
  • Cozzi D; Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, Italy.
  • Pecori B; Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy.
  • Izzo F; Division of Radiotherapy and Innovative Technologies, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131 Naples, Italy.
  • Petrillo A; Division of Hepatobiliary Surgery, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131 Naples, Italy.
J Pers Med ; 11(10)2021 Sep 30.
Article in English | MEDLINE | ID: covidwho-1444254
ABSTRACT

OBJECTIVE:

To report an overview and update on Artificial Intelligence (AI) and COVID-19 using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine Learning and Deep Learning Approaches for Diagnosis and Treatment were identified.

METHODS:

Several electronic datasets were analyzed. The search covered the years from January 2019 to June 2021. The inclusion criteria were studied evaluating the use of AI methods in COVID-19 disease reporting performance results in terms of accuracy or precision or area under Receiver Operating Characteristic (ROC) curve (AUC).

RESULTS:

Twenty-two studies met the inclusion criteria 13 papers were based on AI in CXR and 10 based on AI in CT. The summarized mean value of the accuracy and precision of CXR in COVID-19 disease were 93.7% ± 10.0% of standard deviation (range 68.4-99.9%) and 95.7% ± 7.1% of standard deviation (range 83.0-100.0%), respectively. The summarized mean value of the accuracy and specificity of CT in COVID-19 disease were 89.1% ± 7.3% of standard deviation (range 78.0-99.9%) and 94.5 ± 6.4% of standard deviation (range 86.0-100.0%), respectively. No statistically significant difference in summarized accuracy mean value between CXR and CT was observed using the Chi square test (p value > 0.05).

CONCLUSIONS:

Summarized accuracy of the selected papers is high but there was an important variability; however, less in CT studies compared to CXR studies. Nonetheless, AI approaches could be used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, COVID-19 diagnosis, and disease management.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Prognostic study Language: English Year: 2021 Document Type: Article Affiliation country: Jpm11100993

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Prognostic study Language: English Year: 2021 Document Type: Article Affiliation country: Jpm11100993