Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment
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 analyz...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/11/10/993 |
| _version_ | 1797514146278801408 |
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| author | Roberta Fusco Roberta Grassi Vincenza Granata Sergio Venanzio Setola Francesca Grassi Diletta Cozzi Biagio Pecori Francesco Izzo Antonella Petrillo |
| author_facet | Roberta Fusco Roberta Grassi Vincenza Granata Sergio Venanzio Setola Francesca Grassi Diletta Cozzi Biagio Pecori Francesco Izzo Antonella Petrillo |
| author_sort | Roberta Fusco |
| collection | DOAJ |
| description | 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 (<i>p</i> 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. |
| first_indexed | 2024-03-10T06:27:30Z |
| format | Article |
| id | doaj.art-053c58487e474dbbb28fceffb5bd9afb |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-10T06:27:30Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-053c58487e474dbbb28fceffb5bd9afb2023-11-22T18:48:54ZengMDPI AGJournal of Personalized Medicine2075-44262021-09-01111099310.3390/jpm11100993Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and TreatmentRoberta Fusco0Roberta Grassi1Vincenza Granata2Sergio Venanzio Setola3Francesca Grassi4Diletta Cozzi5Biagio Pecori6Francesco Izzo7Antonella Petrillo8IGEA SpA Medical Division—Oncology, Via Casarea 65, Casalnuovo di Napoli, 80013 Naples, ItalyDivision of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, ItalyDivision of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, ItalyDivision of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, ItalyDivision of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, ItalyDivision of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, ItalyDivision of Radiotherapy and Innovative Technologies, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, ItalyDivision of Hepatobiliary Surgery, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, ItalyDivision of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, ItalyObjective: 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 (<i>p</i> 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.https://www.mdpi.com/2075-4426/11/10/993COVID-19computed tomographyX-rayartificial intelligencemachine learningdeep learning |
| spellingShingle | Roberta Fusco Roberta Grassi Vincenza Granata Sergio Venanzio Setola Francesca Grassi Diletta Cozzi Biagio Pecori Francesco Izzo Antonella Petrillo Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment Journal of Personalized Medicine COVID-19 computed tomography X-ray artificial intelligence machine learning deep learning |
| title | Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment |
| title_full | Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment |
| title_fullStr | Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment |
| title_full_unstemmed | Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment |
| title_short | Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment |
| title_sort | artificial intelligence and covid 19 using chest ct scan and chest x ray images machine learning and deep learning approaches for diagnosis and treatment |
| topic | COVID-19 computed tomography X-ray artificial intelligence machine learning deep learning |
| url | https://www.mdpi.com/2075-4426/11/10/993 |
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