tetano
Editor, Senior Moderator
Sci Rep
. 2022 Apr 21;12(1):6596.
doi: 10.1038/s41598-022-10568-3.
Assessing clinical applicability of COVID-19 detection in chest radiography with deep learning
João Pedrosa[SUP] #[/SUP][SUP] 1 2 [/SUP], Guilherme Aresta[SUP] #[/SUP][SUP] 3 4 [/SUP], Carlos Ferreira[SUP] 3 4 [/SUP], Catarina Carvalho[SUP] 3 4 [/SUP], Joana Silva[SUP] 5 6 [/SUP], Pedro Sousa[SUP] 5 7 [/SUP], Lucas Ribeiro[SUP] 5 [/SUP], Ana Maria Mendonça[SUP] 3 4 [/SUP], Aurélio Campilho[SUP] 3 4 [/SUP]
Affiliations
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has impacted healthcare systems across the world. Chest radiography (CXR) can be used as a complementary method for diagnosing/following COVID-19 patients. However, experience level and workload of technicians and radiologists may affect the decision process. Recent studies suggest that deep learning can be used to assess CXRs, providing an important second opinion for radiologists and technicians in the decision process, and super-human performance in detection of COVID-19 has been reported in multiple studies. In this study, the clinical applicability of deep learning systems for COVID-19 screening was assessed by testing the performance of deep learning systems for the detection of COVID-19. Specifically, four datasets were used: (1) a collection of multiple public datasets (284.793 CXRs); (2) BIMCV dataset (16.631 CXRs); (3) COVIDGR (852 CXRs) and 4) a private dataset (6.361 CXRs). All datasets were collected retrospectively and consist of only frontal CXR views. A ResNet-18 was trained on each of the datasets for the detection of COVID-19. It is shown that a high dataset bias was present, leading to high performance in intradataset train-test scenarios (area under the curve 0.55-0.84 on the collection of public datasets). Significantly lower performances were obtained in interdataset train-test scenarios however (area under the curve > 0.98). A subset of the data was then assessed by radiologists for comparison to the automatic systems. Finetuning with radiologist annotations significantly increased performance across datasets (area under the curve 0.61-0.88) and improved the attention on clinical findings in positive COVID-19 CXRs. Nevertheless, tests on CXRs from different hospital services indicate that the screening performance of CXR and automatic systems is limited (area under the curve < 0.6 on emergency service CXRs). However, COVID-19 manifestations can be accurately detected when present, motivating the use of these tools for evaluating disease progression on mild to severe COVID-19 patients.
. 2022 Apr 21;12(1):6596.
doi: 10.1038/s41598-022-10568-3.
Assessing clinical applicability of COVID-19 detection in chest radiography with deep learning
João Pedrosa[SUP] #[/SUP][SUP] 1 2 [/SUP], Guilherme Aresta[SUP] #[/SUP][SUP] 3 4 [/SUP], Carlos Ferreira[SUP] 3 4 [/SUP], Catarina Carvalho[SUP] 3 4 [/SUP], Joana Silva[SUP] 5 6 [/SUP], Pedro Sousa[SUP] 5 7 [/SUP], Lucas Ribeiro[SUP] 5 [/SUP], Ana Maria Mendonça[SUP] 3 4 [/SUP], Aurélio Campilho[SUP] 3 4 [/SUP]
Affiliations
- PMID: 35449199
- DOI: 10.1038/s41598-022-10568-3
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has impacted healthcare systems across the world. Chest radiography (CXR) can be used as a complementary method for diagnosing/following COVID-19 patients. However, experience level and workload of technicians and radiologists may affect the decision process. Recent studies suggest that deep learning can be used to assess CXRs, providing an important second opinion for radiologists and technicians in the decision process, and super-human performance in detection of COVID-19 has been reported in multiple studies. In this study, the clinical applicability of deep learning systems for COVID-19 screening was assessed by testing the performance of deep learning systems for the detection of COVID-19. Specifically, four datasets were used: (1) a collection of multiple public datasets (284.793 CXRs); (2) BIMCV dataset (16.631 CXRs); (3) COVIDGR (852 CXRs) and 4) a private dataset (6.361 CXRs). All datasets were collected retrospectively and consist of only frontal CXR views. A ResNet-18 was trained on each of the datasets for the detection of COVID-19. It is shown that a high dataset bias was present, leading to high performance in intradataset train-test scenarios (area under the curve 0.55-0.84 on the collection of public datasets). Significantly lower performances were obtained in interdataset train-test scenarios however (area under the curve > 0.98). A subset of the data was then assessed by radiologists for comparison to the automatic systems. Finetuning with radiologist annotations significantly increased performance across datasets (area under the curve 0.61-0.88) and improved the attention on clinical findings in positive COVID-19 CXRs. Nevertheless, tests on CXRs from different hospital services indicate that the screening performance of CXR and automatic systems is limited (area under the curve < 0.6 on emergency service CXRs). However, COVID-19 manifestations can be accurately detected when present, motivating the use of these tools for evaluating disease progression on mild to severe COVID-19 patients.