PMC:7782580 / 103-1163 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7782580","sourcedb":"PMC","sourceid":"7782580","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7782580","text":"Coronavirus disease 2019 (COVID-19) is a global pandemic posing significant health risks. The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling. We propose a deep learning framework that identifies COVID-19 from medical images as an auxiliary testing method to improve diagnostic sensitivity. We use pseudo-coloring methods and a platform for annotating X-ray and computed tomography images to train the convolutional neural network, which achieves a performance similar to that of experts and provides high scores for multiple statistical indices (F1 scores \u003e 96.72% (0.9307, 0.9890) and specificity \u003e99.33% (0.9792, 1.0000)). Heatmaps are used to visualize the salient features extracted by the neural network. The neural network-based regression provides strong correlations between the lesion areas in the images and five clinical indicators, resulting in high accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice.","tracks":[{"project":"LitCovid-PubTator","denotations":[{"id":"7","span":{"begin":0,"end":24},"obj":"Disease"},{"id":"8","span":{"begin":26,"end":34},"obj":"Disease"},{"id":"9","span":{"begin":125,"end":133},"obj":"Disease"},{"id":"10","span":{"begin":242,"end":250},"obj":"Disease"},{"id":"11","span":{"begin":1030,"end":1038},"obj":"Disease"}],"attributes":[{"id":"A7","pred":"tao:has_database_id","subj":"7","obj":"MESH:C000657245"},{"id":"A8","pred":"tao:has_database_id","subj":"8","obj":"MESH:C000657245"},{"id":"A9","pred":"tao:has_database_id","subj":"9","obj":"MESH:C000657245"},{"id":"A10","pred":"tao:has_database_id","subj":"10","obj":"MESH:C000657245"},{"id":"A11","pred":"tao:has_database_id","subj":"11","obj":"MESH:C000657245"},{"subj":"7","pred":"source","obj":"LitCovid-PubTator"},{"subj":"8","pred":"source","obj":"LitCovid-PubTator"},{"subj":"9","pred":"source","obj":"LitCovid-PubTator"},{"subj":"10","pred":"source","obj":"LitCovid-PubTator"},{"subj":"11","pred":"source","obj":"LitCovid-PubTator"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}]},{"project":"LitCovid-sentences","denotations":[{"id":"T3","span":{"begin":0,"end":89},"obj":"Sentence"},{"id":"T4","span":{"begin":90,"end":188},"obj":"Sentence"},{"id":"T5","span":{"begin":189,"end":336},"obj":"Sentence"},{"id":"T6","span":{"begin":337,"end":671},"obj":"Sentence"},{"id":"T7","span":{"begin":672,"end":756},"obj":"Sentence"},{"id":"T8","span":{"begin":757,"end":950},"obj":"Sentence"},{"id":"T9","span":{"begin":951,"end":1060},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"attributes":[{"subj":"T3","pred":"source","obj":"LitCovid-sentences"},{"subj":"T4","pred":"source","obj":"LitCovid-sentences"},{"subj":"T5","pred":"source","obj":"LitCovid-sentences"},{"subj":"T6","pred":"source","obj":"LitCovid-sentences"},{"subj":"T7","pred":"source","obj":"LitCovid-sentences"},{"subj":"T8","pred":"source","obj":"LitCovid-sentences"},{"subj":"T9","pred":"source","obj":"LitCovid-sentences"}]}],"config":{"attribute types":[{"pred":"source","value type":"selection","values":[{"id":"LitCovid-PubTator","color":"#93eca4","default":true},{"id":"LitCovid-sentences","color":"#ec939b"}]}]}}