PMC:7782580 / 1165-1578 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"17","span":{"begin":101,"end":109},"obj":"Disease"},{"id":"18","span":{"begin":110,"end":119},"obj":"Disease"},{"id":"19","span":{"begin":195,"end":223},"obj":"Disease"},{"id":"20","span":{"begin":373,"end":381},"obj":"Disease"},{"id":"21","span":{"begin":382,"end":391},"obj":"Disease"}],"attributes":[{"id":"A17","pred":"tao:has_database_id","subj":"17","obj":"MESH:C000657245"},{"id":"A18","pred":"tao:has_database_id","subj":"18","obj":"MESH:D007239"},{"id":"A19","pred":"tao:has_database_id","subj":"19","obj":"MESH:D012141"},{"id":"A20","pred":"tao:has_database_id","subj":"20","obj":"MESH:C000657245"},{"id":"A21","pred":"tao:has_database_id","subj":"21","obj":"MESH:D007239"}],"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_"}],"text":"Liang, Gu and other colleagues develop a convoluted neural network (CNN)-based framework to diagnose COVID-19 infection from chest X-ray and computed tomography images, and comparison with other upper respiratory infections. Compared to expert evaluation of the images, the neural network achieved upwards of 99% specificity, showing promise for the automated detection of COVID-19 infection in clinical settings."}

{"project":"LitCovid-PD-HP","denotations":[{"id":"T1","span":{"begin":201,"end":223},"obj":"Phenotype"}],"attributes":[{"id":"A1","pred":"hp_id","subj":"T1","obj":"http://purl.obolibrary.org/obo/HP_0011947"}],"text":"Liang, Gu and other colleagues develop a convoluted neural network (CNN)-based framework to diagnose COVID-19 infection from chest X-ray and computed tomography images, and comparison with other upper respiratory infections. Compared to expert evaluation of the images, the neural network achieved upwards of 99% specificity, showing promise for the automated detection of COVID-19 infection in clinical settings."}

{"project":"LitCovid-sentences","denotations":[{"id":"T10","span":{"begin":0,"end":224},"obj":"Sentence"},{"id":"T11","span":{"begin":225,"end":413},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Liang, Gu and other colleagues develop a convoluted neural network (CNN)-based framework to diagnose COVID-19 infection from chest X-ray and computed tomography images, and comparison with other upper respiratory infections. Compared to expert evaluation of the images, the neural network achieved upwards of 99% specificity, showing promise for the automated detection of COVID-19 infection in clinical settings."}