{"doi":"10.1101/2020.05.19.20106336","title":"ROBUST COVID-19-RELATED CONDITION CLASSIFICATION NETWORK","abstract":"COVID-19 can exponentially precipitate life-threatening emergencies as witnessed during the recent spreading of a novel coronavirus infection which can rapidly evolve into lung collapse and respiratory distress (among other various severe clinical conditions). Our study evaluates the performance of a tailor-designed deep convolutional network on the tasks of early detection and localization of radiological signs associated to COVID-19 on frontal chest X-rays. We also asses the frameworks capacity in differentiating the above-mentioned signs, which are usually confused with the more usual common bacterial and viral pneumonias. Open-source chest X-ray images categorized as Normal, Non-COVID-19 and COVID-19 pneumonias were downloaded from the NIH (n=2,259), RSNA (n=600) and HM Hospitales (n=2,307). Our algorithmic framework was able to precisely detect the images with COVID19- related radiological findings (mean Accuracy: 90.5%; Sensitivity: 80.6%; Specificity: 98.0%), whilst correctly categorizing images deemed as Non-COVID-19 pneumonias (mean Accuracy: 88.4%; Sensitivity: 93.3%; Specificity: 92.0%) and normal chest X- rays (mean Accuracy 92.1%; Sensitivity: 91.8%; Specificity: 94.3%). The associated results show that our AI framework is able to classify COVID-19 accurately, making of it a potential tool to improve the diagnostic performance across primary-care centres and, to grant priority to a subset of algorithmic selected images for urgent follow-on expert review. This would sensibly accelerate diagnosis in remote locations, reduce the bottleneck on specialized centres, and/or help to alleviate the needs on situations of scarcity in the availability of molecular tests.","journal":"medRxiv","year":2020,"id":129716,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9651,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":581970,"name":"Martín Belvisi","orcid":null,"position":1,"is_corresponding":false},{"id":581368,"name":"Carlos M. Galmarini","orcid":"0000-0001-5494-6617","position":2,"is_corresponding":false},{"id":581969,"name":"Maximiliano Lucius","orcid":null,"position":0,"is_corresponding":true}],"reference_count":25,"raw_metadata":null,"created_at":"2026-07-18T23:15:49.682497Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}