{"doi":"10.1148/ryai.2020190220","title":"A Multiscale Deep Learning Method for Quantitative Visualization of Traumatic Hemoperitoneum at CT: Assessment of Feasibility and Comparison with Subjective Categorical Estimation","abstract":"Purpose To evaluate the feasibility of a multiscale deep learning algorithm for quantitative visualization and measurement of traumatic hemoperitoneum and to compare diagnostic performance for relevant outcomes with categorical estimation. Materials and Methods This retrospective, single-institution study included 130 patients (mean age, 38 years; interquartile range, 25–50 years; 79 men) with traumatic hemoperitoneum who underwent CT of the abdomen and pelvis at trauma admission between January 2016 and April 2019. Labeled cases were separated into five combinations of training (80%) and test (20%) sets, and fivefold cross-validation was performed. Dice similarity coefficients (DSCs) were compared with those from a three-dimensional (3D) U-Net and a coarse-to-fine deep learning method. Areas under the receiver operating characteristic curve (AUCs) for a composite outcome, including hemostatic intervention, transfusion, and in-hospital mortality, were compared with consensus categorical assessment by two radiologists. An optimal cutoff was derived by using a radial basis function–based support vector machine. Results Mean DSC for the multiscale algorithm was 0.61 ± 0.15 (standard deviation) compared with 0.32 ± 0.16 for the 3D U-Net method and 0.52 ± 0.17 for the coarse-to-fine method (P < .0001). Correlation and agreement between automated and manual volumes were excellent (Pearson r = 0.97, intraclass correlation coefficient = 0.93). The algorithm produced intuitive and explainable visual results. AUCs for automated volume measurement and categorical estimation were 0.86 and 0.77, respectively (P = .004). An optimal cutoff of 278.9 mL yielded accuracy of 84%, sensitivity of 82%, specificity of 93%, positive predictive value of 86%, and negative predictive value of 83%. Conclusion A multiscale deep learning method for traumatic hemoperitoneum quantitative visualization had improved diagnostic performance for predicting hemorrhage-control interventions and mortality compared with subjective volume estimation. Supplemental material is available for this article. Keywords: Abdomen/GI, CT-Quantitative, Computer Aided Diagnosis (CAD) © RSNA, 2020","journal":"Radiology Artificial Intelligence","year":2020,"id":92611,"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":42,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6857,"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":463280,"name":"Yuyin Zhou","orcid":"0000-0003-2232-9563","position":1,"is_corresponding":false},{"id":463281,"name":"Shuhao Fu","orcid":"0000-0002-0995-4922","position":2,"is_corresponding":false},{"id":463282,"name":"Yan Wang","orcid":"0000-0002-1592-9627","position":3,"is_corresponding":false},{"id":463283,"name":"Guang Li","orcid":"0000-0002-9097-572X","position":4,"is_corresponding":false},{"id":463284,"name":"Kathryn Champ","orcid":"0000-0001-9948-3679","position":5,"is_corresponding":false},{"id":463285,"name":"Eliot L. Siegel","orcid":"0000-0002-7458-6281","position":6,"is_corresponding":false},{"id":290832,"name":"Ze Wang","orcid":"0000-0002-8339-5567","position":7,"is_corresponding":false},{"id":464525,"name":"Tina Chen","orcid":null,"position":8,"is_corresponding":false},{"id":56643,"name":"Alan Yuille","orcid":"0000-0001-5207-9249","position":9,"is_corresponding":false},{"id":463279,"name":"David Dreizin","orcid":"0000-0002-0176-0912","position":0,"is_corresponding":true}],"reference_count":38,"raw_metadata":null,"created_at":"2026-07-18T22:30:31.563852Z","pmid":"33330848","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":[]}