{"doi":"10.3390/app15010111","title":"Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography","abstract":"Hematoma expansion (HE) is an independent predictor of poor outcomes and a modifiable treatment target in intracerebral hemorrhage (ICH). Evaluating HE in large datasets requires segmentation of hematomas on admission and follow-up CT scans, a process that is time-consuming and labor-intensive in large-scale studies. Automated segmentation of hematomas can expedite this process; however, cumulative errors from segmentation on admission and follow-up scans can hamper accurate HE classification. In this study, we combined a tandem deep-learning classification model with automated segmentation to generate probability measures for false HE classifications. With this strategy, we can limit expert review of automated hematoma segmentations to a subset of the dataset, tailored to the research team’s preferred sensitivity or specificity thresholds and their tolerance for false-positive versus false-negative results. We utilized three separate multicentric cohorts for cross-validation/training, internal testing, and external validation (n = 2261) to develop and test a pipeline for automated hematoma segmentation and to generate ground truth binary HE annotations (≥3, ≥6, ≥9, and ≥12.5 mL). Applying a 95% sensitivity threshold for HE classification showed a practical and efficient strategy for HE annotation in large ICH datasets. This threshold excluded 47–88% of test-negative predictions from expert review of automated segmentations for different HE definitions, with less than 2% false-negative misclassification in both internal and external validation cohorts. Our pipeline offers a time-efficient and optimizable method for generating ground truth HE classifications in large ICH datasets, reducing the burden of expert review of automated hematoma segmentations while minimizing misclassification rate.","journal":"Applied Sciences","year":2024,"id":472856,"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":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9563,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1269568,"name":"Dmitriy Desser","orcid":"0000-0002-4642-3063","position":1,"is_corresponding":false},{"id":700261,"name":"Tal Zeevi","orcid":"0000-0001-8917-7061","position":2,"is_corresponding":false},{"id":1158309,"name":"Gaby Abou Karam","orcid":"0009-0003-3540-7185","position":3,"is_corresponding":false},{"id":1270025,"name":"Julia Zietz","orcid":null,"position":4,"is_corresponding":false},{"id":1269569,"name":"Andrea Dell’Orco","orcid":"0000-0002-3964-8360","position":5,"is_corresponding":false},{"id":1308758,"name":"M. Keith Chen","orcid":null,"position":6,"is_corresponding":false},{"id":434038,"name":"Ajay Malhotra","orcid":"0000-0001-9223-6640","position":7,"is_corresponding":false},{"id":58037,"name":"Adnan I. Qureshi","orcid":"0000-0003-4962-540X","position":8,"is_corresponding":false},{"id":301019,"name":"Santosh B. Murthy","orcid":"0000-0002-4950-0992","position":9,"is_corresponding":false},{"id":700262,"name":"Shahram Majidi","orcid":"0000-0003-2971-6216","position":10,"is_corresponding":false},{"id":256546,"name":"Guido J. Falcone","orcid":"0000-0002-6407-0302","position":11,"is_corresponding":false},{"id":256547,"name":"Kevin N. Sheth","orcid":"0000-0003-2003-5473","position":12,"is_corresponding":false},{"id":1047779,"name":"Jawed Nawabi","orcid":"0000-0002-1137-0643","position":13,"is_corresponding":false},{"id":549917,"name":"Seyedmehdi Payabvash","orcid":"0000-0003-4628-0370","position":14,"is_corresponding":false},{"id":1263482,"name":"Anh T. Tran","orcid":null,"position":0,"is_corresponding":true}],"reference_count":66,"raw_metadata":null,"created_at":"2026-07-19T02:05:57.032095Z","pmid":"40046237","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":[]}