Deep learning-assisted detection of intracranial hemorrhage: validation and impact on reader performance.
Purpose: Intracranial hemorrhage (ICH) requires urgent treatment, and accurate and timely diagnosis is essential for improving outcomes. This pivotal clinical trial aimed to validate a deep learning algorithm for ICH detection and assess its clinical utility through a reader performance test. Method...
| Publicado en: | Neuroradiology Vol. 67; no. 6; pp. 1511 - 1520 |
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| Autores principales: | , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187382610&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187382610 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Jun2025 vid: 67 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187382610 183890246 187382610 187382610 10.1007/s00234-025-03560-x 187382610 ppf: 1511 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning-assisted detection of intracranial hemorrhage: validation and impact on reader performance. aug: au: Kang, Dong-Wan Kim, Museong Park, Gi-Hun Kim, Yong Soo Han, Moon-Ku Lee, Myungjae Kim, Dongmin Ryu, Wi-Sun Jeong, Han-Gil affil: https://ror.org/00cb3km46 Division of Intensive Care Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea sug: subj: Intracranial Hemorrhage Diagnosis Deep Learning Evaluation Detection Algorithms Evaluation Machine Learning Algorithms Evaluation Diagnosis, Computer Assisted Validity Diagnostic Reasoning Evaluation Human Male Female Adult Middle Age Aged Aged, 80 and Over Validation Studies Retrospective Design Record Review Tomography, X-Ray Computed Hospitals Sensitivity and Specificity Confidence Intervals ROC Curve Descriptive Statistics Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Purpose: Intracranial hemorrhage (ICH) requires urgent treatment, and accurate and timely diagnosis is essential for improving outcomes. This pivotal clinical trial aimed to validate a deep learning algorithm for ICH detection and assess its clinical utility through a reader performance test. Methods: Retrospective CT scans from patients with and without ICH were collected from a tertiary hospital. Two experts evaluated all scans, with a third expert reviewing disagreements for the final diagnosis. We analyzed the performance of the deep learning algorithm, JLK-ICH, for all cases and ICH subtypes. Additional external validation was performed using a multi-ethnic U.S. dataset. A reader performance study included six non-expert readers who evaluated 800 CT scans, with and without JLK-ICH assistance, following a washout period. ICH presence and five-point scale confidence level for decisions were rated. Results: A total of 1,370 CT scans were evaluated. The deep learning model showed 98.7% sensitivity (95% confidence interval [CI] 97.8-99.3%), 88.5% specificity (95% CI, 83.6-92.3%), and an area under the receiver operating characteristic curve (AUROC) of 0.936 (95% CI, 0.915–0.957). The model maintained high accuracy across all ICH subtypes, and additional external validation confirmed these results. In the reader performance study, AUROC with JLK-ICH assistance (0.967 [0.953–0.981]) surpassed that without assistance (0.953 [0.938–0.957]; P = 0.009). JLK-ICH particularly improved performance when readers were highly uncertain. Conclusion: The JLK-ICH algorithm demonstrated high accuracy in detecting all ICH subtypes. Non-expert readers significantly improved diagnostic accuracy for brain CT scans with deep learning assistance. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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