ArticleRadiology2022
Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans.
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Article in Radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 3 of them syntheses that pooled it.
0numbers the graph read from it
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47citing papers in PubMed, 3 pooled it
11.1field-weighted citation impact, top 1% of its field
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
47 citing papers in PubMed, 3 syntheses or guidelines pooled it, 96 citations in OpenAlex.
- The role of artificial intelligence in the diagnosis and prognosis of traumatic brain injury based on brain CT scans: a systematic review.Neurosurgical review · 2026Pooled it
- Brain development in newborns and infants after ECMO.World journal of pediatrics : WJP · 2024Pooled it
- Machine learning-based model for predicting inpatient mortality in adults with traumatic brain injury: a systematic review and meta-analysis.Frontiers in neuroscience · 2023Pooled it
- Expanding the applications of artificial intelligence in emergency radiology: Advancing precision medicine and resource efficiency.World journal of radiology · 2026Article
- Radiomics-based machine learning model for predicting secondary decompressive craniectomy in TBI patients after emergent craniotomy with bone flap replacement.Chinese neurosurgical journal · 2026Article
- Smart Nanomedicines for Neurodegenerative Diseases: Empowering New Therapies with Molecular Imaging and Artificial Intelligence.Molecular diagnosis & therapy · 2026Review
- Traumatic brain injury recovery prediction by harmonizing real brain CT and synthetic brain MRI: a pilot study.Brain communications · 2026Article
- A comprehensive model with dual plane ultrasound based deep learning radiomics to distinguish parotid pleomorphic adenoma from Warthin tumor.Frontiers in oncology · 2026Article
- Soft-voting classifier for outcome prediction in traumatic brain injury patients with clear consciousness: model development and error pattern analysis.Neurosurgical review · 2025Article
- Review
- Interpretable Multiomics Models for Predicting Surgical Interventions and Blood Transfusion Requirements in Traumatic Brain Injury.NPJ digital medicine · 2025Article
- Machine learning to identify hypoxic-ischemic brain injury on early head CT after pediatric cardiac arrest.Resuscitation · 2025Article
- Prediction of functional outcomes in aneurysmal subarachnoid hemorrhage using pre-/postoperative noncontrast CT within 3 days of admission.NPJ digital medicine · 2025Article
- Deep learning and radiomics fusion for predicting the invasiveness of lung adenocarcinoma within ground glass nodules.Scientific reports · 2025Article
- Antiseizure Medication Prescription Patterns for Treatment of Post-Traumatic Epilepsy.Neurology. Clinical practice · 2025Article
- Artificial intelligence in resuscitation: a scoping review.Resuscitation plus · 2025Review
- Predicting outcomes after moderate and severe traumatic brain injury using artificial intelligence: a systematic review.NPJ digital medicine · 2025Article
- Timing of Magnetic Resonance Imaging (MRI) in Moderate and Severe TBI: A Systematic Review.Journal of clinical medicine · 2025Review
- Voxel-level radiomics and deep learning for predicting pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant immunotherapy and chemotherapy.Journal for immunotherapy of cancer · 2025Article
- Multi-dimensional interpretable deep learning-radiomics based on intra-tumoral and spatial habitat for preoperative prediction of thymic epithelial tumours risk categorisation.Acta oncologica (Stockholm, Sweden) · 2025Article
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Authors and funding
13 authors at 9 institutions in 1 country.
Matthew Pease *From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.).ORCID 0000-0001-6125-9858
Dooman Arefan *From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.).ORCID 0000-0001-9679-0438
Jason BarberFrom the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.).ORCID 0000-0002-3016-7800
Esther YuhFrom the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.).ORCID 0000-0002-1180-2274
Ava PuccioFrom the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.).ORCID 0000-0003-2569-9607
Kerri HochbergerFrom the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.).ORCID 0000-0002-0260-2449
Enyinna NwachukuFrom the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.).
Souvik RoyFrom the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.).ORCID 0000-0001-9421-2511
Stephanie CasilloFrom the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.).ORCID 0000-0002-6154-5050
Nancy TemkinFrom the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.).ORCID 0000-0002-6392-5292
David O Okonkwo *From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.).
Shandong Wu *From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.).ORCID 0000-0002-0770-2203
TRACK-TBI InvestigatorsFrom the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.).
University of Pittsburgh · USUniversity of California, San Francisco · USMassachusetts General Hospital · USBaylor College of Medicine · USMedical College of Wisconsin · USUniversity of Cincinnati · USUniversity of Maryland, College Park · USUniversity of Washington · USVirginia Commonwealth University · US
Funding
Transforming Research and Clinical Knowledge in Traumatic Brain InjuryU01NS086090 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI DIAZ-ARRASTIA, RAMON, GIACINO, JOSEPH THOMAS · 2013 to 2017
$19.1MDeep interpretation of mammographic images in breast cancer screeningR01CA218405 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI WU, SHANDONG · 2018 to 2021
$1.4MNCI NIH HHS R01 CA218405NINDS NIH HHS U01 NS086090
Abstract
Background After severe traumatic brain injury (sTBI), physicians use long-term prognostication to guide acute clinical care yet struggle to predict outcomes in comatose patients. Purpose To develop and evaluate a prognostic model combining deep learning of head CT scans and clinical information to predict long-term outcomes after sTBI. Materials and Methods This was a retrospective analysis of two prospectively collected databases. The model-building set included 537 patients (mean age, 40 years ± 17 [SD]; 422 men) from one institution from November 2002 to December 2018. Transfer learning and curriculum learning were applied to a convolutional neural network using admission head CT to predict mortality and unfavorable outcomes (Glasgow Outcomes Scale scores 1-3) at 6 months. This was combined with clinical input for a holistic fusion model. The models were evaluated using an independent internal test set and an external cohort of 220 patients with sTBI (mean age, 39 years ± 17; 166 men) from 18 institutions in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study from February 2014 to April 2018. The models were compared with the International Mission on Prognosis and Analysis of Clinical Trials in TBI (IMPACT) model and the predictions of three neurosurgeons. Area under the receiver operating characteristic curve (AUC) was used as the main model performance metric. Results The fusion model had higher AUCs than did the IMPACT model in the prediction of mortality (AUC, 0.92 [95% CI: 0.86, 0.97] vs 0.80 [95% CI: 0.71, 0.88];
Indexed as
Brain Injuries, TraumaticDeep LearningAdultGlasgow Coma ScaleHumansMalePrognosisRetrospective StudiesTomography, X-Ray Computed
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