ReviewFrontiers in neurology2021
Contribution of CT-Scan Analysis by Artificial Intelligence to the Clinical Care of TBI Patients.
Review in Frontiers in neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?Medicine · 2022Pooled it
- Prediction of therapeutic intensity level from automatic multiclass segmentation of traumatic brain injury lesions on CT-scans.Scientific reports · 2023Trial
- Development of a monoclonal antibody-based time-resolved fluorescence immunochromatographic assay strip for rapid and quantitative determination of ubiquitin C-terminal hydrolase L1 in serum.Mikrochimica acta · 2026Article
- Artificial intelligence-assisted neuropharmacology: reshaping drug discovery and translational strategies for neurological diseases.Turkish journal of medical sciences · 2026Review
- Use of artificial intelligence in diagnosis and prognosis of traumatic brain injury: a scoping review.International journal of emergency medicine · 2025Review
- Artificial intelligence in traumatic brain injury: Brain imaging analysis and outcome prediction: A mini review.World journal of critical care medicine · 2025Review
- Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future.Journal of Korean Neurosurgical Society · 2024Article
- The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Automated volumetric evaluation of intracranial compartments and cerebrospinal fluid distribution on emergency trauma head CT scans to quantify mass effect.Frontiers in neuroscience · 2024Article
- AI-Based Decision Support System for Traumatic Brain Injury: A Survey.Diagnostics (Basel, Switzerland) · 2023Review
- Henry gas solubility optimization double machine learning classifier for neurosurgical patients.PloS one · 2023Article
- The application value of CT radiomics features in predicting pressure amplitude correlation index in patients with severe traumatic brain injury.Frontiers in neurology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The gold standard to diagnose intracerebral lesions after traumatic brain injury (TBI) is computed tomography (CT) scan, and due to its accessibility and improved quality of images, the global burden of CT scan for TBI patients is increasing. The recent developments of automated determination of traumatic brain lesions and medical-decision process using artificial intelligence (AI) represent opportunities to help clinicians in screening more patients, identifying the nature and volume of lesions and estimating the patient outcome. This short review will summarize what is ongoing with the use of AI and CT scan for patients with TBI.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.