ReviewInternational journal of environmental research and public health2021
Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives.
Review in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 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.
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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
20 citing papers in PubMed, 4 syntheses or guidelines pooled it, 59 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
- Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study.Biomedical engineering online · 2023Pooled it
- Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?Medicine · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Prediction of therapeutic intensity level from automatic multiclass segmentation of traumatic brain injury lesions on CT-scans.Scientific reports · 2023Trial
- Fast estimation of patient-specific organ doses from abdomen and head CT examinations without segmenting internal organs using machine learning models.Precision radiation oncology · 2025Article
- Deep learning-based prediction of mortality using brain midline shift and clinical information.Heliyon · 2025Article
- The Role of Artificial Intelligence-Powered Imaging in Cerebrovascular Accident Detection.Cureus · 2024Review
- Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data.Journal of personalized medicine · 2023Review
- Comparison of the predictive value of the Helsinki, Rotterdam, and Stockholm CT scores in predicting 6-month outcomes in patients with blunt traumatic brain injuries.Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2023Article
- Maximizing the Clinical Value of Blood-Based Biomarkers for Mild Traumatic Brain Injury.Diagnostics (Basel, Switzerland) · 2023Review
- Contingent negative variation as an evaluation indicator of neurocognitive disorder after traumatic brain injury.Frontiers in psychiatry · 2023Article
- Emerging Materials, Wearables, and Diagnostic Advancements in Therapeutic Treatment of Brain Diseases.Biosensors · 2022Review
- Traumatic Brain Injury (TBI) Detection: Past, Present, and Future.Biomedicines · 2022Review
- Impact of Intracranial Hypertension on Outcome of Severe Traumatic Brain Injury Pediatric Patients: A 15-Year Single Center Experience.Pediatric reports · 2022Article
- Protective role of wogonin following traumatic brain injury by reducing oxidative stress and apoptosis via the PI3K/Nrf2/HO‑1 pathway.International journal of molecular medicine · 2022Article
- Analysis of Vascular Architecture and Parenchymal Damage Generated by Reduced Blood Perfusion in Decellularized Porcine Kidneys Using a Gray Level Co-occurrence Matrix.Frontiers in cardiovascular medicine · 2022Article
- Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization.International journal of environmental research and public health · 2021Review
- Three dimensional convolutional neural network-based automated detection of midline shift in traumatic brain injury cases from head computed tomography scans.Journal of neurosciences in rural practiceArticle
- Automated intracranial hemorrhage detection in traumatic brain injury using 3D CNN.Journal of neurosciences in rural practiceArticle
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 at 4 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traumatic brain injury (TBI) occurs due to the disruption in the normal functioning of the brain by sudden external forces. The primary and secondary injuries due to TBI include intracranial hematoma (ICH), raised intracranial pressure (ICP), and midline shift (MLS), which can result in significant lifetime disabilities and death. Hence, early diagnosis of TBI is crucial to improve patient outcome. Computed tomography (CT) is the preferred modality of choice to assess the severity of TBI. However, manual visualization and inspection of hematoma and its complications from CT scans is a highly operator-dependent and time-consuming task, which can lead to an inappropriate or delayed prognosis. The development of computer aided diagnosis (CAD) systems could be helpful for accurate, early management of TBI. In this paper, a systematic review of prevailing CAD systems for the detection of hematoma, raised ICP, and MLS in non-contrast axial CT brain images is presented. We also suggest future research to enhance the performance of CAD for early and accurate TBI diagnosis.
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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.