ArticleJournal of neurosurgery. Pediatrics2019
Using an artificial neural network to predict traumatic brain injury.
Article in Journal of neurosurgery. Pediatrics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07349901 (Predicting Hospital Readmission for Surgical Patients Using Deep Learning Models With Smart Watch and Smart Ring Sensors Data), which is not on this map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Predicting Hospital Readmission for Surgical Patients Using Deep Learning Models With Smart Watch and Smart Ring Sensors Data
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review.Neurosurgical review · 2020Pooled it
- Toward Child-Centred Artificial Intelligence in Pediatric Emergency Medicine: A Perspective on Clinical Decision Support, Stakeholder Engagement and Education.Pediatric reports · 2026Article
- Addressing class imbalance in traumatic brain injury prognostication: A survey of resampling approaches.AIMS neuroscience · 2026Review
- Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation-A Review.Life (Basel, Switzerland) · 2025Review
- Comparison of Machine Learning Models in Predicting Mental Health Sequelae Following Concussion in Youth.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future.Journal of Korean Neurosurgical Society · 2024Article
- Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives.Biomedicines · 2024Review
- Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care.World journal of emergency surgery : WJES · 2023Review
- Prognosis prediction in traumatic brain injury patients using machine learning algorithms.Scientific reports · 2023Article
- A Machine Learning-Based Approach to Predict Prognosis and Length of Hospital Stay in Adults and Children With Traumatic Brain Injury: Retrospective Cohort Study.Journal of medical Internet research · 2022Article
- Traumatic Brain Injury (TBI) Detection: Past, Present, and Future.Biomedicines · 2022Review
- Prediction of Prognosis in Patients with Trauma by Using Machine Learning.Medicina (Kaunas, Lithuania) · 2022Article
- Development of an artificial intelligence-assisted computed tomography diagnosis technology for rib fracture and evaluation of its clinical usefulness.Scientific reports · 2022Article
- A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months.Acute and critical care · 2022Article
- Prediction of motor function in patients with traumatic brain injury using genetic algorithms modified back propagation neural network: A data-based study.Frontiers in neuroscience · 2022Article
- Machine learning predicts risk of cerebrospinal fluid shunt failure in children: a study from the hydrocephalus clinical research network.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2021Article
- Artificial intelligence in paediatric radiology: Future opportunities.The British journal of radiology · 2021Review
- Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses.Infection and drug resistance · 2019Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 5 institutions in 1 country.
Funding
Abstract
In BriefPediatric traumatic brain injury (TBI) is common, but not all injuries require hospitalization. A computational tool for ruling-in patients who will have clinically relevant TBI (CRTBI) would be valuable, providing an evidence-based mechanism for safe discharge. Here, using data from 12,902 patients from the Pediatric Emergency Care Applied Research Network (PECARN) TBI data set, the authors utilize artificial intelligence to predict CRTBI using radiologist-interpreted CT information with > 99% sensitivity and an AUC of 0.99.
Indexed as
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What OpenQuestion holds
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.