ArticleEntropy (Basel, Switzerland)2021
Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset.Radiology. Artificial intelligence · 2024Article
- Artificial Intelligence-What to Expect From Machine Learning and Deep Learning in Hernia Surgery.Journal of abdominal wall surgery : JAWS · 2024Review
- Toward automated interpretable AAST grading for blunt splenic injury.Emergency radiology · 2023Article
- Importance of the neutrophil‑to‑lymphocyte ratio as a prognostic factor in patients with spleen trauma: A single center experience.Biomedical reports · 2022Article
- Entropy in Image Analysis III.Entropy (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The spleen is one of the most frequently injured organs in blunt abdominal trauma. Computed tomography (CT) is the imaging modality of choice to assess patients with blunt spleen trauma, which may include lacerations, subcapsular or parenchymal hematomas, active hemorrhage, and vascular injuries. While computer-assisted diagnosis systems exist for other conditions assessed using CT scans, the current method to detect spleen injuries involves the manual review of scans by radiologists, which is a time-consuming and repetitive process. In this study, we propose an automated spleen injury detection method using machine learning. CT scans from patients experiencing traumatic injuries were collected from Michigan Medicine and the Crash Injury Research Engineering Network (CIREN) dataset. Ninety-nine scans of healthy and lacerated spleens were split into disjoint training and test sets, with random forest (RF), naive Bayes, SVM,
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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.