ArticleJournal of digital imaging2023
Using Deep Learning to Detect the Presence and Location of Hemoperitoneum on the Focused Assessment with Sonography in Trauma (FAST) Examination in Adults.
Article in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Diagnostic accuracy of AI-assisted point-of-care ultrasound for abdominal free fluid detection in FAST trauma assessment: a systematic review and meta-analysis.BMC emergency medicine · 2026Pooled it
- Artificial Intelligence in Point-of-Care Ultrasound: Domains, Barriers and a Framework for Future Development.Journal of the American College of Emergency Physicians open · 2026Article
- Role of artificial intelligence and point of care ultrasound in management of critically ill patients.World journal of critical care medicine · 2026Review
- Point-of-care ultrasound in trauma: a systematic review of recent literature.Journal of ultrasonography · 2026Review
- A Lightweight Network for Free Fluid Detection in Focused Assessment with Sonography in Trauma (FAST) Examination.Bioengineering (Basel, Switzerland) · 2026Article
- An artificial intelligence-based semi-quantitative diagnostic model for intra-abdominal hemorrhage based on focused assessment with sonography for trauma: A large animal experimental study.Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2026Article
- AI-Enhanced POCUS in Emergency Care.Diagnostics (Basel, Switzerland) · 2026Review
- Ghost in the Machine: Leveraging artificial intelligence in the trauma bay.Trauma surgery & acute care open · 2026Review
- Artificial intelligence for abdominopelvic trauma imaging: trends, gaps, and future directions.Abdominal radiology (New York) · 2025Review
- SonoGif.com: A Free Online Tool to Remove Protected Health Information From Any Ultrasound Clip.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2025Article
- Evaluating the digital health technology landscape in sub-Saharan Africa and its implications for cardiovascular health.NPJ cardiovascular health · 2025Review
- Ultrasound findings of free fluid in non-traumatic acute abdomen: a prospective case series study.BMC surgery · 2025Article
- Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications.Abdominal radiology (New York) · 2025Review
- Artificial Intelligence in Emergency Medicine: A Primer for the Nonexpert.Journal of the American College of Emergency Physicians open · 2025Article
- Adaptation of convolutional neural networks for real-time abdominal ultrasound interpretation.Frontiers in artificial intelligence · 2025Article
- Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- Automated detection and segmentation of pleural effusion on ultrasound images using an Attention U-net.Journal of applied clinical medical physics · 2024Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
Abdominal ultrasonography has become an integral component of the evaluation of trauma patients. Internal hemorrhage can be rapidly diagnosed by finding free fluid with point-of-care ultrasound (POCUS) and expedite decisions to perform lifesaving interventions. However, the widespread clinical application of ultrasound is limited by the expertise required for image interpretation. This study aimed to develop a deep learning algorithm to identify the presence and location of hemoperitoneum on POCUS to assist novice clinicians in accurate interpretation of the Focused Assessment with Sonography in Trauma (FAST) exam. We analyzed right upper quadrant (RUQ) FAST exams obtained from 94 adult patients (44 confirmed hemoperitoneum) using the YoloV3 object detection algorithm. Exams were partitioned via fivefold stratified sampling for training, validation, and hold-out testing. We assessed each exam image-by-image using YoloV3 and determined hemoperitoneum presence for the exam using the detection with highest confidence score. We determined the detection threshold as the score that maximizes the geometric mean of sensitivity and specificity over the validation set. The algorithm had 95% sensitivity, 94% specificity, 95% accuracy, and 97% AUC over the test set, significantly outperforming three recent methods. The algorithm also exhibited strength in localization, while the detected box sizes varied with a 56% IOU averaged over positive cases. Image processing demonstrated only 57-ms latency, which is adequate for real-time use at the bedside. These results suggest that a deep learning algorithm can rapidly and accurately identify the presence and location of free fluid in the RUQ of the FAST exam in adult patients with hemoperitoneum.
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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.