AIMC Topic: Abdominal Injuries
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SMART: Development and Application of a Multimodal Multi-organ Trauma Screening Model for Abdominal Injuries in Emergency Settings.
Academic radiology
Dec 16, 2024
RATIONALE AND OBJECTIVES: Effective trauma care in emergency departments necessitates rapid diagnosis by interdisciplinary teams using various medical data. This study constructed a multimodal diagnostic model for abdominal trauma using deep learning...
A quality assessment tool for focused abdominal sonography for trauma examinations using artificial intelligence.
The journal of trauma and acute care surgery
Dec 14, 2024
BACKGROUND: Current tools to review focused abdominal sonography for trauma (FAST) images for quality have poorly defined grading criteria or are developed to grade the skills of the sonographer and not the examination. The purpose of this study is t...
Prediction of intra-abdominal injury using natural language processing of electronic medical record data.
Surgery
Jul 6, 2024
BACKGROUND: This study aimed to use natural language processing to predict the presence of intra-abdominal injury using unstructured data from electronic medical records.
The application of deep learning in abdominal trauma diagnosis by CT imaging.
World journal of emergency surgery : WJES
May 6, 2024
BACKGROUND: Abdominal computed tomography (CT) scan is a crucial imaging modality for creating cross-sectional images of the abdominal area, particularly in cases of abdominal trauma, which is commonly encountered in traumatic injuries. However, inte...
Deep Learning for Automated Detection and Localization of Traumatic Abdominal Solid Organ Injuries on CT Scans.
Journal of imaging informatics in medicine
Feb 16, 2024
Computed tomography (CT) is the most commonly used diagnostic modality for blunt abdominal trauma (BAT), significantly influencing management approaches. Deep learning models (DLMs) have shown great promise in enhancing various aspects of clinical pr...
Machine Learning Detection and Characterization of Splenic Injuries on Abdominal Computed Tomography.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Jan 8, 2024
BACKGROUND: Multi-detector contrast-enhanced abdominal computed tomography (CT) allows for the accurate detection and classification of traumatic splenic injuries, leading to improved patient management. Their effective use requires rapid study inter...
The three-dimensional weakly supervised deep learning algorithm for traumatic splenic injury detection and sequential localization: an experimental study.
International journal of surgery (London, England)
May 1, 2023
BACKGROUND: Splenic injury is the most common solid visceral injury in blunt abdominal trauma, and high-resolution abdominal computed tomography (CT) can adequately detect the injury. However, these lethal injuries sometimes have been overlooked in c...
Using Machine Learning to Identify Change in Surgical Decision Making in Current Use of Damage Control Laparotomy.
Journal of the American College of Surgeons
Jan 9, 2019
BACKGROUND: In an earlier study, we reported the successful reduction in the use of damage control laparotomy (DCL); however, no change in the relative frequencies of specific indications was observed. In this study, we aimed to use machine learning ...
Image Segmentation and Machine Learning for Detection of Abdominal Free Fluid in Focused Assessment With Sonography for Trauma Examinations: A Pilot Study.
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Oct 13, 2016
The objective of this pilot study was to test the feasibility of automating the detection of abdominal free fluid in focused assessment with sonography for trauma (FAST) examinations. Perihepatic views from 10 FAST examinations with positive results ...
RSNA 2023 Abdominal Trauma AI Challenge: Review and Outcomes.
Radiology. Artificial intelligence
Jan 1, 2025
Purpose To evaluate the performance of the winning machine learning models from the 2023 RSNA Abdominal Trauma Detection AI Challenge. Materials and Methods The competition was hosted on Kaggle and took place between July 26 and October 15, 2023. The...
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