Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2026
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.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Liu Chang, Li Yang, Tang Hao, Han Dong, Zhang Yi, Lai Jiangyuan, Zhang Yusheng, Xiao Yao, Zhang Yingying, Zhao Dongchu, Li Tao, Fang Jingqin, Tian Yinli, Zhang Lianyang
Аннотация
PURPOSE: To develop and validate an artificial intelligence model based on focused assessment with sonography for trauma (FAST) for the semi-quantitative grading of intra-abdominal hemorrhage resulting from blunt abdominal trauma, particularly for use in prehospital or resource-limited settings.
METHODS: Nine Bama miniature pigs, mean weight (31.46 ± 3.73) kg were enrolled. Graded hemorrhage from 0 to 1000 mL was simulated by infusing 100 mL of autologous arterial blood into the peritoneal cavity at each step. The hemorrhage volume was mapped to 3 grades based on total blood volume (estimated at 65 mL/kg): Grade I (< 15%), Grade II (15% - 30%), and Grade III (> 30%). FAST ultrasound videos were acquired from 6 standard sites: right upper quadrant-1, right upper quadrant-2, left upper quadrant-1, left upper quadrant-2, right pelvic cavity, and left pelvic cavity. The pixel area of hemorrhage was obtained by manually segmenting the frame with the largest fluid collection using ITK-SNAP, and the corresponding scanning depth was recorded. A linear mixed-effects model was used to assess the impact of scanning depth on pixel area. A deep neural network, incorporating class weighting and dynamic probability threshold optimization, was constructed using a multimodal feature set including animal weight, pixel areas and scanning depths from each site, and the total pixel area. A 3-grade classification was performed. The model's performance was evaluated using leave-one-out cross-validation on an animal basis and compared with logistic regression, random forest, gradient boosting decision tree, and support vector machine.
RESULTS: A total of 797 raw videos were acquired, with 522 videos comprising 87 data groups (each covering 6 sites) included after screening. As hemorrhage volume increased, heart rate and shock index rose, while systolic blood pressure decreased; at 800 mL of hemorrhage, the shock index was 2.31 ± 0.38. The mixed-effects model revealed a significant negative correlation between scanning depth and pixel area (β = -2099.00, SE = 1041.13, z = -2.02, p = 0.044). The proposed model achieved an overall accuracy of 81.19%, outperforming support vector machine (73.77%), gradient boosting decision tree (70.63%), random forest (69.52%), and logistic regression (65.99%).
CONCLUSION: In a porcine model of blunt abdominal trauma, a multimodal artificial intelligence approach based on FAST multi-site pixel area features, combined with a deep neural network optimized by class weighting and dynamic probability thresholds, can achieve semi-quantitative grading of intra-abdominal hemorrhage.
METHODS: Nine Bama miniature pigs, mean weight (31.46 ± 3.73) kg were enrolled. Graded hemorrhage from 0 to 1000 mL was simulated by infusing 100 mL of autologous arterial blood into the peritoneal cavity at each step. The hemorrhage volume was mapped to 3 grades based on total blood volume (estimated at 65 mL/kg): Grade I (< 15%), Grade II (15% - 30%), and Grade III (> 30%). FAST ultrasound videos were acquired from 6 standard sites: right upper quadrant-1, right upper quadrant-2, left upper quadrant-1, left upper quadrant-2, right pelvic cavity, and left pelvic cavity. The pixel area of hemorrhage was obtained by manually segmenting the frame with the largest fluid collection using ITK-SNAP, and the corresponding scanning depth was recorded. A linear mixed-effects model was used to assess the impact of scanning depth on pixel area. A deep neural network, incorporating class weighting and dynamic probability threshold optimization, was constructed using a multimodal feature set including animal weight, pixel areas and scanning depths from each site, and the total pixel area. A 3-grade classification was performed. The model's performance was evaluated using leave-one-out cross-validation on an animal basis and compared with logistic regression, random forest, gradient boosting decision tree, and support vector machine.
RESULTS: A total of 797 raw videos were acquired, with 522 videos comprising 87 data groups (each covering 6 sites) included after screening. As hemorrhage volume increased, heart rate and shock index rose, while systolic blood pressure decreased; at 800 mL of hemorrhage, the shock index was 2.31 ± 0.38. The mixed-effects model revealed a significant negative correlation between scanning depth and pixel area (β = -2099.00, SE = 1041.13, z = -2.02, p = 0.044). The proposed model achieved an overall accuracy of 81.19%, outperforming support vector machine (73.77%), gradient boosting decision tree (70.63%), random forest (69.52%), and logistic regression (65.99%).
CONCLUSION: In a porcine model of blunt abdominal trauma, a multimodal artificial intelligence approach based on FAST multi-site pixel area features, combined with a deep neural network optimized by class weighting and dynamic probability thresholds, can achieve semi-quantitative grading of intra-abdominal hemorrhage.