Development and validation of the diagnostic accuracy of artificial intelligence-assisted ultrasound in the classification of splenic trauma

doi:10.21037/atm-22-3767

	Development and validation of the diagnostic accuracy of artificial intelligence-assisted ultrasound in the classification of splenic trauma
	Jiang, Xue 1; Luo, Yukun 2; He, Xuelei 3; Wang, Kun4 ; Song, Wenjing 1; Ye, Qinggui 1; Feng, Lei 1; Wang, Wei1 ; Hu, Xiaojuan 1; Li, Hua 5
发表期刊	ANNALS OF TRANSLATIONAL MEDICINE
ISSN	2305-5839
	2022-10-01
卷号	10 期号:19 页码:12
通讯作者	Luo, Yukun(lyk301@163.com)
摘要	Background: Spleen is the most vulnerable organ in abdominal trauma. Ultrasound (US) has become an important examination method for splenic trauma. However, the sensitivity of routine US in the diagnosis of splenic trauma is low. Contrast-enhanced ultrasound (CEUS) can improve the sensitivity, but it also has some limitations. This study sought to explore the application value of artificial intelligence (AI)-assisted US in the classification of splenic trauma.Methods: The splenic injuries of Bama miniature pigs were established. A large number of ultrasonic images were collected. Then, 3-fold cross validation (CV) was used to establish the animal models. Next, clinical ultrasonic images were collected at multiple centers. All injuries were diagnosed by CEUS, enhanced CT or surgery. We used animal models to fine tune a small amount of human data, and then established the final AI splenic trauma recognition model. The whole model was constructed by averaging the prediction ability of the 3 fine-tuned models. Finally, 2 doctors' recognition US results of splenic trauma were compared to the AI recognition results. The area under the curve (AUC), sensitivity, specificity, negative predictive value, and positive predictive value were used to evaluate the diagnostic performance in diagnosis of spleen trauma.Results: (I) Based on the receiver operating characteristic (ROC) curves, the test cohort 1 (AUC =0.90) and 2 (AUC =0.84) had a similar performance. Based on the decision curve analysis (DCA) curves, while threshold smaller than 0.8, the proposed model had better performance on test cohort 1 than test cohort 2. Test cohort 1 had higher sensitivity (0.82 vs. 0.71, P<0.01) and higher specificity (0.88 vs. 0.81, P<0.01) than test cohort 2. (II) The diagnostic accuracy of the AI model was higher than that of doctor 1 (0.82 vs. 0.62, P<0.001) and doctor 2 (0.82 vs. 0.66, P<0.001), and its specificity was higher than that of doctor (0.88 vs. 0.78, P=0.001).Conclusions: AI-assisted US diagnosis of splenic trauma can significantly improve the ultrasonic diagnosis rate. We still need to increase the number of samples to further improve the diagnostic efficiency of the model.
关键词	Trauma splenic injury deep learning ultrasound
DOI	10.21037/atm-22-3767
关键词[WOS]	BLUNT ABDOMINAL-TRAUMA ; SONOGRAPHY ; INJURIES
收录类别	SCI
语种	英语
资助项目	Clinical Research Support Fund of PLA General Hospital[ZH19021] ; Major Project of Military Logistical Support Department[ALB19J001]
项目资助者	Clinical Research Support Fund of PLA General Hospital ; Major Project of Military Logistical Support Department
WOS研究方向	Oncology ; Research & Experimental Medicine
WOS类目	Oncology ; Medicine, Research & Experimental
WOS记录号	WOS:000874951400001
出版者	AME PUBLISHING COMPANY
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50496
专题	中国科学院分子影像重点实验室
通讯作者	Luo, Yukun
作者单位	1.Peoples Liberat Army Gen Hosp, Med Ctr 4, Beijing, Peoples R China 2.Peoples Liberat Army Gen Hosp, Med Ctr 1, Beijing, Peoples R China 3.Northwest Univ, Sch Informat Sci & Technol, Xian, Peoples R China 4.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 5.Beijing Mindray Med Instrument Co Ltd, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Jiang, Xue,Luo, Yukun,He, Xuelei,et al. Development and validation of the diagnostic accuracy of artificial intelligence-assisted ultrasound in the classification of splenic trauma[J]. ANNALS OF TRANSLATIONAL MEDICINE,2022,10(19):12.
APA	Jiang, Xue.,Luo, Yukun.,He, Xuelei.,Wang, Kun.,Song, Wenjing.,...&Li, Hua.(2022).Development and validation of the diagnostic accuracy of artificial intelligence-assisted ultrasound in the classification of splenic trauma.ANNALS OF TRANSLATIONAL MEDICINE,10(19),12.
MLA	Jiang, Xue,et al."Development and validation of the diagnostic accuracy of artificial intelligence-assisted ultrasound in the classification of splenic trauma".ANNALS OF TRANSLATIONAL MEDICINE 10.19(2022):12.