In Annals of translational medicine
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.
Jiang Xue, Luo Yukun, He Xuelei, Wang Kun, Song Wenjing, Ye Qinggui, Feng Lei, Wang Wei, Hu Xiaojuan, Li Hua
2022-Oct
Trauma, deep learning, splenic injury, ultrasound
