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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.
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BACKGROUND: The diagnosis of burn depth often relies on the subjective judgment of plastic surgeons. Contrast-enhanced ultrasound (CEUS) can visualize the microcirculation well and has potential value in diagnosing the depth of burn wounds. We assessed the depth of the burn wounds by CEUS, and compared the results with histological examination. METHODS: Two rhesus monkeys were used, and multiple burn wounds with different depths were made on their backs. The echo of the dermis and subcutaneous tissue were observed for each wound, and the thickness of the dermis was measured. CEUS was performed to evaluate the depth of burn wounds and compared with pathological results. RESULTS: (I) After scalding, dermal tissue edema occurred, and the thickness of the dermis measured by a US tended to increase gradually, related to the time of scalding and the order of measurement. (II) With the prolongation of the burn time, the depth of filling by contrast agent gradually increased, from the superficial dermis to the deep dermis and subcutaneous tissue, indicating that the depth of tissue damage gradually increased. This was consistent with the pathological observation. The thickness of the healthy dermis was about 1.3-1.8 mm, and 2.7-4.1 mm after scalding. The depth of the burn wounds was 0.9-4.1 mm, accounting for 32-100% of the full skin thickness. CONCLUSIONS: CEUS is a convenient and fast examination method that is consistent with pathological diagnosis of the depth of burn wounds and could prove valuable for the accurate assessment of burn injuries.
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OBJECTIVE: To investigate the correlation between Young's modulus of the thyroid tissue measured by in shear wave elastography (SWE) and the clinical manifestations of Hashimoto's thyroiditis in different stages. METHODS: A total of 104 patients with the clinical diagnosis of Hashimoto's thyroiditis were enrolled in this study, including 26 with hyperthyroidism, 29 with normal thyroid function, 27 with subclinical hypothyroidism, 22 with clinical hypothyroidism, with 50 healthy volunteers serving as the healthy control group. All the subjects underwent SWE to obtain the Young's modulus value of the thyroid tissue. Univariate analysis of variance was used to compare the Young's modulus among the groups, and Pearson correlation analysis was used to analyze the correlation between the Young's modulus of the thyroid tissue and serum levels of thyroid microsomal antibody (TMAb) and thyroid globulin antibody (TGAb). RESULTS: In the 4 groups of patients, the Young's modulus increased significantly in the order of hyperthyroidism group, normal thyroid function group, subclinical hypothyroidism group and clinical hypothyroidism group (F=60.983, P<0.01). The Young's modulus was significantly lower in hyperthyroidism group than in the other 3 groups (P<0.05), and was significantly lower in normal thyroid function group than in subclinical hypothyroidism group and clinical hypothyroidism group (P<0.05). CONCLUSION: The Young's modulus of the thyroid tissue measured by shear wave elastography is related with the clinical manifestations of Hashimoto's thyroiditis in different stages, but the relevance needs to be further confirmed by multi-center, randomized, controlled studies involving a larger sample size.