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Purpose This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the model-generated confidence score, as determined from the ITS, was verified using the ETS. Results The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

Contrast-enhanced ultrasonography (CEUS) is a noninvasive imaging modality used to diagnose hepatocellular carcinoma (HCC) based on specific imaging features, without the need for pathologic confirmation. Two types of ultrasound contrast agents are commercially available: pure intravascular agents (such as SonoVue) and Kupffer agents (such as Sonazoid). Major guidelines recognize CEUS as a reliable imaging method for HCC diagnosis, although they differ depending on the contrast agents used. The Korean Liver Cancer Association–National Cancer Center guideline includes CEUS with either SonoVue or Sonazoid as a second-line diagnostic technique. However, Sonazoid-enhanced ultrasound is associated with several unresolved issues. This review provides a comparative overview of these contrast agents regarding pharmacokinetic features, examination protocols, diagnostic criteria for HCC, and potential applications in the HCC diagnostic algorithm.