Identification of Appendicitis Using Ultrasound with the Aid of Machine Learning

Background: Diagnosing pediatric appendicitis by ultrasonography (US) is difficult because US requires significant training and skill. We evaluated whether artificial intelligence (AI) can augment US. Materials and Methods: Among 70 abdominal ultrasound videos containing 85-347 images each, 50 were used to train the AI neural network. Each video was categorized based on the detection percentage and percent accuracy: most (>50%), partial (10-50%), and none (<10%). Test 1 involved verification of appendix detection by AI using the remaining 20 videos. Test 2 involved the evaluation of the effect of AI utilization on pediatricians. Results: From 50 videos, 6914 images were used to train the AI network. In test 1, 3 pediatric surgeons judged 10 (50.0%), 4 (20.0%), and 6 (30.0%) videos as "most," "partial," and "none," respectively, regarding the detection percentage; 7 (35.0%), 7 (35.0%), and 6 (30.0%) videos were judged, respectively, concerning the percent accuracy. Five (83.3%) of six test videos with a scan area depth of 8 cm were judged as "none" for both detection and accuracy. In test 2, six videos were also judged as "none" for both categories, showing a negative effect on the participants (5 pediatric residents and 5 pediatric intensive-emergency fellows), but the other categories showed little negative effect. Conclusions: Appendicitis in a shallow US scan area can be easily identified with AI support. Even with the detection of a partial appendicitis shadow, AI is still helpful. However, if AI does not detect appendicitis at all, examiners may be negatively affected.