Identification of Appendicitis Using Ultrasound with the Aid of Machine Learning

Diagnosing pediatric appendicitis by ultrasonography (US) is difficult because US requires significant training and skill. We evaluated whether artificial intelligence (AI) can augment US. 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. 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. 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.