Identification of Appendicitis Using Ultrasound with the Aid of Machine Learning.
J Laparoendosc Adv Surg Tech A
; 31(12): 1412-1419, 2021 Dec.
Article
em En
| MEDLINE
| ID: mdl-34748429
ABSTRACT
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 andMethods:
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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Apendicite
/
Apêndice
Tipo de estudo:
Diagnostic_studies
Limite:
Child
/
Humans
Idioma:
En
Revista:
J Laparoendosc Adv Surg Tech A
Ano de publicação:
2021
Tipo de documento:
Article