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Appendicitis is one of the most common surgically treated diseases, with more than 300,000 appendectomies being performed in the United States annually. Despite its common nature, appendicitis can be difficult to diagnose at times even with the established criteria of systems like the Alvarado Score. Atypical presentations and poor predictive value of laboratory tests complicate diagnoses and decisions for surgical intervention. CT imaging improves sensitivity and specificity of diagnoses, however this tool bears the drawbacks of high operator dependency and needless radiation exposure. The need for a framework that can inform selective use of CT scans, especially for equivocally-scored cases of appendicitis, warrants the use of machine learning. Machine learning is a rapidly evolving field with increasing applications in healthcare at-large. This artificial intelligence approach uses historical data to train a model that captures existing patterns in data to predict outcomes. The aim of this review is to classify these novel uses of various machine learning algorithms in the context of appendicitis management and examine their potential to reshape pre-operative and post-operative decision-making.

Appendicitis is one of the most common surgically treated diseases, with more than 300,000 appendectomies being performed in the United States annually. Despite its common nature, appendicitis can be difficult to diagnose at times even with the established criteria of systems like the Alvarado Score. Atypical presentations and poor predictive value of laboratory tests complicate diagnoses and decisions for surgical intervention. CT imaging improves sensitivity and specificity of diagnoses, however this tool bears the drawbacks of high operator dependency and needless radiation exposure. The need for a framework that can inform the selective use of CT scans, especially for equivocally-scored cases of appendicitis, warrants the use of machine learning. Machine learning is a rapidly evolving field with increasing applications in healthcare at-large. This artificial intelligence approach uses historical data to train a model that captures existing patterns in data to predict outcomes. The aim of this review is to classify these novel uses of various machine learning algorithms in the context of appendicitis management and examine their potential to reshape pre-operative and post-operative decision-making.