Fatima Kayali
Introduction:
Acute appendicitis is a common cause of acute abdomen in secondary care. Despite advancements in diagnostics, misdiagnosis and negative appendectomies remain significant. Artificial Intelligence (AI), particularly machine learning (ML) and deep learning, shows promise in improving diagnostic accuracy.
Methods:
A literature review using PubMed and Cochrane databases included studies on AI’s role in diagnosing and prognosing appendicitis. Studies relying solely on clinical or radiology reports were excluded.
Results:
AI models, particularly random forest (RF), logistic regression (LR), and neural networks (NN), demonstrated high diagnostic accuracy, with RF outperforming others. Machine learning methods like SVM and XGBoost (XGB) were effective in predicting appendicitis prognosis, especially in distinguishing complicated cases. AI models outperformed traditional diagnostic scores, such as the Alvarado score.
Conclusion:
AI has significant potential to enhance the diagnosis and prognosis of acute appendicitis, but challenges in data requirements and standardisation must be addressed for widespread clinical use.
Authors:
Ali Murtad, Glan Clwyd Hospital, Rhyl, United Kingdom
Marco David Bokobza De la Rosa, Lancaster Medical School, Lancaster, United Kingdom
Fatima Kayali, Mersey and West Lancashire NHS Trust, Prescot, United Kingdom
Albert Mensah, University Hospital Sussex, Worthing, United Kingdom
Shuaiyb Majid, Diana Princess of Wales Hospital, Grimsby, United Kingdom
Samuel N.S. Ghattas, Glan Clwyd Hospital, Rhyl, United Kingdom
Samuel S.S. Rezk,Glan Clwyd Hospital, Rhyl, United Kingdom
Ian Williams, University Hospital of Wales, Cardiff, United Kingdom
Damian M. Bailey, Neurovascular Research Laboratory, Faculty of Life Sciences and Education, University of South Wales, Pontypridd, United Kingdom
Matti Jubouri, Hull York Medical School, York, United Kingdom
Mohamad Bashir, Neurovascular Research Laboratory, Faculty of Life Sciences and Education, University of South Wales, Pontypridd, United Kingdom