Clinical Report: AI in Diagnosing and Managing Acute Appendicitis
Overview
Artificial intelligence (AI), particularly machine learning (ML) models such as random forest (RF), shows promising accuracy in diagnosing acute appendicitis and differentiating complicated cases. Despite encouraging results, current evidence is limited by retrospective study designs and small sample sizes.
Background
Acute appendicitis is a common emergency surgical condition requiring timely and accurate diagnosis. Traditional diagnosis integrates clinical evaluation, biochemical markers, and imaging, with ultrasound and CT scans as key tools. AI, especially ML and deep learning (DL), has emerged as a potential aid to improve diagnostic accuracy and clinical decision-making in emergency surgery. Various AI subfields, including supervised models like logistic regression and random forest, have been applied to appendicitis diagnosis using demographic, clinical, laboratory, and imaging data.
Data Highlights
Study
AI Model
Input Data
Accuracy
Sensitivity
Specificity
AUC
Aydin et al.
Random Forest
Demographics, clinical, laboratory
High
High
High
0.99
Hsieh et al.
Random Forest
Demographics, clinical, biomarkers
>90%
>90%
>90%
Not specified
Mijwil et al.
Random Forest
Laboratory only
Lower
Lower
Lower
Not specified
Key Findings
AI applications in acute appendicitis diagnosis are emerging but currently limited in number.
Random forest (RF) models outperform other machine learning techniques in accuracy, sensitivity, and specificity for appendicitis diagnosis.
Combining demographic, clinical, and laboratory data enhances AI diagnostic performance.
AI can also assist in differentiating complicated from uncomplicated appendicitis forms.
Deep learning remains underused due to complexity despite its potential for feature learning and diagnostic support.
Current studies are mostly retrospective with limited case series, restricting generalizability.
Clinical Implications
AI, especially RF-based models, could become valuable adjuncts in emergency settings to improve diagnostic accuracy for acute appendicitis and guide management decisions. Integration of AI tools with clinical and laboratory data may reduce diagnostic uncertainty and optimize patient outcomes. However, prospective validation and larger datasets are needed before routine clinical implementation.
Conclusion
AI demonstrates promising potential in diagnosing and managing acute appendicitis, with random forest models showing the best current performance. Further research is required to validate these findings and facilitate clinical adoption.
References
De Simone et al. -- Artificial Intelligence in Emergency and Trauma Surgery project
Aydin et al. -- Prospective study on RF accuracy in appendicitis diagnosis
Hsieh et al. -- RF model performance in appendicitis diagnosis
Mijwil et al. -- RF sensitivity and specificity with laboratory data only
World Society of Emergency Surgery Guidelines 2020 -- Diagnosis and treatment of acute appendicitis
