## 2 Related Work
### Predictive Clinical Scoring Systems  
Several recent studies focus on clinical scoring systems to differentiate between complicated and uncomplicated appendicitis, aiming to reduce unnecessary surgical interventions. [PERSON] et al. [12] evaluated the Appendicitis Inflammatory Response (AIR) score, finding high sensitivity (89.5%) and specificity (71.9%) for distinguishing perforated from non-perforated appendicitis. However, limitations include data imbalance and lack of external validation.  
[PERSON] et al. [13] performed a multicenter evaluation of 12 clinical prediction rules (CPRs) for pediatric appendicitis. The Gorter CPR, validated externally, achieved a sensitivity of 86% and specificity of 91%. The study's strengths include its rigorous methodology and use of objective variables to improve reproducibility.  
Both studies provide valuable insights into scoring systems for pediatric appendicitis, with [PERSON] et al. emphasizing sensitivity and [PERSON] et al. offering a broader multicenter perspective, showcasing the utility of scoring systems in guiding clinical decision-making.  
A study by [PERSON] [14] investigated the predictive performance of six different ML algorithms in distinguishing between simple and complicated acute appendicitis. The authors analyzed data from 1132 patients who underwent emergency appendectomy between 2012 and 2022. They identified significant features such as age, C-reactive protein levels, and periappendicular liquid collection. Among the algorithms tested, k-nearest neighbors and logistic regression achieved the highest accuracy of 96% in the validation group. The study concludes that, using a minimal set of input features, the severity of acute appendicitis can be predicted with high accuracy, offering a practical tool for clinical decision-making.  
A comprehensive overview of artificial intelligence (AI) applications in diagnosing acute appendicitis, especially in pediatric cases, is provided by [PERSON] et al. [15]. The authors assess various AI and ML models, analyzing their effectiveness and diagnostic accuracy compared with traditional diagnostic methods. Key findings suggest that AI models, particularly those utilizing image-based diagnostics and clinical feature analysis, can significantly enhance diagnostic precision. However, the review identifies challenges in implementing these models clinically, such as a lack of interpretability, concerns with data quality, and issues with model generalizability across different populations. The study emphasizes the potential of AI to aid in timely and accurate diagnosis, while also highlighting areas that require further development for clinical adoption.  
A method for developing interpretable medical risk scores tailored for pediatric appendicitis is presented by [PERSON] et al. [16]. The study applies ML techniques to create risk scores that are both clinically interpretable and actionable, helping healthcare providersassess the likelihood of appendicitis and make informed decisions. Using real-world clinical data, the authors demonstrate that their risk scores can effectively guide clinical decisions with a balance of transparency and predictive accuracy. The work underscores the importance of model interpretability in clinical settings, aiming to build trust and utility among healthcare professionals. By focusing on interpretability and real-time clinical application, this approach offers a promising tool for enhancing pediatric appendicitis management.