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Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An
Josephine Reismann1, Alessandro Romualdi2, Natalie Kiss1
1Department of Pediatric Surgery, Charité -Universitätsmedizin Berlin, Augustenburger Platz, Berlin, Germany.
Insights
This study developed an AI-powered diagnostic tool for acute appendicitis in children. The method uses routine blood tests and ultrasound data to accurately diagnose appendicitis and differentiate inflammation severity, potentially reducing unnecessary surgeries.
Area of Science:
- Pediatric Surgery
- Medical Diagnostics
- Artificial Intelligence in Medicine
Background:
- Acute appendicitis is a common cause of emergency surgery in children and adolescents.
- Current diagnostic methods for appendicitis, including clinical presentation, blood markers, and ultrasound, often lack sufficient accuracy and require expert interpretation.
- There is a need for objective and reliable diagnostic tools to improve appendicitis management and reduce unnecessary interventions.
Purpose of the Study:
- To develop and validate an automated diagnostic method for acute appendicitis in pediatric patients.
- To differentiate between complicated and uncomplicated appendicitis using routinely available clinical and imaging data.
- To assess the potential of machine learning (ML) and artificial intelligence (AI) algorithms in improving appendicitis diagnostics.
Main Methods:
- Retrospective analysis of data from 590 pediatric patients (0-17 years) with suspected appendicitis.
- Inclusion of routine parameters: full blood counts, C-reactive protein (CRP), and appendiceal diameter from ultrasound.
- Application of ML/AI algorithms for biomarker signature discovery and classification model training and validation.
Main Results:
- The AI-based biomarker signature achieved 90% accuracy (93% sensitivity, 67% specificity) for diagnosing appendicitis in validation data.
- The method showed 51% accuracy (95% sensitivity, 33% specificity) in differentiating complicated from uncomplicated inflammation.
- The diagnostic approach has the potential to prevent unnecessary surgeries in a significant proportion of patients.
Conclusions:
- AI and ML algorithms can significantly enhance the diagnostic accuracy of acute appendicitis using routine parameters.
- This automated diagnostic method offers a promising approach to optimize surgical decision-making in pediatric appendicitis.
- The findings suggest a potential shift in the therapeutic approach for appendicitis, improving patient outcomes and resource utilization.
Abstract:
Acute appendicitis is one of the major causes for emergency surgery in childhood and adolescence. Appendectomy is still the therapy of choice, but conservative strategies are increasingly being studied for uncomplicated inflammation. Diagnosis of acute appendicitis remains challenging, especially due to the frequently unspecific clinical picture. Inflammatory blood markers and imaging methods like ultrasound are limited as they have to be interpreted by experts and still do not offer sufficient diagnostic certainty. This study presents a method for automatic diagnosis of appendicitis as well as the differentiation between complicated and uncomplicated inflammation using values/parameters which are routinely and unbiasedly obtained for each patient with suspected appendicitis. We analyzed full blood counts, c-reactive protein (CRP) and appendiceal diameters in ultrasound investigations corresponding to children and adolescents aged 0-17 years from a hospital based population in Berlin, Germany. A total of 590 patients (473 patients with appendicitis in histopathology and 117 with negative histopathological findings) were analyzed retrospectively with modern algorithms from machine learning (ML) and artificial intelligence (AI). The discovery of informative parameters (biomarker signatures) and training of the classification model were done with a maximum of 35% of the patients. The remaining minimum 65% of patients were used for validation. At clinical relevant cut-off points the accuracy of the biomarker signature for diagnosis of appendicitis was 90% (93% sensitivity, 67% specificity), while the accuracy to correctly identify complicated inflammation was 51% (95% sensitivity, 33% specificity) on validation data. Such a test would be capable to prevent two out of three patients without appendicitis from useless surgery as well as one out of three patients with uncomplicated appendicitis. The presented method has the potential to change today's therapeutic approach for appendicitis and demonstrates the capability of algorithms from AI and ML to significantly improve diagnostics even based on routine diagnostic parameters.
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