An innovative machine learning-based algorithm for diagnosing pediatric ovarian torsion.

AIM

We aimed to develop a machine-learning (ML) algorithm consisting of physical examination, sonographic findings, and laboratory markers.

METHODS

The data of 70 patients with confirmed ovarian torsion followed and treated in our clinic for ovarian torsion and 73 patients for control group that presented to the emergency department with similar complaints but didn't have ovarian torsion detected on ultrasound as the control group between 2013 and 2023 were retrospectively analyzed. Sonographic findings, laboratory values, and clinical status of patients were examined and fed into three supervised ML systems to identify and develop viable decision algorithms.

RESULTS

Presence of nausea/vomiting and symptom duration were statistically significant (p < 0.05) for ovarian torsion. Presence of abdominal pain and palpable mass on physical examination weren't significant (p > 0.05). White blood cell count (WBC), neutrophile/lymphocyte ratio (NLR), systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI), high values of C-reactive protein was highly significant in prediction of torsion (p < 0.001, p < 0.05). Ovarian size ratio, medialization, follicular ring sign, presence of free fluid in pelvis in ultrasound demonstrated statistical significance in the torsion group (p < 0.001). We used supervised ML algorithms, including decision trees, random forests, and LightGBM, to classify patients as either control or having torsion. We evaluated the models using 5-fold cross-validation, achieving an average F1-score of 98 %, an accuracy of 98 %, and a specificity of 100 % across each fold with the decision tree model.

CONCLUSION

This study represents the first development of a ML algorithm that integrates clinical, laboratory and ultrasonographic findings for the diagnosis of pediatric ovarian torsion with over 98 % accuracy.