AN INNOVATIVE MACHINE LEARNING-BASED ALGORITHM FOR DIAGNOSING PEDIATRIC OVARIAN TORSION.
Authors
Affiliations (4)
Affiliations (4)
- Health Sciences University, Dr. Behcet Uz Pediatric Diseases and Surgery Training and Research Hospital, Department of Pediatric Surgery, Izmir Turkey. Electronic address: [email protected].
- Boston University, College of Engineering, Electrical and Computer Engineering Department, Boston, MA USA; 8 St Mary's St 324, Boston, MA 02215, USA. Electronic address: [email protected].
- Health Sciences University, Dr. Behcet Uz Pediatric Diseases and Surgery Training and Research Hospital, Department of Pediatric Surgery, Izmir Turkey; Ali Fuat Cebesoy mh. 9519 sk no:20 Granada Besli bloklar, C blok Kapı no:1 Karabaglar, Izmir Turkey. Electronic address: [email protected].
- Health Sciences University, Izmir Faculty of Medicine, Dr. Behcet Uz Pediatric Diseases and Surgery Training and Research Hospital, Department of Pediatric Surgery, Izmir Turkey; Ismet kaptan mh. Sezer doğan sk. Dr. Behcet Uz Cocuk Hastalıkları ve Cerrahisi Hastanesi, Konak, Izmir Turkey. Electronic address: [email protected].
Abstract
We aimed to develop a machine-learning(ML) algorithm consisting of physical examination, sonographic findings, and laboratory markers. 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 2013-2023 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. Presence of nausea/vomiting and symptom duration was 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. 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.