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Original Research

Open Access

Effectiveness of artificial intelligence algorithms in identification of patients with high-grade histopathology after conisation

  • Marko Mlinarič1,*,†,
  • Maša Mlinarič2,†
  • Miljenko Križmarić3,†
  • Iztok Takač4,5,†
  • Alenka Repše Fokter6,†

1Outpatient clinic for gynaecology and obstetrics, 1410 Zagorje ob Savi, Slovenia

2General Hospital Trbovlje, 1420 Trbovlje, Slovenia

3University of Maribor Faculty of Medicine, 2000 Maribor, Slovenia

4University Clinic of Gynaecology and Perinatology, University Medical Centre Maribor, 2000 Maribor, Slovenia

5Department of Gynaecology and Perinatology, Faculty of Medicine, University of Maribor, 2000 Maribor, Slovenia

6Department of Pathology and Cytology, General Hospital Celje, 3000 Celje, Slovenia

DOI: 10.22514/ejgo.2023.050 Vol.44,Issue 4,August 2023 pp.66-75

Submitted: 26 October 2022 Accepted: 11 May 2023

Published: 15 August 2023

*Corresponding Author(s): Marko Mlinarič E-mail:

† These authors contributed equally.


The aim of this study was to compare effectiveness of various artificial intelligence classification algorithms in identifying patients with high-grade final histopathology of conisation based on last PAP smear result and risk factors for development of uterine cervical dysplasia and cancer. The data of 1475 patients who underwent conisation surgery at University Clinical Centre Maribor between 1993–2005 were analysed. Synthetic Minority Oversampling Technique (SMOTE) algorithm was employed for the imbalanced data correction. Various classification algorithms were tested with Weka open-source software. The 10-fold cross validation was used to define testing and hold-out set for analysis. Random Forest (RF) classification algorithm was better than the other tested algorithms and achieved 89.42% correct classifications (baseline ZeroR classification 63.4%, sensitivity 96.80%, specificity 76.60%, kappa 0.7632, Area under Receiver Operation Characteristic curve (AUC ROC) 0.911, Precision Recall curve (PRC) Area 0.916, and Matthews Correlation Coefficient (MCC) 0.771. Random Forest (RF) algorithm correctly identified majority of patients with final high-grade histopathology of conisation from patients dataset based on last PAP smear result and risk factors of developing high-grade dysplasia and carcinoma. Such algorithms can help clinicians to identify high-risk patients in future. An invitation could be sent to patients who did not participate in organized screening program, thus preventing the serious disease. Further studies are required in this regard.


Uterine cervical dysplasia; Uterine cervical cancer; Conisation; Artificial intelligence

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Marko Mlinarič,Maša Mlinarič,Miljenko Križmarić,Iztok Takač,Alenka Repše Fokter. Effectiveness of artificial intelligence algorithms in identification of patients with high-grade histopathology after conisation. European Journal of Gynaecological Oncology. 2023. 44(4);66-75.


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