ORIGINAL RESEARCH article
Front. Med.
Sec. Obstetrics and Gynecology
Diagnostic accuracy of artificial intelligence in detection of ovarian cancer-A pilot study
Dipanwita Banerjee 1
Ashok Sharma 2
Ekta Dhamija 3
Sahar Qazi 2
Sandeep R Mathur 2
Neerja Bhatla 2
1. Chittaranjan National Cancer Institute (CNCI), Kolkata, India
2. All India Institute of Medical Sciences New Delhi, New Delhi, India
3. Onco Radiology, BRAIRCH,, All India Institute of Medical Sciences New Delhi Department of Radiodiagnosis, New Delhi, India
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Abstract
Objective: To investigate a panel of variables using four machine learning based classifiers i.e. support vector machine (SVM), random forest (RF), artificial neural network (ANN) and logistic regression (LR) to make a diagnosis of ovarian cancer, differentiating it from benign ovarian masses. Material and Methods: A prospective observational pilot study was done between November 2021 and June 2023. Following data pre-processing to ensure compatibility with ML models, four ML algorithms i.e support vector machine (SVM), logistic regression (LR), random forest (RF) and artificial neural network (ANN) were tested by multimodal parameters from the datasets of 50 patients presenting with suspected epithelial ovarian cancer (Group A) or benign ovarian tumor (Group B). Statistical analysis was done using STATA version 14.0. Results: We found that the machine learning approach could predict malignant tumors with appreciably high accuracy similar to a few studies done so far in this field. All four ML algorithms showed high level of accuracy with a maximum AUROC of 0.92 in the RF model. Both RF and SVM had an accuracy of 85.87% and 83.05%. Conclusion: The ML algorithms can detect ovarian cancers with a high level of accuracy. Further, a large-volume prospective study on large volume data sets is required before inclusion of ML algorithms in clinical practice.
Summary
Keywords
AI and Ovarian cancer, Artificial intelligence in Ovarian Cancer, Diagnosis of Ovarian Cancer by Artificial Intelligence, Machine Learning in Ovarian Cancer, ML in Ovarian Ca, Ovarian Ca and AI
Received
21 October 2025
Accepted
03 February 2026
Copyright
© 2026 Banerjee, Sharma, Dhamija, Qazi, Mathur and Bhatla. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Dipanwita Banerjee; Ashok Sharma
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