Uterine bioimpedance combined with artificial intelligence as a means of cancer detection.

This study hypothesised that benign and tumour-bearing uterine tissue could be differentiated by their unique electrical bioimpedance patterns, with the aid of artificial intelligence. Twenty whole, ex-vivo uterine specimens were obtained at the time of hysterectomy. A total of 11 benign and 9 malignant specimens were studied. A uterine bioimpedance probe was designed to measure the tissue between the endometrial and serosal layers of the uterus. The impedance data was then analysed with multiple instance learning and principal component analysis, forms of artificial intelligence. Final pathology results for the specimens included uterine sarcoma, adenocarcinoma, carcinosarcoma, and high-grade serous carcinoma. The analysis correctly identified 78% (7/9) of the malignant specimens and 82% (9/11) of the benign specimens. The overall accuracy of our analysis was 80%. Our results demonstrate distinction between electrical impedance properties of malignant and benign uterine specimens. Bioimpedance and artificial intelligence may have potential implications in risk assessment of patients and may subsequently guide surgical decision-making regarding route of organ removal.