Deep learning-based electrical impedance spectroscopy analysis for malignant and potentially malignant oral disorder detection

Abstract

Abstract Electrical impedance spectroscopy (EIS) is a powerful tool used to investigate the properties of materials and biological tissues. This study presents one of the first applications of EIS for the detection and classification of oral potentially malignant disorders (OPMDs) and oral cancer. We aimed to apply EIS in conjunction with deep learning to assist the clinical diagnosis of OPMD and oral cancer as a non-invasive diagnostic technology. Currently, the diagnosis of OPMD and oral cancer relies on clinical examination and histopathological analysis of invasive scalpel tissue biopsies, which is stressful for patients, time-consuming for clinicians and subject to histopathological interobserver variation in diagnosis, although recent advances in artificial intelligence may circumvent discrepancy. Here we developed a novel deep learning convolutional neural network (CNN)-based method to automatically differentiate normal, OPMD and malignant oral tissues using EIS measurements. EIS readings were initially taken from untreated or glacial acetic acid-treated porcine oral mucosa and analyzed via CNN to determine if this method could discriminate between normal and damaged oral epithelium. CNN models achieved area under the curve (AUC) values of 0.92 ± 0.03, with specificity 0.95 and sensitivity 0.84, showing good discrimination. EIS data from ventral tongue and floor-of-the-mouth were collected from 51 healthy humans and 11 patients with OPMD and oral cancer. When a binary classification (low or high risk of malignancy) was applied, the best CNN model achieved an AUC 0.91 ± 0.1, with accuracy 0.91 ± 0.05, specificity 0.97 and sensitivity 0.74. These results demonstrate the considerable potential of EIS in combination with CNN models as an adjunctive non-invasive diagnostic tool for OPMD and oral cancer.