Detection of Glottic Neoplasm Based on Voice Signals Using Deep Neural Networks

Use of artificial intelligence in medical practice has considerably increased in recent years. Machine learning algorithms have been successfully applied to detect abnormal voice samples. This letter further investigated the ability of deep neural networks (DNNs) to differentiate voice signals between patients with glottic neoplasms and those with benign voice disorders. We retrospectively included voice samples from 43 patients with histologically proved glottic neoplasm and 129 patients with benign voice disorders. Each neoplastic case was matched with three benign cases (i.e., vocal palsy, atrophy, and phonotraumatic lesions) based on age, sex, and the date of first clinical visit. Mel-frequency cepstrum coefficients were retrieved from the voice samples of a continuous vowel “ah.” The optimal DNN configuration of hidden layers and neurons was systematically investigated. The highest training accuracy (98%) was achieved at four hidden layers, with 500 neurons per layer. By threefold cross validation, the testing accuracy was 86.11% with a detection threshold of 0.2, whereas the sensitivity, specificity, and the area under the receiver operating characteristic curve were 77.78%, 88.89%, and 0.91%, respectively. These results demonstrated that DNNs are a potential tool for detecting and differentiating glottic neoplasms from common benign voice disorders.