Participants diagnosed with oral cancer, oropharyngeal cancer, or oral potentially malignant lesions, as well as healthy controls, had their speech audio recordings collected for the development and validation of AI-driven models for diagnosis and prognosis prediction of oral cancer and oropharyngeal cancer.
Participants were instructed to articulate three sustained vowels (/a/, /i/, /u/) repeatedly at a moderate volume and pace, with three repetitions per vowel and each utterance lasting at least one second. We developed a neuromorphic computing framework that orthogonally decomposes acoustic features into ultra-dimensional omics representations, enabling the characterization of both localized lesions and systemic physiological conditions. The study collected a comprehensive spectrum of biological profiles, including sociodemographic characteristics, tumor metrics, oral function-related factors, patient-reported outcome measures (PROMs), immunoinflammatory indices, and general health status indicators, to thoroughly investigate the paralinguistic representations of transformed speech omics features. These features were then rigorously evaluated for their clinical efficacy across multiple diagnostic tasks, including screening, early detection, pathological diagnosis, disease staging, and risk factor identification.
Study Type
OBSERVATIONAL
Enrollment
501
Department of oral and maxillofacial surgery, Hospital of Stomatology, Sun Yat-sen University
Guangzhou, Guangdong, China
AUC value
Area under the receiver operating characteristic curve (AUC) for discriminating OC/OPC from healthy controls
Time frame: From enrollment to the report of surgical pathology，up to two weeks.
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