The accuracy of an Online Sequential Extreme Learning Machine in detecting voice pathology using the Malaysian Voice Pathology Database.

BACKGROUND: A multidimensional voice quality assessment is recommended for all patients with dysphonia, which requires a patient visit to the otolaryngology clinic. The aim of this study was to determine the accuracy of an online artificial intelligence classifier, the Online Sequential Extreme Learning Machine (OSELM), in detecting voice pathology. In this study, a Malaysian Voice Pathology Database (MVPD), which is the first Malaysian voice database, was created and tested. METHODS: The study included 382 participants (252 normal voices and 130 dysphonic voices) in the proposed database MVPD. Complete data were obtained for both groups, including voice samples, laryngostroboscopy videos, and acoustic analysis. The diagnoses of patients with dysphonia were obtained. Each voice sample was anonymized using a code that was specific to each individual and stored in the MVPD. These voice samples were used to train and test the proposed OSELM algorithm. The performance of OSELM was evaluated and compared with other classifiers in terms of the accuracy, sensitivity, and specificity of detecting and differentiating dysphonic voices. RESULTS: The accuracy, sensitivity, and specificity of OSELM in detecting normal and dysphonic voices were 90%, 98%, and 73%, respectively. The classifier differentiated between structural and non-structural vocal fold pathology with accuracy, sensitivity, and specificity of 84%, 89%, and 88%, respectively, while it differentiated between malignant and benign lesions with an accuracy, sensitivity, and specificity of 92%, 100%, and 58%, respectively. Compared to other classifiers, OSELM showed superior accuracy and sensitivity in detecting dysphonic voices, differentiating structural versus non-structural vocal fold pathology, and between malignant and benign voice pathology. CONCLUSION: The OSELM algorithm exhibited the highest accuracy and sensitivity compared to other classifiers in detecting voice pathology, classifying between malignant and benign lesions, and differentiating between structural and non-structural vocal pathology. Hence, it is a promising artificial intelligence that supports an online application to be used as a screening tool to encourage people to seek medical consultation early for a definitive diagnosis of voice pathology.

Alarming triad of progressive hoarseness in a male smoker.

Hoarseness accounts for 1% of all consultations in primary care. Suspicion of malignancy should be considered in individuals with risk factors presenting with unexplained hoarseness lasting more than two weeks. A significant number of patients with laryngeal cancer present at an advanced stage due to lack of awareness regarding vocal health. It is important to educate both the public and primary care health providers concerning laryngeal cancer. We present the case of an 81-year- old male smoker who presented to us with a six-month history of progressive hoarseness. He was initially treated in two primary and one secondary care centres, where a diagnosis of laryngeal cancer was not considered. Careful assessment in our centre managed to determine a diagnosis of T3N0M0 glottic carcinoma. We will discuss this alarming triad of progressive hoarseness in a male smoker to help primary care physicians streamline their thoughts and identify red flags in a hoarse patient.