ABSTRACT
BACKGROUND: Studies have used questionnaires of dysphonic symptoms to screen voice disorders. This study investigated whether the differential presentation of demographic and symptomatic features can be applied to computerized classification. METHODS: We recruited 100 patients with glottic neoplasm, 508 with phonotraumatic lesions, and 153 with unilateral vocal palsy. Statistical analyses revealed significantly different distributions of demographic and symptomatic variables. Machine learning algorithms, including decision tree, linear discriminant analysis, K-nearest neighbors, support vector machine, and artificial neural network, were applied to classify voice disorders. RESULTS: The results showed that demographic features were more effective for detecting neoplastic and phonotraumatic lesions, whereas symptoms were useful for detecting vocal palsy. When combining demographic and symptomatic variables, the artificial neural network achieved the highest accuracy of 83 ± 1.58%, whereas the accuracy achieved by other algorithms ranged from 74 to 82.6%. Decision tree analyses revealed that sex, age, smoking status, sudden onset of dysphonia, and 10-item voice handicap index scores were significant characteristics for classification. CONCLUSION: This study demonstrated a significant difference in demographic and symptomatic features between glottic neoplasm, phonotraumatic lesions, and vocal palsy. These features may facilitate automatic classification of voice disorders through machine learning algorithms.
Subject(s)
Neural Networks, Computer , Supervised Machine Learning , Voice Disorders/classification , Adult , Age Factors , Aged , Alcohol Drinking/epidemiology , Algorithms , Demography , Female , Glottis/injuries , Glottis/physiopathology , Humans , Laryngeal Neoplasms/complications , Laryngeal Neoplasms/diagnosis , Laryngeal Neoplasms/physiopathology , Male , Middle Aged , Retrospective Studies , Severity of Illness Index , Sex Factors , Smoking/epidemiology , Symptom Assessment , Vocal Cord Paralysis/complications , Vocal Cord Paralysis/diagnosis , Vocal Cord Paralysis/physiopathology , Voice Disorders/epidemiology , Voice Quality , Wounds and Injuries/diagnosisABSTRACT
A robust modelling method was proposed to extract chromophore information in multi-layered skin tissue with spatially-resolved diffuse reflectance spectroscopy. Artificial neural network models trained with a pre-simulated database were first built to map geometric and optical parameters into diffuse reflectance spectra. Nine fitting parameters including chromophore concentrations and oxygen saturation were then determined by solving the inverse problem of fitting spectral measurements from three different parts of the skin. Compared to the Monte Carlo simulation accelerated by a graphics processing unit, the proposed modelling method not only reduced the computation time, but also achieved a better fitting performance.
