Artificial intelligence for the recognition of benign lesions of vocal folds from audio recordings

Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale(2023)

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摘要
Objective. The diagnosis of benign lesions of the vocal fold (BLVF) is still challenging. The analysis of the acoustic signals through the implementation of machine learning models can be a viable solution aimed at offering support for clinical diagnosis. Materials and methods. In this study, a support vector machine was trained and cross-validated (10-fold cross-validation) using 138 features extracted from the acoustic signals of 418 patients with polyps, nodules, oedema, and cysts. The model's performance was presented as accuracy and average F1-score. The results were also analysed in male (M) and female (F) subgroups. Results. The validation accuracy was 55%, 80%, and 54% on the overall cohort, and in M and F, respectively. Better performances were observed in the detection of cysts and nodules (58% and 62%, respectively) vs polyps and oedema (47% and 53%, respectively). The results on each lesion and the different patterns of the model on M and F are in line with clinical observations, obtaining better results on F and more accurate detection of polyps in M. Conclusions. This study showed moderately accurate detection of four types of BLVF us-ing acoustic signals. The analysis of the diagnostic results on gender subgroups highlights different behaviours of the diagnostic model.
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关键词
artificial intelligence, machine learning, benign lesions of vocal folds, dysphonia
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