ArticleDigital health
Acoustic signatures of organic lesions and the role of artificial intelligence in voice disorder diagnostics.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- A deep learning approach for acoustic-based identification of muscle tension dysphonia and spasmodic dysphonia.Frontiers in digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Voice disorders resulting from organic vocal cord lesions, whether benign or malignant, often lack reliable non-invasive diagnostic tools, which can lead to delays in treatment. This study aims to identify distinctive acoustic biomarkers and develop machine learning models for accurate classification of these lesions and prediction of malignancy. We investigated the acoustic characteristics of voice production in patients with organic vocal cord lesions, comparing benign and malignant cases, and evaluated the diagnostic potential of machine learning models in distinguishing between healthy and pathological voices. Methods: A total of 157 participants were enrolled, including 127 patients with organic vocal cord lesions (109 benign, 18 malignant) and 30 healthy controls. Acoustic analysis was performed on vowel sounds, assessing vocal fold vibration parameters. Machine learning models (eXtreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM)) were trained to classify lesion types and predict malignancy. Receiver operating characteristic analysis identified key diagnostic parameters. Results: Comparative analysis revealed 63 statistically significant differences in acoustic parameters between healthy and lesion-affected groups, with skewness and kurtosis being particularly discriminative. Six key parameters (/u/skew, /i/skew, /o/kurt, /o/shapefactor, /o/impulsefactor, and /a/peak2valley) demonstrated high diagnostic value in distinguishing benign from malignant lesions. The XGBoost model achieved the best performance in classifying vocal cord lesions (area under the curve (AUC) = 0.735), while LightGBM excelled in malignancy prediction (AUC = 0.924). Age and specific acoustic parameters were significant predictors in the models. Conclusion: The integration of acoustic analysis with machine learning significantly enhances the diagnostic accuracy for vocal cord lesions, particularly in differentiating between benign and malignant cases. These findings underscore the potential of artificial intelligence (AI)-assisted voice analysis as a non-invasive tool for early detection and clinical decision-making. Further validation in larger cohorts is necessary to refine predictive algorithms for broader clinical application.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.