Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review

• URL: http://arxiv.org/abs/2307.09230v2
• Date: Wed, 24 Jul 2024 16:15:44 GMT
• Title: Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review
• Authors: Mary Paterson, James Moor, Luisa Cutillo,
• Abstract summary: Artificial intelligence (AI) and machine learning (ML) have the potential to detect throat cancer from patient speech. Cases of throat cancer are rising worldwide. No comprehensive review has explored the use of AI and ML for detecting throat cancer from speech.
• Score: 0.30723404270319693
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Introduction: Cases of throat cancer are rising worldwide. With survival decreasing significantly at later stages, early detection is vital. Artificial intelligence (AI) and machine learning (ML) have the potential to detect throat cancer from patient speech, facilitating earlier diagnosis and reducing the burden on overstretched healthcare systems. However, no comprehensive review has explored the use of AI and ML for detecting throat cancer from speech. This review aims to fill this gap by evaluating how these technologies perform and identifying issues that need to be addressed in future research. Materials and Methods: We conducted a scoping literature review across three databases: Scopus,Web of Science, and PubMed. We included articles that classified speech using machine learning and specified the inclusion of throat cancer patients in their data. Articles were categorized based on whether they performed binary or multi-class classification. Results: We found 27 articles fitting our inclusion criteria, 12 performing binary classification, 13 performing multi-class classification, and two that do both binary and multiclass classification. The most common classification method used was neural networks, and the most frequently extracted feature was mel-spectrograms. We also documented pre-processing methods and classifier performance. We compared each article against the TRIPOD-AI checklist, which showed a significant lack of open science, with only one article sharing code and only three using open-access data. Conclusion: Open-source code is essential for external validation and further development in this field. Our review indicates that no single method or specific feature consistently outperforms others in detecting throat cancer from speech. Future research should focus on standardizing methodologies and improving the reproducibility of results.

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