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Gender, Smoking History, and Age Prediction from Laryngeal Images
Zhang, Tianxiao Bur, Andrés M. Kraft, Shannon Kavookjian, Hannah Renslo, Bryan Chen, Xiangyu Luo, Bo Wang, Guanghui
Zhang, Tianxiao
Bur, Andrés M.
Kraft, Shannon
Kavookjian, Hannah
Renslo, Bryan
Chen, Xiangyu
Luo, Bo
Wang, Guanghui
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Abstract
Flexible laryngoscopy is commonly performed by otolaryngologists to detect laryngeal diseases and to recognize potentially malignant lesions. Recently, researchers have introduced machine learning techniques to facilitate automated diagnosis using laryngeal images and achieved promising results. The diagnostic performance can be improved when patients’ demographic information is incorporated into models. However, the manual entry of patient data is time-consuming for clinicians. In this study, we made the first endeavor to employ deep learning models to predict patient demographic information to improve the detector model’s performance. The overall accuracy for gender, smoking history, and age was 85.5%, 65.2%, and 75.9%, respectively. We also created a new laryngoscopic image set for the machine learning study and benchmarked the performance of eight classical deep learning models based on CNNs and Transformers. The results can be integrated into current learning models to improve their performance by incorporating the patient’s demographic information.
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Date
2023-05-29
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Publisher
MDPI
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Keywords
Laryngeal images, CAM, Gender, Smoking history, Demographic information
Citation
Zhang, T.; Bur, A.M.; Kraft, S.; Kavookjian, H.; Renslo, B.; Chen, X.; Luo, B.; Wang, G. Gender, Smoking History, and Age Prediction from Laryngeal Images. J. Imaging 2023, 9, 109. https://doi.org/10.3390/jimaging9060109