Gender, Smoking History, and Age Prediction from Laryngeal Images.
Gender, Smoking History, and Age Prediction from Laryngeal Images.
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DOI：
10.3390/jimaging9060109
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发表时间：
2023-05-29
影响因子：
3.2
通讯作者：
中科院分区：
文献类型：
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作者：
软喉镜通常由耳鼻喉科医生进行，以检测喉部疾病并识别潜在的恶性病变。最近，研究人员引入了机器学习技术，以促进使用喉部图像进行自动诊断，并取得了可喜的成果。当患者的人口统计信息被纳入模型中时，诊断性能可以得到改善。然而，患者数据的手动输入对于临床医生来说是耗时的。在这项研究中，我们首次奋进使用深度学习模型来预测患者的人口统计信息，以提高检测器模型的性能。性别、吸烟史和年龄的总体准确率分别为85.5%、65.2%和75.9%。我们还为机器学习研究创建了一个新的喉镜图像集，并对基于CNN和Transformers的八种经典深度学习模型的性能进行了基准测试。这些结果可以整合到当前的学习模型中，通过整合患者的人口统计信息来提高其性能。
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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影响因子：
1.9
作者：
Ebisumoto K;Sakai A;Maki D;Robinson K;Murakami T;Iijima H;Yamauchi M;Saito K;Watanabe T;Okami K
通讯作者：
Okami K
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通讯作者：
Wang, Guanghui
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通讯作者：
Peretti, Giorgio