Abstract：
We aimed to construct an artificial intelligence-based model for detecting oral cancer and dysplastic leukoplakia using oral cavity images captured with a single-lens reflex camera.
We used 1043 images of lesions from 424 patients with oral squamous cell carcinoma (OSCC), leukoplakia, and other oral mucosal diseases. An object detection model was constructed using a Single Shot Multibox Detector to detect oral diseases and their locations using images. The model was trained using 523 images of oral cancer, and its performance was evaluated using images of oral cancer (n = 66), leukoplakia (n = 49), and other oral diseases (n = 405).
For the detection of only OSCC versus OSCC and leukoplakia, the model demonstrated a sensitivity of 93.9% versus 83.7%, a negative predictive value of 98.8% versus 94.5%, and a specificity of 81.2% versus 81.2%.
Our proposed model is a potential diagnostic tool for oral diseases.
We used 1043 images of lesions from 424 patients with oral squamous cell carcinoma (OSCC), leukoplakia, and other oral mucosal diseases. An object detection model was constructed using a Single Shot Multibox Detector to detect oral diseases and their locations using images. The model was trained using 523 images of oral cancer, and its performance was evaluated using images of oral cancer (n = 66), leukoplakia (n = 49), and other oral diseases (n = 405).
For the detection of only OSCC versus OSCC and leukoplakia, the model demonstrated a sensitivity of 93.9% versus 83.7%, a negative predictive value of 98.8% versus 94.5%, and a specificity of 81.2% versus 81.2%.
Our proposed model is a potential diagnostic tool for oral diseases.
摘要：
背景：我们旨在构建基于人工智能的模型，用于使用单镜头反光相机捕获的口腔图像来检测口腔癌和增生性白斑。
方法：我们使用了424例口腔鳞状细胞癌（OSCC）患者的1043张病变图像，白斑,和其他口腔粘膜疾病。使用单镜头多盒检测器构建对象检测模型，以使用图像检测口腔疾病及其位置。该模型使用523张口腔癌图像进行了训练，并使用口腔癌图像评估其性能(n=66)，白斑（n=49），和其他口腔疾病（n=405）。
结果：对于仅OSCC与OSCC和白斑的检测，该模型的灵敏度为93.9%对83.7%，阴性预测值为98.8%对94.5%，特异性为81.2%对81.2%。
结论：我们提出的模型是口腔疾病的潜在诊断工具。
方法：我们使用了424例口腔鳞状细胞癌（OSCC）患者的1043张病变图像，白斑,和其他口腔粘膜疾病。使用单镜头多盒检测器构建对象检测模型，以使用图像检测口腔疾病及其位置。该模型使用523张口腔癌图像进行了训练，并使用口腔癌图像评估其性能(n=66)，白斑（n=49），和其他口腔疾病（n=405）。
结果：对于仅OSCC与OSCC和白斑的检测，该模型的灵敏度为93.9%对83.7%，阴性预测值为98.8%对94.5%，特异性为81.2%对81.2%。
结论：我们提出的模型是口腔疾病的潜在诊断工具。
