The prevalence of oral potentially malignant disorders (OPMDs) and oral cancer is surging in low- and middle-income countries. A lack of resources for population screening in remote locations delays the detection of these lesions in the early stages and contributes to higher mortality and a poor quality of life. Digital imaging and artificial intelligence (AI) are promising tools for cancer screening. This study aimed to evaluate the utility of AI-based techniques for detecting OPMDs in the Indian population using photographic images of oral cavities captured using a smartphone. A dataset comprising 1120 suspicious and 1058 non-suspicious oral cavity photographic images taken by trained front-line healthcare workers (FHWs) was used for evaluating the performance of different deep learning models based on convolution (DenseNets) and Transformer (Swin) architectures. The best-performing model was also tested on an additional independent test set comprising 440 photographic images taken by untrained FHWs (set I). DenseNet201 and Swin Transformer (base) models show high classification performance with an F1-score of 0.84 (CI 0.79–0.89) and 0.83 (CI 0.78–0.88) on the internal test set, respectively. However, the performance of models decreases on test set I, which has considerable variation in the image quality, with the best F1-score of 0.73 (CI 0.67–0.78) obtained using DenseNet201. The proposed AI model has the potential to identify suspicious and non-suspicious oral lesions using photographic images. This simplified image-based AI solution can assist in screening, early detection, and prompt referral for OPMDs.
在低收入和中等收入国家，口腔潜在恶性病变（OPMDs）和口腔癌的患病率正急剧上升。偏远地区缺乏人群筛查资源，导致这些病变在早期阶段难以及时发现，从而造成更高的死亡率及较差的生活质量。数字成像和人工智能（AI）是癌症筛查中极具前景的工具。本研究旨在评估基于人工智能的技术，利用智能手机拍摄的口腔照片图像，在印度人群中检测口腔潜在恶性病变的实用性。研究采用了一个数据集，包含由经过培训的一线医疗工作者拍摄的1120张可疑和1058张非可疑口腔照片图像，用于评估基于卷积（DenseNets）和Transformer（Swin）架构的不同深度学习模型的性能。表现最佳的模型还在一个额外的独立测试集上进行了测试，该测试集包含440张由未经培训的一线医疗工作者拍摄的照片图像（测试集I）。在内部测试集上，DenseNet201和Swin Transformer（基础）模型显示出较高的分类性能，F1分数分别为0.84（置信区间0.79–0.89）和0.83（置信区间0.78–0.88）。然而，在图像质量存在显著差异的测试集I上，模型的性能有所下降，其中使用DenseNet201获得的最佳F1分数为0.73（置信区间0.67–0.78）。所提出的人工智能模型有潜力利用照片图像识别可疑和非可疑的口腔病变。这种简化的基于图像的人工智能解决方案可协助口腔潜在恶性病变的筛查、早期检测及及时转诊。
肿瘤(癌症)患者之家