Oral leukoplakia (OL) has an inherent disposition to develop oral cancer. OL with epithelial dysplasia (OED) is significantly likely to undergo malignant transformation; however, routine OED assessment is invasive and challenging. This study investigated whether a deep learning (DL) model can predict dysplasia probability among patients with leukoplakia using oral photographs. In addition, we assessed the performance of the DL model in comparison with clinicians’ ratings and in providing decision support on dysplasia assessment. Retrospective images of leukoplakia taken before biopsy/histopathology were obtained to construct the DL model ( n = 2,073). OED status following histopathology was used as the gold standard for all images. We first developed, fine-tuned, and internally validated a DL architecture with an EfficientNet-B2 backbone that outputs the predicted probability of OED, OED status, and regions-of-interest heat maps. Then, we tested the performance of the DL model on a temporal cohort before geographical validation. We also assessed the model’s performance at external validation with opinions provided by human raters on OED status. Performance evaluation included discrimination, calibration, and potential net benefit. The DL model achieved good Brier scores, areas under the curve, and balanced accuracies of 0.124 (0.079–0.169), 0.882 (0.838–0.926), and 81.8% (76.5–87.1) at testing and 0.146 (0.112–0.18), 0.828 (0.792–0.864), and 76.4% (72.3–80.5) at external validation, respectively. In addition, the model had a higher potential net benefit in selecting patients with OL for biopsy/histopathology during OED assessment than when biopsies were performed for all patients. External validation also showed that the DL model had better accuracy than 92.3% (24/26) of human raters in classifying the OED status of leukoplakia from oral images (balanced accuracy: 54.8%–79.7%). Overall, the photograph-based intelligent model can predict OED probability and status in leukoplakia with good calibration and discrimination, which shows potential for decision support to select patients for biopsy/histopathology, obviate unnecessary biopsy, and assist in patient self-monitoring.

中文翻译：

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预测口腔白斑上皮发育不良的深度学习系统


口腔白斑病 （OL） 具有发展为口腔癌的先天倾向。OL 伴上皮发育不良 （OED） 显著发生恶性转化;然而，常规 OED 评估是侵入性的且具有挑战性。本研究调查了深度学习 （DL） 模型是否可以使用口腔照片预测黏膜白细胞患者发育不良的可能性。此外，我们评估了 DL 模型与临床医生评级的比较以及为异型增生评估提供决策支持的性能。获得活检/组织病理学前拍摄的白斑回顾性图像以构建 DL 模型 （n = 2,073）。组织病理学后的 OED 状态被用作所有图像的金标准。我们首先开发、微调和内部验证了一个具有 EfficientNet-B2 主干的 DL 架构，该架构输出 OED 的预测概率、OED 状态和感兴趣区域的热图。然后，我们在地理验证之前测试了 DL 模型在时间队列上的性能。我们还根据人类评分者提供的关于 OED 状态的意见评估了模型在外部验证中的性能。性能评估包括鉴别、校准和潜在净收益。DL 模型在测试中取得了良好的 Brier 分数、曲线下面积和平衡精度，分别为 0.124 （0.079-0.169）、0.882 （0.838-0.926） 和 81.8% （76.5-87.1），外部验证时分别为 0.146 （0.112-0.18）、0.828 （0.792-0.864） 和 76.4% （72.3-80.5）。此外，与 OED 评估期间选择患有 OL 的患者进行活检/组织病理学相比，该模型在对所有患者进行活检时具有更高的潜在净收益。外部验证还表明 DL 模型的准确率优于 92。3% （24/26） 的人类评分者从口腔图像中对白斑的 OED 状态进行分类（平衡准确率：54.8%–79.7%）。总体而言，基于照片的智能模型可以预测黏膜白斑的 OED 概率和状态，具有良好的校准和鉴别能力，显示出为选择患者进行活检/组织病理学、避免不必要的活检和辅助患者自我监测的决策支持潜力。