Odontogenic keratocyst (OKC) is a common jaw cyst with a high recurrence rate. OKC combined with basal cell carcinoma as well as skeletal and other developmental abnormalities is thought to be associated with Gorlin syndrome. Moreover, OKC needs to be differentiated from orthokeratinized odontogenic cyst and other jaw cysts. Because of the different prognosis, differential diagnosis of several cysts can contribute to clinical management. We collected 519 cases, comprising a total of 2 157 hematoxylin and eosin-stained images, to develop digital pathology-based artificial intelligence (AI) models for the diagnosis and prognosis of OKC. The Inception_v3 neural network was utilized to train and test models developed from patch-level images. Finally, whole slide image-level AI models were developed by integrating deep learning-generated pathology features with several machine learning algorithms. The AI models showed great performance in the diagnosis (AUC = 0.935, 95% CI: 0.898–0.973) and prognosis (AUC = 0.840, 95%CI: 0.751–0.930) of OKC. The advantages of multiple slides model for integrating of histopathological information are demonstrated through a comparison with the single slide model. Furthermore, the study investigates the correlation between AI features generated by deep learning and pathological findings, highlighting the interpretative potential of AI models in the pathology. Here, we have developed the robust diagnostic and prognostic models for OKC. The AI model that is based on digital pathology shows promise potential for applications in odontogenic diseases of the jaw.

牙源性角化囊肿（OKC）是一种常见的颌骨囊肿，复发率很高。OKC 与基底细胞癌以及骨骼和其他发育异常相结合被认为与 Gorlin 综合征有关。此外，OKC 需要与正角化牙源性囊肿和其他颌骨囊肿相鉴别。由于预后不同，对几种囊肿进行鉴别诊断有助于临床治疗。我们收集了 519 例病例，总共 2 157 张苏木精和伊红染色图像，开发基于数字病理学的人工智能 (AI) 模型，用于 OKC 的诊断和预后。Inception_v3 神经网络用于训练和测试从补丁级图像开发的模型。最后，通过将深度学习生成的病理特征与多种机器学习算法相结合，开发了全幻灯片图像级人工智能模型。AI模型在OKC的诊断（AUC = 0.935，95% CI：0.898–0.973）和预后（AUC = 0.840，95% CI：0.751–0.930）方面表现出良好的性能。通过与单玻片模型的比较，证明了多玻片模型在整合组织病理学信息方面的优势。此外，该研究还调查了深度学习生成的人工智能特征与病理结果之间的相关性，强调了人工智能模型在病理学中的解释潜力。在这里，我们为 OKC 开发了稳健的诊断和预后模型。基于数字病理学的人工智能模型显示出在颌骨牙源性疾病中的应用潜力。