基于数字病理学的人工智能模型用于散发性牙源性角化囊肿的鉴别诊断和预后判断

IF 10.8 1区 医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE International Journal of Oral Science Pub Date : 2024-02-26 DOI:10.1038/s41368-024-00287-y
Xinjia Cai, Heyu Zhang, Yanjin Wang, Jianyun Zhang, Tiejun Li
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引用次数: 0

摘要

牙源性角化囊肿(OKC)是一种常见的颌骨囊肿,复发率很高。OKC合并基底细胞癌以及骨骼和其他发育异常被认为与戈林综合征有关。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 个病例,共 2 157 张苏木精和伊红染色的图像,开发了基于数字病理学的人工智能(AI)模型,用于 OKC 的诊断和预后。利用 Inception_v3 神经网络来训练和测试根据斑块级图像开发的模型。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,开发出了整张切片图像级人工智能模型。这些人工智能模型在 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)方面表现出色。通过与单切片模型的比较,证明了多切片模型在整合组织病理学信息方面的优势。此外,该研究还探讨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,凸显了人工智能模型在病理学领域的解释潜力。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有望应用于颌骨牙源性疾病。
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Digital pathology-based artificial intelligence models for differential diagnosis and prognosis of sporadic odontogenic keratocysts

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.

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来源期刊
International Journal of Oral Science
International Journal of Oral Science DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
31.80
自引率
1.30%
发文量
53
审稿时长
>12 weeks
期刊介绍: The International Journal of Oral Science covers various aspects of oral science and interdisciplinary fields, encompassing basic, applied, and clinical research. Topics include, but are not limited to: Oral microbiology Oral and maxillofacial oncology Cariology Oral inflammation and infection Dental stem cells and regenerative medicine Craniofacial surgery Dental material Oral biomechanics Oral, dental, and maxillofacial genetic and developmental diseases Craniofacial bone research Craniofacial-related biomaterials Temporomandibular joint disorder and osteoarthritis The journal publishes peer-reviewed Articles presenting new research results and Review Articles offering concise summaries of specific areas in oral science.
期刊最新文献
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