AI在口腔遗传病与罕见病机制研究及临床诊断中的应用进展

doi:10.3969/j.issn.1674-8115.2026.07.001

上海交通大学学报（医学版） ›› 2026, Vol. 46 ›› Issue (7): 829-838.doi: 10.3969/j.issn.1674-8115.2026.07.001

• 前沿述评 •

AI在口腔遗传病与罕见病机制研究及临床诊断中的应用进展

沈靖婷, 柳稚旭, 王旭东()

上海交通大学医学院附属第九人民医院口腔颅颌面科，上海交通大学口腔医学院，国家口腔医学中心，口腔疾病国家临床医学研究中心，上海市口腔医学重点实验室，上海市口腔医学研究所，上海 200011

收稿日期:2026-02-28 接受日期:2026-04-29 出版日期:2026-07-28 发布日期:2026-07-28
通讯作者: 王旭东，主任医师，教授，博士；电子信箱：xudongwang70@hotmail.com。
基金资助:
国家自然科学基金(82370905);国家资助博士后研究人员计划(GZC20251220);上海交通大学医学院“双百人”项目(20152225)

Applications and progress of AI in mechanistic research and clinical diagnosis of oral genetic and rare diseases

Shen Jingting, Liu Zhixu, Wang Xudong()

Department of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People′s Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology; Shanghai Research Institute of Stomatology, Shanghai 200011, China

Received:2026-02-28 Accepted:2026-04-29 Online:2026-07-28 Published:2026-07-28
Contact: Wang Xudong, E-mail: xudongwang70@hotmail.com.
Supported by:
National Natural Science Foundation of China(82370905);Postdoctoral Fellowship Program of CPSF(GZC20251220);“Two-hundred Talents” Program of Shanghai Jiao Tong University School of Medicine(20152225)

摘要/Abstract

摘要：

口腔遗传病与罕见病具有发病率低、表型异质性强且致病机制复杂的特点，病情严重，诊断困难且治疗手段有限。随着多组学测序以及医学影像技术的快速发展，海量的分子数据和临床数据为该类疾病的研究提供了新的视角。人工智能（artificial intelligence，AI）技术凭借其强大的模式识别和复杂关系建模能力，已在口腔遗传病与罕见病基础研究和临床诊疗领域取得显著成果。在致病机制研究方面，AI可解读基因组、转录组、蛋白组及微生物组等多组学数据，识别新的分子标志物并构建疾病预测模型，推动复杂病因的解析。在临床诊断方面，AI显著提升了口腔影像的自动化分析能力，智能诊断龋齿、牙发育异常、唾液腺疾病、骨纤维异常增殖症及多种颅颌面畸形疾病，也为手术治疗规划提供决策支持。尽管AI在口腔遗传病领域取得显著进展，但仍面临数据稀缺、模型可解释性不足及伦理规范限制等挑战。该文综述AI在口腔遗传病与罕见病领域应用的最新进展，系统梳理其在深化致病机制研究和加速临床诊断方面的进展、挑战与前景，旨在推动口腔遗传病精准医学的整体进步及优质医疗资源下沉。

关键词: 人工智能, 口腔遗传病与罕见病, 致病机制, 临床诊断, 精准医学

Abstract:

Oral genetic and rare diseases are characterized by low prevalence, marked phenotypic heterogeneity, and complex pathogenic mechanisms. They often present with severe clinical manifestations, pose significant diagnostic challenges, and lack effective therapeutic options. With the rapid advancement of multi‑omics sequencing and medical imaging technologies, large‑scale molecular and clinical data have provided new perspectives for understanding these disorders. Artificial intelligence (AI), with its powerful capabilities in pattern recognition and modeling complex relationships, has achieved significant progress in both fundamental research and clinical diagnosis and treatment related to oral genetic and rare diseases. In studies of pathogenic mechanisms, AI enables the explanation of genomic, transcriptomic, proteomic, and microbiome data, facilitates the identification of novel molecular biomarkers, and supports the construction of disease prediction models, thereby advancing the elucidation of complex etiologies. In clinical diagnosis, AI significantly enhances the automation and accuracy of oral imaging analysis, enabling intelligent diagnosis of dental caries, tooth developmental anomalies, salivary gland diseases, fibrous dysplasia, and various craniofacial malformations. AI‑based tools also provide decision support for surgical treatment planning. Despite these advances, challenges remain, including data scarcity, limited model interpretability, and ethical and regulatory concerns. This paper aims to provide a comprehensive review of the latest advancements in the application of AI in the field of oral genetic and rare diseases, systematically examining the progress, challenges, and prospects in deepening the understanding of disease mechanisms and accelerating clinical diagnosis, with the goal of ultimately promoting the overall advancement of precision medicine for oral genetic diseases and improving the accessibility of high-quality healthcare resources.

Key words: artificial intelligence (AI), oral genetic and rare disease, pathogenic mechanism, clinical diagnosis, precision medicine

中图分类号:

R782

沈靖婷, 柳稚旭, 王旭东. AI在口腔遗传病与罕见病机制研究及临床诊断中的应用进展[J]. 上海交通大学学报（医学版）, 2026, 46(7): 829-838.

Shen Jingting, Liu Zhixu, Wang Xudong. Applications and progress of AI in mechanistic research and clinical diagnosis of oral genetic and rare diseases[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2026, 46(7): 829-838.

图/表 3

参考文献 53

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Reference	Year	Dataset	Objective	AI model
Zhang, et al^[11]	2018	43 GWAS-significant SNPs associated with OFCs	Genetic risk assessment of OFCs	SVM, LR, NB, RF, KNN, DT, and DNN
Dai, et al^[15]	2024	Epigenetic sequencing data from PCW4-10	Pathogenic variant prioritization for OFCs	CNN
Forrest, et al^[16]	2025	Training cohort of 1.3 million individuals with 10 genetic diseases	Genetic variant penetrance estimation	XGBoost
Kamitaki, et al^[17]	2026	Genomic and oral microbiome data from 12 519 individuals	Genetic variant-oral microbiome association analysis	PCA
Guo, et al^[18]	2025	scRNA-seq data of mouse molars from embryonic stages to postnatal days	Cell subtype identification	PCA, UMAP, etc.
Huang, et al^[19]	2025	scRNA-seq data of mouse palate during embryonic development	Cell subtype identification	PCA, UMAP, etc.
Yankee, et al^[20]	2023	scRNA-seq data of human embryonic craniofacial tissues	Cell subtype identification	PCA, UMAP, etc.
Liu, et al^[21]	2025	Paired bulk transcriptomic and single-cell transcriptomic data	Single-cell proteome prediction	Transformer

Reference	Year	Dataset	Objective	AI model
Zhang, et al^[11]	2018	43 GWAS-significant SNPs associated with OFCs	Genetic risk assessment of OFCs	SVM, LR, NB, RF, KNN, DT, and DNN
Dai, et al^[15]	2024	Epigenetic sequencing data from PCW4-10	Pathogenic variant prioritization for OFCs	CNN
Forrest, et al^[16]	2025	Training cohort of 1.3 million individuals with 10 genetic diseases	Genetic variant penetrance estimation	XGBoost
Kamitaki, et al^[17]	2026	Genomic and oral microbiome data from 12 519 individuals	Genetic variant-oral microbiome association analysis	PCA
Guo, et al^[18]	2025	scRNA-seq data of mouse molars from embryonic stages to postnatal days	Cell subtype identification	PCA, UMAP, etc.
Huang, et al^[19]	2025	scRNA-seq data of mouse palate during embryonic development	Cell subtype identification	PCA, UMAP, etc.
Yankee, et al^[20]	2023	scRNA-seq data of human embryonic craniofacial tissues	Cell subtype identification	PCA, UMAP, etc.
Liu, et al^[21]	2025	Paired bulk transcriptomic and single-cell transcriptomic data	Single-cell proteome prediction	Transformer

Reference	Year	Dataset	Objective	AI model
Lee, et al^[23]	2018	3 000 periapical radiographs	Dental caries detection	CNN
Hiraiwa, et al^[24]	2019	CBCT images and panoramic radiographs of mandibular first molars from 400 patients	Root canal morphology classification	CNN
Ekert, et al^[25]	2019	2 001 panoramic radiographs	Periapical lesion diagnosis	DNN
Duman, et al^[26]	2023	220 panoramic radiographs of children aged 6‒9 years	Hyperdontia diagnosis	CNN
Incerti Parenti, et al^[27]	2025	A retrospective dataset of 250 panoramic radiographs from patients aged 6‒13 years	Primary and permanent tooth detection and segmentation	CNN
Sheng, et al^[28]	2023	100 panoramic radiographs	Tooth segmentation	Transformer
Han, et al^[30]	2023	203 salivary gland pathology slides	Segmentation of lymphocytic infiltration areas	DNN
Chiesa-Estomba, et al^[31]	2021	Multi-source clinical data from 345 patients undergoing salivary gland tumor surgery	Prediction of postoperative facial nerve palsy	KNN, RF, BC, and LDC
Jurek, et al^[32]	2020	Fetal ultrasound images	Fetal cleft palate diagnosis	NLP
Shafi, et al^[33]	2020	1 000 prenatal survey records	Risk prediction of cleft lip and palate	DNN
Kuwada, et al^[34]	2021	Panoramic radiographs of 383 patients with cleft conditions (with or without alveolar cleft) and 210 healthy controls	Cleft palate classification	DNN
Zhang, et al^[35]	2020	CBCT images of 21 patients with unilateral or bilateral CLP	Alveolar bone defect volume estimation	CNN
Pactas, et al^[36]	2019	Pre- and post-treatment photographs of 146 orthognathic patients	Assessment of orthognathic surgical outcome	CNN
Huang, et al^[38]	2025	Pre- and post-treatment CBCT images of 100 orthognathic surgery patients	Prediction of postoperative facial appearance	CNN
Adel, et al^[39]	2025	3D facial images of 130 patients	Facial symmetry assessment	DNN
Chen, et al^[40]	2025	CT images of 15 patients	Soft tissue defect assessment	CNN
Saranya, et al^[41]	2022	Bone CT images	FD lesion segmentation	R-CNN
Li, et al^[42]	2025	CT images of 148 FD patients	Prediction of FD lesion growth status	CNN
Zhang, et al^[43]	2024	CT images of 220 patients with FD or OF	Differential diagnosis between FD and OF	RF, SVM, LightGBM, and XGBoost

Reference	Year	Dataset	Objective	AI model
Lee, et al^[23]	2018	3 000 periapical radiographs	Dental caries detection	CNN
Hiraiwa, et al^[24]	2019	CBCT images and panoramic radiographs of mandibular first molars from 400 patients	Root canal morphology classification	CNN
Ekert, et al^[25]	2019	2 001 panoramic radiographs	Periapical lesion diagnosis	DNN
Duman, et al^[26]	2023	220 panoramic radiographs of children aged 6‒9 years	Hyperdontia diagnosis	CNN
Incerti Parenti, et al^[27]	2025	A retrospective dataset of 250 panoramic radiographs from patients aged 6‒13 years	Primary and permanent tooth detection and segmentation	CNN
Sheng, et al^[28]	2023	100 panoramic radiographs	Tooth segmentation	Transformer
Han, et al^[30]	2023	203 salivary gland pathology slides	Segmentation of lymphocytic infiltration areas	DNN
Chiesa-Estomba, et al^[31]	2021	Multi-source clinical data from 345 patients undergoing salivary gland tumor surgery	Prediction of postoperative facial nerve palsy	KNN, RF, BC, and LDC
Jurek, et al^[32]	2020	Fetal ultrasound images	Fetal cleft palate diagnosis	NLP
Shafi, et al^[33]	2020	1 000 prenatal survey records	Risk prediction of cleft lip and palate	DNN
Kuwada, et al^[34]	2021	Panoramic radiographs of 383 patients with cleft conditions (with or without alveolar cleft) and 210 healthy controls	Cleft palate classification	DNN
Zhang, et al^[35]	2020	CBCT images of 21 patients with unilateral or bilateral CLP	Alveolar bone defect volume estimation	CNN
Pactas, et al^[36]	2019	Pre- and post-treatment photographs of 146 orthognathic patients	Assessment of orthognathic surgical outcome	CNN
Huang, et al^[38]	2025	Pre- and post-treatment CBCT images of 100 orthognathic surgery patients	Prediction of postoperative facial appearance	CNN
Adel, et al^[39]	2025	3D facial images of 130 patients	Facial symmetry assessment	DNN
Chen, et al^[40]	2025	CT images of 15 patients	Soft tissue defect assessment	CNN
Saranya, et al^[41]	2022	Bone CT images	FD lesion segmentation	R-CNN
Li, et al^[42]	2025	CT images of 148 FD patients	Prediction of FD lesion growth status	CNN
Zhang, et al^[43]	2024	CT images of 220 patients with FD or OF	Differential diagnosis between FD and OF	RF, SVM, LightGBM, and XGBoost

AI在口腔遗传病与罕见病机制研究及临床诊断中的应用进展

Applications and progress of AI in mechanistic research and clinical diagnosis of oral genetic and rare diseases

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