Application progress of artificial intelligence in diagnosis and treatment of oral genetic diseases

doi:10.19438/j.cjoms.2025.04.015

Abstract

Abstract: Rare condition, scattered cases, complex etiology and clinical features, inadequate diagnostic criteria and methods and other reasons lead to the serious lack of accessibility for early diagnosis and treatment of oral genetic diseases. Artificial intelligence(AI) is expected to make breakthroughs in the prevention, diagnosis, treatment and basic research of oral genetic diseases due to its unique advantages in feature extraction, variant recognition, classification, typing and outcome prediction of large and complex data sets. Many studies have identified and processed patients' medical records, photos, radiographic images, key genes or transcriptome, biomarkers and other data through AI models or software to assist the diagnosis, treatment and research of various oral genetic diseases. Based on the literature evidence, this article reviewed and summarized the application of AI in teeth, periodontal tissues, oral mucosa, orofacial clefts related genetic diseases and other oral genetic diseases that cause craniofacial malformations. The future development of this field was also prospected.

Key words: Artificial intelligence, Oral genetic diseases, Deep learning, Machine learning, Syndrome

CLC Number:

Gu Anqi, Zhang Chen, Tao Baoxin, Wu Yiqun, Zhou Wenjie. Application progress of artificial intelligence in diagnosis and treatment of oral genetic diseases[J]. China Journal of Oral and Maxillofacial Surgery, 2025, 23(4): 406-415.

References

[1] 段小红. 口腔遗传病学[M]. 北京:人民卫生出版社,2012.
Duan XH.Genetic diseases of oral cavity[M]. Beijing: People's Medical Publishing House, 2012.
[2] 段小红. 口腔罕见病名录(第一版)[J]. 中华口腔医学杂志, 2020, 55(7): 494-500.
Duan XH.The first edition of oral rare diseases list[J]. Chinese Journal of Stomatology, 2020, 55(7): 494-500.
[3] 关淑元, 周媛, 周学东, 等. 孕前口腔保健及遗传咨询[J]. 国际口腔医学杂志, 2018, 45(3): 324-330.
Guan SY, Zhou Y, Zhou XD, et al.Preconception oral health care and genetic counseling[J]. International Journal of Stomatology, 2018, 45(3): 324-330.
[4] Dargan NS, Kumar M, Ayyagari MR, et al.A survey of deep learning and its applications: a new paradigm to machine learning[J]. Arch Comput Methods Eng, 2020, 27(4): 1071-1092.
[5] Khanagar SB, Al-Ehaideb A, Maganur PC, et al.Developments, application, and performance of artificial intelligence in dentistry - a systematic review[J]. J Dent Sci, 2021, 16(1): 508-522.
[6] 刘洪臣. 人工智能口腔医学[J]. 中华口腔医学杂志, 2020, 55(12): 915-919.
Liu HC.Artificial intelligence stomatology[J]. Chinese Journal of Stomatology, 2020, 55(12): 915-919.
[7] Ertas K, Pence I, Cesmeli MS, et al.Determination of the stage and grade of periodontitis according to the current classification of periodontal and peri-implant diseases and conditions (2018) using machine learning algorithms[J]. J Periodontal Implant Sci, 2023, 53(1): 38-53.
[8] Standardization of Uveitis Nomenclature (SUN) Working Group. Classification criteria for Behçet disease uveitis[J]. Am J Ophthalmol, 2021, 228: 80-88.
[9] Shafi N, Bukhari F, Iqbal W, et al.Cleft prediction before birth using deep neural network[J]. Health Informatics J, 2020, 26(4): 2568-2585.
[10] Schönewolf J, Meyer O, Engels P, et al.Artificial intelligence-based diagnostics of molar-incisor-hypomineralization (MIH) on intraoral photographs[J]. Clin Oral Investig, 2022, 26(9): 5923-5930.
[11] Alevizakos V, Bekes K, Steffen R, et al.Artificial intelligence system for training diagnosis and differentiation with molar incisor hypomineralization (MIH) and similar pathologies[J]. Clin Oral Investig, 2022, 26(12): 6917-6923.
[12] Ferrer-Sánchez A, Bagan J, Vila-Francés J, et al.Prediction of the risk of cancer and the grade of dysplasia in leukoplakia lesions using deep learning[J]. Oral Oncol, 2022, 132: 105967.
[13] Li Y, Cheng J, Mei H, et al.CLPNet: cleft lip and palate surgery support with deep learning[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2019: 3666-3672.
[14] Vorravanpreecha N, Lertboonnum T, Rodjanadit R, et al.Studying Down syndrome recognition probabilities in Thai children with de-identified computer-aided facial analysis[J]. Am J Med Genet A, 2018, 176(9): 1935-1940.
[15] Srisraluang W, Rojnueangnit K.Facial recognition accuracy in photographs of Thai neonates with Down syndrome among physicians and the Face2Gene application[J]. Am J Med Genet A, 2021, 185(12): 3701-3705.
[16] Hennocq Q, Bongibault T, Bizière M, et al.An automatic facial landmarking for children with rare diseases[J]. Am J Med Genet A, 2023, 191(5): 1210-1221.
[17] Pan Z, Shen Z, Zhu H, et al.Clinical application of an automatic facial recognition system based on deep learning for diagnosis of Turner syndrome[J]. Endocrine, 2021, 72(3): 865-873.
[18] Özdemir ME, Telatar Z, Erogul O, et al.Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree[J]. Australas Phys Eng Sci Med, 2018, 41(2): 451-461.
[19] Porras AR, Bramble MS, Mosema Be Amoti K, et al. Facial analysis technology for the detection of Down syndrome in the Democratic Republic of the Congo[J]. Eur J Med Genet, 2021, 64(9): 104267.
[20] Guo L, Park J, Yi E, et al.KBG syndrome: videoconferencing and use of artificial intelligence driven facial phenotyping in 25 new patients[J]. Eur J Hum Genet, 2022, 30(11): 1244-1254.
[21] Qin B, Liang L, Wu J, et al.Automatic identification of Down syndrome using facial images with deep convolutional neural network[J]. Diagnostics (Basel), 2020, 10(7): 487.
[22] Duman S, Yilmaz EF, Eser G, et al.Detecting the presence of taurodont teeth on panoramic radiographs using a deep learning-based convolutional neural network algorithm[J]. Oral Radiol, 2023, 39(1): 207-214.
[23] Mine Y, Iwamoto Y, Okazaki S, et al.Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: a pilot study[J]. Int J Paediatr Dent, 2022, 32(5): 678-685.
[24] Lee CT, Kabir T, Nelson J, et al.Use of the deep learning approach to measure alveolar bone level[J]. J Clin Periodontol, 2022, 49(3): 260-269.
[25] Lee JH, Kim DH, Jeong SN, et al.Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm[J]. J Periodontal Implant Sci, 2018, 48(2): 114-123.
[26] Thanathornwong B, Suebnukarn S.Automatic detection of periodontal compromised teeth in digital panoramic radiographs using faster regional convolutional neural networks[J]. Imaging Sci Dent, 2020, 50(2): 169-174.
[27] Kuwada C, Ariji Y, Kise Y, et al.Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system[J]. Sci Rep, 2021, 11(1): 16044.
[28] Lin G, Kim PJ, Baek SH, et al.Early prediction of the need for orthognathic surgery in patients with repaired unilateral cleft lip and palate using machine learning and longitudinal lateral cephalometric analysis data[J]. J Craniofac Surg, 2021, 32(2): 616-620.
[29] Lim J, Tanikawa C, Kogo M, et al.Determination of prognostic factors for orthognathic surgery in children with cleft lip and/or palate[J]. Orthod Craniofac Res, 2021, 24(Suppl 2): 153-162.
[30] Alam MK, Alfawzan AA.Evaluation of sella turcica bridging and morphology in different types of cleft patients[J]. Front Cell Dev Biol, 2020, 8: 656.
[31] Alam MK, Alfawzan AA.Dental characteristics of different types of cleft and non-cleft individuals[J]. Front Cell Dev Biol, 2020, 8: 789.
[32] Alam MK, Alfawzan AA, Haque S, et al.Sagittal Jaw relationship of different types of cleft and non-cleft individuals[J]. Front Pediatr, 2021, 9: 651951.
[33] Brookes SJ.Using imageJ (Fiji) to analyze and present X-ray CT images of enamel[J]. Methods Mol Biol, 2019, 1922: 267-291.
[34] Wang X, Pastewait M, Wu TH, et al.3D morphometric quantification of maxillae and defects for patients with unilateral cleft palate via deep learning-based CBCT image auto-segmentation[J]. Orthod Craniofac Res, 2021, 24(Suppl 2): 108-116.
[35] Zhang X, Qin N, Zhou Z, et al.Machine learning in 3D auto-filling alveolar cleft of CT images to assess the influence of alveolar bone grafting on the development of maxilla[J]. BMC Oral Health, 2023, 23(1): 16-25.
[36] Seo J, Yang IH, Choi JY, et al.Three-dimensional facial soft tissue changes after orthognathic surgery in cleft patients using artificial intelligence-assisted landmark autodigitization[J]. J Craniofac Surg, 2021, 32(8): 2695-2700.
[37] O' Sullivan E, van de Lande LS, Papaioannou A, et al. Convolutional mesh autoencoders for the 3-dimensional identification of FGFR-related craniosynostosis[J]. Sci Rep, 2022, 12(1): 2230.
[38] Wang X, Liu Z, Du Y, et al.Recognition of fetal facial ultrasound standard plane based on texture feature fusion[J]. Comput Math Methods Med, 2021: 6656942.
[39] Jurek J, Wójtowicz W, Wójtowicz A.Syntactic pattern recognition-based diagnostics of fetal palates[J]. Pattern Recognit Lett, 2020, 133: 144-150.
[40] Catic A, Gurbeta L, Kurtovic-Kozaric A, et al.Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics[J]. BMC Med Genomics, 2018, 11(1): 19-30.
[41] Kim JM, Kang JG, Kim S, et al.Deep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis[J]. J Gastroenterol Hepatol, 2021, 36(8): 2141-2148.
[42] Grill FD, Ritschl LM, Bauer FX, et al.A semi-automated virtual workflow solution for the design and production of intraoral molding plates using additive manufacturing: the first clinical results of a pilot-study[J]. Sci Rep, 2018, 8(1): 11845.
[43] Schiebl J, Bauer FX, Grill F, et al.RapidNAM: algorithm for the semi-automated generation of nasoalveolar molding device designs for the presurgical treatment of bilateral cleft lip and palate[J]. IEEE Trans Biomed Eng, 2020, 67(5): 1263-1271.
[44] Bauer FX, Schönberger M, Gattinger J, et al.RapidNAM: generative manufacturing approach of nasoalveolar molding devices for presurgical cleft lip and palate treatment[J]. Biomed Tech (Berl), 2017, 62(4): 407-414.
[45] Bauer FX, Gau D, Guell F, et al.Automated detection of alveolar arches for nasoalveolar molding in cleft lip and palate treatment[J]. Curr Dir Biomed Eng, 2016, 2(1): 701-705.
[46] Wang X, Tang M, Yang S, et al.Automatic hypernasality detection in cleft palate speech using CNN[J]. Circuits Syst Signal Process, 2019, 38(8): 3521-3547.
[47] Wang X, Yang S, Tang M, et al.HypernasalityNet: deep recurrent neural network for automatic hypernasality detection[J]. Int J Med Inform, 2019, 129: 1-12.
[48] Dubey AK, Prasanna SRM, Dandapat S.Sinusoidal model-based hypernasality detection in cleft palate speech using CVCV sequence[J]. Speech Commun, 2020, 124: 1-12.
[49] Xiao F, Zhou Z, Song X, et al.Dissecting mutational allosteric effects in alkaline phosphatases associated with different hypophosphatasia phenotypes: an integrative computational investigation[J]. PLoS Comput Biol, 2022, 18(3): e1010009.
[50] Li S, Liu X, Li H, et al.Integrated analysis of long noncoding RNA-associated competing endogenous RNA network in periodontitis[J]. J Periodontal Res, 2018, 53(4): 495-505.
[51] Zhang Q, Jiao Y, Ma N, et al.Identification of endoplasmic reticulum stress-related biomarkers of periodontitis based on machine learning: a bioinformatics analysis[J]. Dis Markers, 2022: 8611755.
[52] Ji J, Li X, Zhu Y, et al.Screening of periodontitis-related diagnostic biomarkers based on weighted gene correlation network analysis and machine algorithms[J]. Technol Health Care, 2022, 30(5): 1209-1221.
[53] Ning W, Acharya A, Sun Z, et al.Deep learning reveals key immunosuppression genes and distinct immunotypes in periodontitis[J]. Front Genet, 2021, 12: 648329.
[54] Xiang J, Huang W, He Y, et al.Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis[J]. Front Genet, 2022, 13: 1041524.
[55] Peng L, Chen H, Wang Z, et al.Identification and validation of a classifier based on hub aging-related genes and aging subtypes correlation with immune microenvironment for periodontitis[J]. Front Immunol, 2022, 13: 1042484.
[56] Işık YE, Görmez Y, Aydın Z,et al. The determination of distinctive single nucleotide polymorphism sets for the diagnosis of Behçet's disease[J]. IEEE/ACM Trans Comput Biol Bioinform, 2022, 19(3): 1909-1918.
[57] Enzo E, Secone Seconetti A, Forcato M, et al.Single-keratinocyte transcriptomic analyses identify different clonal types and proliferative potential mediated by FOXM1 in human epidermal stem cells[J]. Nat Commun, 2021, 12(1): 2505.
[58] Zhang H, Lee CAA, Li Z, et al.A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa[J]. PLoS Comput Biol, 2018, 14(4): e1006053.
[59] Machado RA, De Oliveira Silva C, Martelli-Junior H, et al. Machine learning in prediction of genetic risk of nonsyndromic oral clefts in the Brazilian population[J]. Clin Oral Investig, 2021, 25(3): 1273-1280.
[60] Zhang SJ, Meng P, Zhang J, et al.Machine learning models for genetic risk assessment of infants with non-syndromic orofacial cleft[J]. Genomics Proteomics Bioinformatics, 2018, 16(5): 354-364.
[61] Garcia-Carretero R, Olid-Velilla M, Perez-Torrella D, et al.Predictive modeling of hypophosphatasia based on a case series of adult patients with persistent hypophosphatasemia[J]. Osteoporos Int, 2021, 32(9): 1815-1824.
[62] Huang W, Wu J, Mao Y, et al.Developing a periodontal disease antibody array for the prediction of severe periodontal disease using machine learning classifiers[J]. J Periodontol, 2020, 91(2): 232-243.
[63] Feres M, Louzoun Y, Haber S, et al.Support vector machine-based differentiation between aggressive and chronic periodontitis using microbial profiles[J]. Int Dent J, 2018, 68(1): 39-46.
[64] Liu W, Qiu W, Huang Z, et al.Identification of nine signature proteins involved in periodontitis by integrated analysis of TMT proteomics and transcriptomics[J]. Front Immunol, 2022, 13: 963123.
[65] Na HS, Kim SY, Han H, et al.Identification of potential oral microbial biomarkers for the diagnosis of periodontitis[J]. J Clin Med, 2020, 9(5): 1549.
[66] Senusi AA, Liu J, Bevec D, et al.Why are Behçet's disease patients always exhausted?[J]. Clin Exp Rheumatol, 2018, 36(6 Suppl 115): 53-62.
[67] Hammam N, Bakhiet A, El-Latif EA, et al.Development of machine learning models for detection of vision threatening Behçet's disease (BD) using Egyptian College of Rheumatology (ECR)-BD cohort[J]. BMC Med Inform Decis Mak, 2023, 23(1): 37-50.
[68] Liu H, Zhang P, Li F, et al.Identification of the immune-related biomarkers in Behcet's disease by plasma proteomic analysis[J]. Arthritis Res Ther, 2023, 25(1): 92-105.
[69] He F, Lin B, Mou K, et al.A machine learning model for the prediction of down syndrome in second trimester antenatal screening[J]. Clin Chim Acta, 2021, 521: 206-211.
[70] Schneider L, Arsiwala-Scheppach L, Krois J, et al.Benchmarking deep learning models for tooth structure segmentation[J]. J Dent Res, 2022, 101(11): 1343-1349.
[71] Leite AF, Gerven AV, Willems H, et al.Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs[J]. Clin Oral Invest, 2021, 25(4): 2257-2267.
[72] 周娜, 陈晖. 牙周炎的遗传病因研究进展[J]. 口腔生物医学, 2012, 3(2): 105-108.
Zhou N, Chen H.Research progress in genetic etiology of periodontitis[J]. Oral Biomedicine, 2012, 3(2): 105-108.
[73] 束蓉, 倪靖. 2018牙周病和植体周病国际新分类—牙周炎分期分级疾病定义系统临床应用体会[J]. 口腔医学, 2020, 40(1): 1-6.
Shu R, Ni J.2018 International classification of periodontal diseases and implant diseases: clinical application of staging and grading of periodontitis[J]. Stomatology, 2020, 40(1): 1-6.
[74] Dias R, Torkamani A.Artificial intelligence in clinical and genomic diagnostics[J]. Genome Med, 2019, 11(1): 70-82.
[75] Shi C, Meijer JM, Azzopardi G, et al.Use of convolutional neural networks for the detection of u-serrated patterns in direct immunofluorescence images to facilitate the diagnosis of epidermolysis bullosa acquisita[J]. Am J Pathol, 2021, 191(9): 1520-1525.
[76] Li P, Kong D, Tang T, et al.Orthodontic treatment planning based on artificial neural networks[J]. Sci Rep, 2019, 9(1): 2037.
[77] Chaker SC, Hung YC, Saad M, et al.Easing the burden on caregivers- applications of artificial intelligence for physicians and caregivers of children with cleft lip and palate[J]. Cleft Palate Craniofac J, 2024, 5:10556656231223596.
[78] Gashi F, Regli SF, May R, et al.Developing intelligent interviewers to collect the medical history: lessons learned and guidelines[J]. Stud Health Technol Inform, 2021, 279: 18-25.
[79] 刘清海, 刘廷廷, 朱凌, 等. 机器学习在口腔医疗诊断中的应用进展[J]. 中国口腔颌面外科杂志, 2024, 22(6): 605-610.
Liu QH, Liu TT, Zhu L, et al.Application progress of machine learning in oral medical diagnosis[J]. China Journal of Oral and Maxillofacial Surgery, 2024, 22(6): 605-610.