Main Article Content

Syed Akbar Abbas Zaidi
Ashique Hussain Sahito
Shahrukh Irfan
Dinesh Kumar
Naina Devi
Shahzad Baloch
Ravi Shanker Essarani
Vijai Nand


Periodontal disease, Artifical Intelligence, Accuracy, Specificity, Sensitivity, Mental Disorders


Machine learning (ML) is a crucial component of artificial intelligence (AI), and it's a common misconception that these terms, including deep learning, are interchangeable. The field of medical and dental diagnostics stands to gain significant advantages from this technological advancement. Therefore, a comprehensive understanding of AI and its fundamental components, such as ML, artificial neural networks (ANN), and deep learning (DP), is essential.The objective of this study was to assess the effectiveness of artificial intelligence in both preventing and diagnosing periodontal disease in individuals with mental disorders.This prospective research took place at Muhammad Dental College from February 2023 to July 2023 and involved a total of 527 patients of diverse genders who were diagnosed with mental disorders and presented with complaints related to periodontal disease. Detailed demographic information, including educational and socioeconomic status, was collected for all participants. The study evaluated the sensitivity and specificity of a novel AI system for identifying periodontal disease using intraoral images, adhering to the STARD-2015 statement guidelines for reporting accuracy. Statistical analysis was performed using SPSS 22.0.Among the participants, there were 305 males (57.9%) and 223 females (42.1%), with an average age of 47.5±16.52 years. Of the participants, 295 (55.97%) were smokers. The majority of patients, 325 (61.7%), resided in urban areas, while 202 (38.3%) had rural residences. The most prevalent mental disorders were schizophrenia (50%), depression (29%), and anxiety (13%). Patients with more severe mental disorders, as indicated by the K6 scale index, exhibited a higher prevalence of periodontal disease. Intraoral images accurately detected periodontal disease (gingivitis) with an accuracy ranging between 88%, while AI models for detecting alveolar bone loss achieved an accuracy of 95%. The AI diagnosis had an accuracy rate of 0.90 for identifying healthy pixels and 0.92 for detecting disease pixels.This study underscored that individuals with psychiatric disorders tend to have poorer periodontal health compared to the general population. In addition to providing psychiatric care and therapy, it is advisable to incorporate a targeted preventive dental program. Artificial intelligence demonstrates the potential to identify specific areas with gingival inflammation or periodontal disease, as well as sites without these conditions, with a sensitivity and specificity comparable to those of a visual examination conducted by a human dentist.

Abstract 99 | pdf Downloads 37


1.Cecoro G, Annunziata M, Iuorio MT, Nastri L, Guida L. Periodontitis, low-grade inflammation and systemic health: a scoping review. Medicina. 2020;56(6):272.
2. Nazir M, Al-Ansari A, Al-Khalifa K, Alhareky M, Gaffar B, Almas K. Global prevalence of periodontal disease and lack of its surveillance. Sci World J. 2020;2020.
3. Joury E, Kisely S, Watt R, Ahmed N, Morris A, Fortune F, et al. Mental disorders and oral diseases: future research directions. J Dental Res. 2023;102(1):5–12.
4. Choi J, Price J, Ryder S, Siskind D, Solmi M, Kisely S. Prevalence of dental disorders among people with mental illness: an umbrella review. Aust N Z J Psychiatry. 2022;56(8):949–63. An umbrella review of systematic reviews of the association between psychiatric disorders and dental pathology including periodontal disease.
5. Chang H-J, Lee S-J, Yong T-H, Shin N-Y, Jang B-G, Kim J-E, et al. Deep learning hybrid method to automatically diagnose periodontal bone loss and stage periodontitis. Sci Rep. 2020;10(1):1–8.
6. Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to Machine Learning, Neural Networks, and Deep Learning. Transl Vis Sci Technol. •••;9(2):14
7. Haenlein M, Kaplan A. A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. Calif Manage Rev. 2019. July 17;61:000812561986492. 10.1177/0008125619864925
8. Kaplan A, Haenlein M. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus Horiz. 2019. January 1;62(1):15–25. 10.1016/j.bushor.2018.08.004
9. Putra RH, Doi C, Yoda N, Astuti ER, Sasaki K. Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofac Radiol. 2022. January 1;51(1):20210197. 10.1259/dmfr.20210197
10. Bui FQ, Almeida-da-Silva CLC, Huynh B, et al. Association between periodontal pathogens and systemic disease. Biomed J. 2019;42(1):27-35.
11. Cuevas-González MV, Suaste-Olmos F, García-Calderón AG, et al. Expression of microRNAs in periodontal disease: a systematic review. Biomed Res Int. 2021;2021:2069410. doi: 10.1155/2021/2069410.
12. Isola G, Santonocito S, Distefano A, et al. Impact of periodontitis on gingival crevicular fluid miRNAs profiles associated with cardiovascular disease risk. J Periodontal Res. 2023;58(1):165-174.
13. Zardawi F, Gul S, Abdulkareem A, et al. Association between periodontal disease and atherosclerotic cardiovascular diseases: revisited. Front Cardiovasc Med. 2021;7:625579.
14. Arigbede AO, Babatope BO, Bamidele MK. Periodontitis and systemic diseases: a literature review. J Indian Soc Periodontol. 2012;16(4):487-491.
15. Munz M, Richter GM, Loos BG, et al. Meta-analysis of genome-wide association studies of aggressive and chronic periodontitis identifies two novel risk loci. Eur J Hum Genet. 2019;27(1):102-113.
16. D'Aiuto F, Gkranias N, Bhowruth D, et al; TASTE Group. Systemic effects of periodontitis treatment in patients with type 2 diabetes: a 12 month, single-centre, investigator-masked, randomised trial. Lancet Diabetes Endocrinol. 2018;6(12):954-965.
17. L Young Ho.Overview of the process of conducting meta-analyses of the diagnostic test accuracy
J Rheum Dis, 25 (1) (2018), pp. 3-10
18. JD Bader, DA Shugars, AJ Bonito.Systematic reviews of selected dental caries diagnostic and management methods.J Dent Educ, 65 (10) (2001), pp. 960-968
19. R Usamentiaga, R Usamentiaga, DG Lema, OD Pedrayes, G Daniel.Automated surface defect detection in metals: a comparative review of object detection and semantic segmentation using deep learning.IEEE Trans Ind Appl, 58 (3) (2022), pp. 4203-4213
20. Patil S, Albogami S, Hosmani J, et al. Artificial intelligence in the diagnosis of oral diseases: applications and pitfalls. Diagnostics (Basel). 2022;12(5):1029.
21. Shan T, Tay FR, Gu L. Application of artificial intelligence in dentistry. J Dent Res. 2021;100(3):232-244
22. Kierce E, Balaban C. The AI revolution: transforming dental hygiene care. Inside Dental Hygiene. 2021;17(5)spec iss 2:16-18.
23. Lee JH, Kim DH, Jeong SN, Choi SH. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci. 2018;48(2):114-123.
24. Feres M, Louzoun Y, Haber S, et al. Support vector machine-based differentiation between aggressive and chronic periodontitis using microbial profiles. Int Dent J. 2018;68(1):39-46.
25. Danks RP, Bano S, Orishko A, et al. Automating periodontal bone loss measurement via dental landmark localisation. Int J Comput Assist Radiol Surg. 2021;16(7):1189-1199.
26. Lee J-H, Kim D-H, Jeong S-N, Choi S-H. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci. 2018;48(2):114–23.
27. Lee CT, Kabir T, Nelson J, Sheng S, Meng HW, Van Dyke TE, et al. Use of the deep learning approach to measure alveolar bone level. J Clin Periodontol. 2021.
28. Chen Y-W, Stanley K, Att W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int. 2020;51(3):248–57.
29. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10.
30. Jung S-K, Kim T-W. New approach for the diagnosis of extractions with neural network machine learning. Am J Orthod Dentofac Orthop. 2016;149(1):127–33.
31. Pepelassi EA, Tsiklakis K, Diamanti-Kipioti A. Radiographic detection and assessment of the periodontal endosseous defects. J Clin Periodontol. 2000;27(4):224–30.
32. Mol A. Imaging methods in periodontology. Periodontology. 2004;34(1):34–48.
33. Lindhe J, Ranney R, Lamster I, Charles A, Chung CP, Flemmig T, et al. Consensus report: chronic periodontitis. Ann Periodontol. 1999;4(1):38.

Most read articles by the same author(s)