Development of an AI App to Improve Compliance in Periodontal Maintenance Patients

Periodontal Diseases Clinical Trial

Official title:

Development of an Artificial Intelligence Platform / Digital Dental Hygienist to Improve Compliance in Periodontal Maintenance Patients

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT05068778
• Other study ID #: Asua 001-2020
• Status: Active, not recruiting
• Start date: September 10, 2021
• Est. completion date: June 1, 2023

Study information

• Verified date: May 2022
• Source: McGuire Institute
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

An Artificial Intelligence platform to develop an app which improves compliance in periodontal treatments

Description:

To develop the accuracy of an AI-determined decision-making process for compliance score (CS) assignment, to assess clinical efficacy, and develop patient reported outcomes of AI-facilitated communication / education on a subject's oral health behaviors over successive maintenance visits.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 50
• Est. completion date: June 1, 2023
• Est. primary completion date: January 30, 2023
• Gender: All
• Age group: 18 Years to 75 Years

Eligibility

Inclusion Criteria:

1. Subjects 18 to 75-yrs of age

2. Subjects with diagnosis and treatment of Generalized and/or Localized Periodontitis Stages I, II, or III; grades A-C and or Peri-implantitis (Generalized Mild, Moderate, or Advanced Chronic Periodontitis) who are following a periodontal maintenance interval of every three months.

3. Subjects are able to provide self-care (oral hygiene) without disabilities requiring assistance with daily oral hygiene measures.

4. Subjects will have read, understood and signed an institutional review board (IRB) approved Informed Consent Form (ICF).

5. Subjects must be able and willing to follow study procedures and instructions.

Exclusion Criteria:

1. Subjects who do not own or are incapable of interacting with a smart phone or device

2. Subjects who are incapable of operating an application on a smart phone or device

3. Subjects who are not compliant with periodontal maintenance / recall intervals (Based on Wilson's classification as any lack of compliance with their recommended maintenance interval- therefore exclude subjects who demonstrate erratic compliance or noncompliance)18

4. Subjects who require active periodontal therapy during the study

5. Subjects who are on immunosuppressive medications, chemotherapy, or radiation therapy which would adversely affect their periodontal stability.

6. Subjects participating in other clinical studies involving therapeutic intervention (either medical or dental).

7. Subjects, who in the opinion of the investigator, for any reason other than those listed above, will not be able to complete the study per protocol. - Exclusion Criteria: -

Related Conditions & MeSH terms

• Periodontal Diseases

Intervention

Behavioral:
• Artificial Intelligence Application
AI application through smart phone

Locations

Country	Name	City	State
United States	Perio Health Professionals	Houston	Texas
United States	Metro West Orthodontics & Periodontics	La Vista	Nebraska

Sponsors (2)

Lead Sponsor	Collaborator
McGuire Institute	Asua, Inc

Country where clinical trial is conducted

United States, 

References & Publications (21)

Amerio E, Mainas G, Petrova D, Giner Tarrida L, Nart J, Monje A. Compliance with supportive periodontal/peri-implant therapy: A systematic review. J Clin Periodontol. 2020 Jan;47(1):81-100. doi: 10.1111/jcpe.13204. Epub 2019 Nov 13. — View Citation

Asimakopoulou K, Newton JT, Daly B, Kutzer Y, Ide M. The effects of providing periodontal disease risk information on psychological outcomes - a randomized controlled trial. J Clin Periodontol. 2015 Apr;42(4):350-5. doi: 10.1111/jcpe.12377. Epub 2015 Mar 24. — View Citation

Asimakopoulou K, Nolan M, McCarthy C, Newton JT. The effect of risk communication on periodontal treatment outcomes: A randomized controlled trial. J Periodontol. 2019 Sep;90(9):948-956. doi: 10.1002/JPER.18-0385. Epub 2019 Apr 17. — View Citation

Free C, Knight R, Robertson S, Whittaker R, Edwards P, Zhou W, Rodgers A, Cairns J, Kenward MG, Roberts I. Smoking cessation support delivered via mobile phone text messaging (txt2stop): a single-blind, randomised trial. Lancet. 2011 Jul 2;378(9785):49-55. doi: 10.1016/S0140-6736(11)60701-0. — View Citation

Jackson P. Introduction to Expert Systems. London: Addison Wesley; 1986

Khanna S. Artificial intelligence: contemporary applications and future compass. Int Dent J. 2010 Aug;60(4):269-72. — View Citation

Kim EY, Lim KO, Rhee HS. Predictive modeling of dental pain using neural network. Stud Health Technol Inform. 2009;146:745-6. — View Citation

McGuire MK, Nunn ME. Prognosis versus actual outcome. III. The effectiveness of clinical parameters in accurately predicting tooth survival. J Periodontol. 1996 Jul;67(7):666-74. — View Citation

Miller SC. Textbook of Periodontia: Philadelphia, Blakiston; 1938

Miyamoto T, Kumagai T, Jones JA, Van Dyke TE, Nunn ME. Compliance as a prognostic indicator: retrospective study of 505 patients treated and maintained for 15 years. J Periodontol. 2006 Feb;77(2):223-32. — View Citation

Needleman I, Nibali L, Di Iorio A. Professional mechanical plaque removal for prevention of periodontal diseases in adults--systematic review update. J Clin Periodontol. 2015 Apr;42 Suppl 16:S12-35. doi: 10.1111/jcpe.12341. Review. — View Citation

Newton JT, Asimakopoulou K. Managing oral hygiene as a risk factor for periodontal disease: a systematic review of psychological approaches to behaviour change for improved plaque control in periodontal management. J Clin Periodontol. 2015 Apr;42 Suppl 16:S36-46. doi: 10.1111/jcpe.12356. Review. — View Citation

O'Leary TJ, Drake RB, Naylor JE. The plaque control record. J Periodontol. 1972 Jan;43(1):38. — View Citation

Park WJ, Park JB. History and application of artificial neural networks in dentistry. Eur J Dent. 2018 Oct-Dec;12(4):594-601. doi: 10.4103/ejd.ejd_325_18. Review. — View Citation

Reddy MS, Shetty SR, Shetty RM, Vannala V, Sk S. Future of periodontics lies in artificial intelligence: Myth or reality? J Investig Clin Dent. 2019 Nov;10(4):e12423. doi: 10.1111/jicd.12423. Epub 2019 May 23. — View Citation

Shapiro SC. Encyclopedia of Artificial Intelligence. New York: Wiley; 1992.

Tonetti MS, Eickholz P, Loos BG, Papapanou P, van der Velden U, Armitage G, Bouchard P, Deinzer R, Dietrich T, Hughes F, Kocher T, Lang NP, Lopez R, Needleman I, Newton T, Nibali L, Pretzl B, Ramseier C, Sanz-Sanchez I, Schlagenhauf U, Suvan JE. Principles in prevention of periodontal diseases: Consensus report of group 1 of the 11th European Workshop on Periodontology on effective prevention of periodontal and peri-implant diseases. J Clin Periodontol. 2015 Apr;42 Suppl 16:S5-11. doi: 10.1111/jcpe.12368. — View Citation

Tonetti MS, Greenwell H, Kornman KS. Staging and grading of periodontitis: Framework and proposal of a new classification and case definition. J Periodontol. 2018 Jun;89 Suppl 1:S159-S172. doi: 10.1002/JPER.18-0006. Review. Erratum in: J Periodontol. 2018 Dec;89(12):1475. — View Citation

Wilson TG Jr, Glover ME, Malik AK, Schoen JA, Dorsett D. Tooth loss in maintenance patients in a private periodontal practice. J Periodontol. 1987 Apr;58(4):231-5. — View Citation

Wilson TG Jr, Glover ME, Schoen J, Baus C, Jacobs T. Compliance with maintenance therapy in a private periodontal practice. J Periodontol. 1984 Aug;55(8):468-73. — View Citation

Xie X, Wang L, Wang A. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. Angle Orthod. 2010 Mar;80(2):262-6. doi: 10.2319/111608-588.1. — View Citation

* Note: There are 21 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	PRO	PRO for subject experience using the AI app system	6 months
Other	Subject demographics	determination of demographics on subjects with periodontal disease	6 months
Other	AI generated surveys	survey to determine subjects progress, risk disease, oral hygiene needs, coping and goal setting abilities, and compliance	6 months
Other	Hygienist In office subject survey	to measure subjects hygiene and other life events (e.g. work stress)	6 months
Primary	To identify the input variables the AI utilizes to arrive at CS assignment and message selection for subject interaction via DDH app	Digital hygiene app will provide messages to subjects via their smartphone regarding their their lst maintenance appointment and throughout the study.	through study completion, approximately 7 months
Secondary	Compliance Score Accuracy	the increase the compliance score ant 3 and 6 months after subject begins use of the AI app	6 months
Secondary	change in recession	number of subjects with a lesser degree of recession from baseline to end of study due to increased subject compliance	6 months
Secondary	change in probing depth	number of subjects with a lesser probing depth from baseline to end of study due to increased subject compliance	6 months
Secondary	change in clinical attachment level	number of subjects with improved clinical attachment level due to increased subject compliance	6 months
Secondary	change in gingival inflammation	number of subjects with a reduction in gingival inflammation due to increased subject compliance	6 months
Secondary	decrease mobility	number of subjects showing a decreased degree of tooth mobility due to increased subject compliance	6 months