AI Gum Health Evaluation with Smartphone

NCT05685355 | Recruiting

• 1 other identifier: UW21-447
• Study Type: observational
• Target: 1,200
• Locations: 1 country
• Sites: 1

Brief Summary

Background The most common dental diseases are tooth decay (caries) and gum disease (gingivitis and periodontitis). Obviously, these diseases are caused by dental plaque (bacterial biofilm). Although most patients brush their teeth every day, they cannot keep all their teeth clean. Areas in the mouth that are difficult to access, such as crowded areas, posterior teeth or interdental areas, are usually affected (site-specific). After a thorough professional tooth cleaning, dental plaque will begin to accumulate on the tooth surface near the gum edge within a few days. Clinical studies indicating that regular disruption to the plaque is needed and can prevent and arrest gum disease. However, dental diseases may take years to develop, the patient usually does not have any pain symptoms unless the disease has progressed to the advanced stage. A significant amount of resources and clinical time have been used to motivate and instruct patients to keep their mouth clean and yet the results are not satisfactory. It is desirable to adopt an automated technique for monitoring oral health daily so participants can seek treatment when it is needed. Patients' response to plaque accumulated at the gum margin is by inflammation which brings more blood cells to the site to fight against the bacterial invasion. Inflammation of gum is manifested as an increase in redness (color), an increase in volume (oedema), and loss of surface characteristics (stippling; gum fibre attachment). These affected areas can be identified by visual inspection with the dentist during the consultation or using intraoral photography. The objective of this research is to apply deep neural network technology to detect gum inflammation from intraoral photos. As the target inflammation site is at gum margin with varied shape and size, semantic segmentation at pixel level is needed. In this research, the investigators are planning to have an extensive study of deep neural network (DNN) approach for the automatic multiple level gum disease detection. Standardized intraoral photography will be collected for 1200 cases and will be labelled by several dentists as "diseased" (inflammation), "healthy" or "questionable". Only gum area in which the dentists have same rating will be used to train/validate the system. Using the successfully developed system, one can use his/her mobile device to monitor their gum health when needed. They may be able to prevent the two main oral diseases (tooth decay and gum diseases) with minimal additional cost. It will be an important contribution to the promotion of public dental care. Aim of study This study aims to train and validate the computer to automatically monitor gum inflammation using standardized intraoral photos and selfie by smartphone.

1. 1.to collect 1200 standard intraoral photographs and randomly cropped into training and validation sets.
2. 2.to develop ground truth gingivitis label images into four health status levels (healthy, questionable healthy, questionable diseased and diseased) and verified by dental specialists.
3. 3.to develop intelligent system for automatically detect inflamed disease sites with four health status levels.
4. 4.to develop and standardize the image acquisition protocol for the detection with mobile devices.

Conditions

Gingivitis

Keywords

Gingivitis detection; Semantic segmentation; inflammation; photography; mobile device; deep neural network; machine learning; artificial intelligence

Outcome Measures

Primary Outcomes (1)

• Database of standard intraoral photographs with ground truth gingivitis label

  1. A database of 1200 standard intraoral photographs with ground truth gingivitis label images into four health status levels (healthy, questionable healthy, questionable diseased and diseased) which are developed and verified by dental specialists. 2. An intelligent system for automatically detect inflamed disease sites with four health status levels.

  1/1/2023-31/12/2025

Secondary Outcomes (1)

• Patient reported outcome on the use of smartphone selfies

  1/1/2023-31/12/2025

Study Arms (1)

Gingivitis samples

Inclusion criteria: 1. Adult subjects attending PPDH can give informed consent. 2. Subjects who are diagnosed to have gingivitis only and have 24 or more teeth. 3. Subjects who are otherwise medically healthy. 4. Subjects who can attend multiple dental visits. Exclusion criteria 1. Subjects who are in acute dental infection or in pain. 2. Subjects who have oral mucosal diseases that preclude retraction of soft tissues for photos. 3. Subjects who are in the fixed appliance for orthodontic treatment. 4. Subjects who are pregnant, or medically unfit for periodontal charting or require antibiotic coverage (e.g. risk of infective endocarditis)

Diagnostic Test: Standardized intraoral photography

Interventions

Standardized intraoral photography DIAGNOSTIC_TEST

Mouth photos will be taken with a plastic retractor to retract the subject's cheek and lips. This is a standard clinical procedure that is non-invasive and does not cause any harm or adverse effect on subjects.

Gingivitis samples

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The target study population is the adult subjects attending The Prince Philip Dental Hospital who are able to give informed consent.

You may qualify if:

• Adult subjects attending The Prince Philip Dental Hospital (PPDH) whoare able to give informed consent.
• Subjects who are diagnosed to have gingivitis only and have 24 or more teeth.
• Subjects who are otherwise medically healthy.
• Subjects who can attend multiple dental visits.

You may not qualify if:

• Subjects who are in acute dental infection or in pain.
• Subjects who have oral mucosal diseases that preclude retraction of soft tissues for photos.
• Subjects who are in a fixed appliance for orthodontic treatment.
• Subjects who are pregnant, or medically unfit for periodontal charting or require antibiotic coverage (e.g. risk of infective endocarditis)

Sponsors & Collaborators

• The University of Hong Kong: lead
• Hong Kong Chu Hai College: collaborator

Study Sites (1)

Faculty of Dentistry, The University of Hong Kong

Hong Kong, Hong Kong

RECRUITING

MeSH Terms

Conditions

Gingivitis; Inflammation

Condition Hierarchy (Ancestors)

Infections; Gingival Diseases; Periodontal Diseases; Mouth Diseases; Stomatognathic Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Tai Chiu Hsung, PhD

CONTACT

(852)28590270; tchsung@hku.hk

Yu Hang Lam, MDS

CONTACT

(852)28590306; retlaw@hku.hk

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Honorary Associate Professor

Study Record Dates

• First Submitted: January 5, 2023
• First Posted: January 17, 2023
• Study Start: January 1, 2022
• Primary Completion: December 31, 2025
• Study Completion: December 31, 2025
• Last Updated: December 6, 2024

Record last verified: 2024-05

Locations

HK (1)