Accuracy of an AI-clinical Knowledge-based Hybrid System for Detecting Periodontitis in OPG Images

What is the Diagnostic Accuracy of a AI-clinical Knowledge Based Hybrid System for the Diagnosis of Stage II-IV Periodontitis Using OPG Images?A Multi-center Study

Periodontitis is highly prevalent and rarely detected and treated in the earlier stages of the disease. Orthopantomography (OPG) is the most frequently taken dental radiograph around the world, and its systematic screening may contribute to early detection of periodontitis and access to the needed level of care. The investigators' recent study initially developed an AI-clinical knowledge-based system for automatic periodontitis diagnosis and indicated good performance for differentiating stage II-IV periodontitis. This cross-sectional diagnostic study aims to compare the diagnostic accuracy of this AI-clinical knowledge-based hybrid system (Index test) with human experts (reference test) for differentiating stage II-IV periodontitis using the OPG images obtained from different 4 centers around the world.

Study Overview

• Status: Recruiting
• Conditions: Periodontitis; Periodontal Diseases
• Intervention / Treatment: Diagnostic test: AI-clincial-based hybrid system for radiographic image analysis

Detailed Description

Periodontitis is a major public health problem due to its high prevalence worldwide, substantial socio-economic impacts, and considerable effects on individuals' quality of life. However, periodontitis in the population remains largely undetected. It is crucial to raise awareness about periodontal health and enhance early diagnosis of periodontitis to ensure timely intervention.

The 2018 Classification of Periodontal and Peri-implant Diseases and Conditions defines four stages of periodontitis ranging from the initial stage (stage I) to the advanced stage (stage IV). In stages II-IV, comprehensive treatment procedures are essential otherwise there is a high risk of tooth or even the entire dentition loss. Although clinical examinations are regarded as the gold standard for determining the stage of periodontitis, the process is laborious and time-consuming, demanding highly experienced specialists. Therefore, alternative cost-effective but reliable and valid approaches for differentiating stage II-IV periodontitis diagnosis, particularly in public communities are highly needed.

Orthopantomography (OPG), also known as panoramic radiography, is a non-invasive and low-dose imaging technique that provides a comprehensive view of the maxillofacial region in one procedure. As an extraoral radiograph, it has advantages in capturing the image, especially in cases where patients struggle to open their mouths or exhibit a pronounced gag reflex that hinders the use of intraoral films. Thus, OPG is likely the most frequently taken dental radiograph around the world and may potentially serve as an effective tool for differentiating stage II-IV periodontitis in populations. Recently, several investigations have been carried out to utilize OPG images for periodontitis diagnosis. However, the strategies of these studies rely on the radiographic annotations for specific landmarks by clinicians which may lack compelling accuracy. Furthermore, only the radiographs with high quality could be a valuable adjunct for the periodontitis diagnosis, so many available OPG images with the superimposition of anatomical structures, disproportionate image magnification, distortion, and blur may decrease the generalization of the developed system.

Artificial Intelligence (AI) has emerged as a powerful tool in various fields of medicine, including dentistry. AI-based algorithms, particularly deep learning techniques, have shown remarkable capabilities in image analysis, pattern recognition, and decision-making. In recent years, the integration of AI technology in dentistry has opened new avenues for enhancing the accuracy and efficiency of diagnosis. AI-based algorithms may be able to recognize some features in OPG images that are imperceptible to the human eye, allowing for the detection of subtle bone loss and achieving a more accurate diagnosis of periodontal staging.

Notably, findings from our recent study revealed that a hybrid system combining AI algorithms and clinical knowledge has good performance for differentiating stage II-IV periodontitis. In the development process of this hybrid system, only clinical information provided by experienced specialists was utilized and no radiographic annotations were employed. Despite the promising potential of the hybrid system developed from our initial investigation, it is essential to further train and validate it in different independent populations because a prediction rule derived from one sample could perform better in another sample/population. Besides, it is reasonable to assume that the OPG images taken from different machines may greatly influence the accuracy of the developed hybrid system. Therefore, it is logical to conduct a multi-center study to collect different OPG images from various centers worldwide and the dataset will be utilized to train further and validate the hybrid system ensuring its accuracy and efficacy in periodontitis diagnosis.

In this study, we will compare the diagnostic characteristics of a novel AI-clinical-based hybrid system (Index test) with a panel of experts (reference standard). Experts will independently assess all radiographs and reach agreement if any discrepancy among them is found.

• Study Type: Observational
• Enrollment (Estimated): 1200

Contacts and Locations

• Study Contact: Name: Maurizio Tonetti; Phone Number: 15000102368; Email: maurizio.tonetti@ergoperio.eu

Study Locations

• China
  • Shanghai
    • Shanghai, Shanghai, China, 201206
      • Recruiting
      • Shanghai Perio-Implant Innovation Center
      • Contact: Yuan Li; Phone Number: 13916337473; Email: ly9919@hotmail.com
• Hong Kong
  • Hong Kong, Hong Kong
    • Recruiting
    • Prince Philip Dental Hospital
    • Contact: George Pelekos; Email: george74@hku.hk
• Italy
  • Roma, Italy
    • Recruiting
    • La Sapienza Dental School
    • Contact: Lorenzo Marini; Email: lorenzo.marini@uniroma1.it

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

1. Patients seeking care at Shanghai Ninth People's Hospital Pudong Clinics;
2. Patients seeking care at Shanghai Ninth People's Hospital South Clinics;
3. Patients seeking care at Prince Philip Dental Hospital in Hong Kong;
4. Patients seeking care at The University of Rome La Sapienza in Italy;

Description

Inclusion Criteria:

1. Aged 18 and above
2. Having taken the OPG image

Exclusion Criteria:

1. Edentulous mouth

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Subjects presenting for care at Hospitals; The group is formed by subjects reporting for care at one of the four participating hospitals (China, Hong Kong SAR, Italy) who required an OPG radiographs for their routine clinical care.	Diagnostic test: AI-clincial-based hybrid system for radiographic image analysis; The Index test is a novel AI-clinical-based hybrid system for radiographic image analysis. Its diagnostic performance will be compared to the reference represented by a panel of experts.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sensitivity measure	Sensitivity of the AI-clinical based hybrid system to correctly identify periodontitis cases labeled by panel of experts	1 day
Specificity measure	Specificity of the AI-clinical based hybrid system to correctly identify periodontitis cases labeled by panel of experts	1 day
The area under the receiver operating characteristic curve (AUC) measure	The area under the receiver operating characteristic curve (AUC) measure of AI-clinical based hybrid system to correctly identify periodontitis cases labeled by panel of experts	1 day
Diagnostic accuracy	The overall diagnostic accuracy of the AI-clinical-based system will be calculated based on the fraction of true test results [Accuracy = (true positives + true negatives) / (total)] and compared with the panel of experts	1 day

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Assessment time	Comparison of the time for diagnosis required by the AI-clinical-based hybrid system and the panel of experts for each OPG image	1 day

Collaborators and Investigators

• Sponsor: Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University
• Collaborators: The University of Hong Kong; University of Roma La Sapienza; ShanghaiTech University
• Investigators: Principal Investigator: Maurizio Tonetti, Shanghai Ninth People Hospital

Study record dates

Study Major Dates

• Study Start (Actual): March 12, 2024
• Primary Completion (Estimated): August 1, 2024
• Study Completion (Estimated): December 31, 2024

Study Registration Dates

• First Submitted: March 4, 2024
• First Submitted That Met QC Criteria: March 11, 2024
• First Posted (Actual): March 12, 2024

Study Record Updates

• Last Update Posted (Actual): March 13, 2024
• Last Update Submitted That Met QC Criteria: March 12, 2024
• Last Verified: March 1, 2024

More Information

Terms related to this study

• Keywords: Periodontitis; Artificial Intelligence; Radiograph
• Additional Relevant MeSH Terms: Stomatognathic Diseases; Mouth Diseases; Periodontitis; Periodontal Diseases
• Other Study ID Numbers: SH9H-2023-T369-1

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No