Accuracy of an AI-clinical Knowledge-based Hybrid System for Detecting Periodontitis in OPG Images
NCT ID: NCT06306677
Last Updated: 2024-03-13
Study Results
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Basic Information
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RECRUITING
1200 participants
OBSERVATIONAL
2024-03-12
2024-12-31
Brief Summary
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Detailed Description
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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.
Conditions
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Study Design
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COHORT
CROSS_SECTIONAL
Study Groups
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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.
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.
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
2. Having taken the OPG image
Exclusion Criteria
18 Years
ALL
Yes
Sponsors
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ShanghaiTech University
OTHER
University of Roma La Sapienza
OTHER
The University of Hong Kong
OTHER
Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University
OTHER
Responsible Party
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Maurizio Tonetti
Professor
Principal Investigators
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Maurizio Tonetti
Role: PRINCIPAL_INVESTIGATOR
Shanghai Ninth People Hospital
Locations
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Shanghai Perio-Implant Innovation Center
Shanghai, Shanghai Municipality, China
Prince Philip Dental Hospital
Hong Kong, , Hong Kong
La Sapienza Dental School
Roma, , Italy
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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SH9H-2023-T369-1
Identifier Type: -
Identifier Source: org_study_id
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