Peri-implantitisNCT07907900
Diagnostic Accuracy of Large Language Models for Peri-Implant Marginal Bone Loss on Panoramic Radiographs
An observational study of Peri-implantitis and Alveolar Bone Loss, run by Ankara University at 1 site in Turkey (Türkiye).
Trial summary
Dental implants are a routine treatment for missing teeth, but the number of implants affected by peri-implantitis, a progressive inflammatory disease of the tissues around implants, is increasing. The main radiographic sign of peri-implantitis is marginal bone loss around the implant. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Peri-implantitis, Alveolar Bone Loss, Dental Implant
- What is being tested?
- Multimodal large language model evaluation (ChatGPT, Gemini, Claude, DeepSeek, Grok)
- How many people
- 784 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 27 Oct 2026to 31 Jan 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Anonymized panoramic radiographs retrieved retrospectively from the PACS archive of Ankara University Faculty of Dentistry, taken for diagnostic purposes in adult patients (18 years or older) of either sex who have at least one functionally loaded dental implant. The unit of analysis is the implant site (standardized cropped region of interest); one radiograph may contribute more than one implant site. Consecutive eligible radiographs will be screened until at least 392 implant sites with and 392 implant sites without marginal bone loss are obtained.
Who may be able to join
- Panoramic radiographs of adult patients (18 years or older) obtained during routine clinical care at Ankara University Faculty of Dentistry and stored in the hospital PACS archive
- At least one dental implant visible on the radiograph (with or without marginal bone loss)
- Bone-level implant in function, i.e., prosthetic loading completed
- Diagnostic image quality (contrast and sharpness) sufficient to clearly evaluate implant borders, threads, implant platform and the adjacent crestal bone
Who cannot join
- Records of patients younger than 18 years
- High distortion due to patient movement, positioning error or device-related causes, or radiographic artifacts over the implant body or marginal bone that prevent evaluation
- Implants not yet prosthetically loaded (submerged or healing-abutment stage)
- Dense radiopaque bone graft material around the implant masking the natural marginal bone level
- Cystic lesions or tumoral formations around the implant that could be confused with, or completely distort, ordinary peri-implant marginal bone loss
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Turkey (Türkiye)1 site
Ankara University Faculty of Dentistry
Ankara, 06560
Ilkim Karadag, PhD, +903122965690, karadag@ankara.edu.tr
About this study
Official title: Diagnostic Accuracy of Large Language Models in the Evaluation of Peri-Implant Marginal Bone Loss on Panoramic Radiographs: A Multi-Observer Retrospective Study
Dental implants are a routine treatment for missing teeth, but the number of implants affected by peri-implantitis, a progressive inflammatory disease of the tissues around implants, is increasing. The main radiographic sign of peri-implantitis is marginal bone loss around the implant. Early detection and correct grading of this bone loss in proportion to implant length are important for treatment planning and prognosis.
Panoramic radiographs are widely used for general screening in dental practice, but judging bone loss on them is subjective, time-consuming and depends on the clinician's experience. Multimodal large language models (vision-language models) are increasingly able to interpret images, yet their ability to evaluate peri-implant bone loss on panoramic radiographs has not been tested.
Read more
This single-center, retrospective, observational diagnostic accuracy study will evaluate how accurately five publicly available large language models (ChatGPT, Gemini, Claude, DeepSeek and Grok) detect and grade peri-implant marginal bone loss on anonymized, cropped panoramic radiographs of functionally loaded dental implants, compared with a reference standard established by three blinded clinicians (two periodontologists and one oral and maxillofacial radiologist). Bone loss will be graded with the Froum and Rosen (2012) classification: Score 0 (no visible bone loss), Score 1 (<25% of implant length), Score 2 (25-50%) and Score 3 (>50%).
Approximately 784 implant sites (392 with and 392 without marginal bone loss) from existing panoramic radiographs in the hospital archive will be included. No new radiographs will be taken, no patient will be contacted and no treatment will be given; informed consent has been waived by the ethics committee. All images will be fully anonymized before evaluation.
Read the detailed description
DESIGN AND SETTING Single-center, retrospective, observational diagnostic accuracy study conducted at Ankara University Faculty of Dentistry (Departments of Periodontology and Oral and Maxillofacial Radiology), Ankara, Türkiye. The study is reported according to STARD 2015.
IMAGE ACQUISITION AND ANONYMIZATION Panoramic radiographs taken during routine diagnosis and treatment will be retrieved retrospectively from the hospital information system/PACS archive. Before any evaluation, the data curator will remove all patient identifiers (name, national ID number, protocol number, acquisition date). Images will be exported in PNG format and each implant with its surrounding bone will be cropped to a standardized region of interest (ROI) using ImageJ. Each radiograph and each ROI will receive a random sequential code (e.g., R-001, IMP-001). Only anonymized, coded ROI images will be used in all subsequent steps.
REFERENCE STANDARD Three blinded investigators will independently grade each ROI according to the Froum and Rosen (2012) peri-implantitis classification, which expresses marginal bone loss as a proportion of implant length: Score 0, no visible marginal bone loss; Score 1 (mild), bone loss <25% of implant length; Score 2 (moderate), 25-50%; Score 3 (severe), >50%. A proportional index was chosen instead of millimetric measurement because it is robust to magnification differences on panoramic radiographs and accounts for implant length. Inter-rater reliability will be assessed with Fleiss' kappa. Disagreements will be resolved by consensus; the consensus score will serve as the reference standard ("ground truth").
INDEX TESTS Anonymized ROI images will be uploaded to the current secure/enterprise interfaces of five multimodal large language models (ChatGPT, Gemini, Claude, DeepSeek, Grok) with settings that prevent use of the images for model training. To avoid prompt-engineering bias, all models will receive an identical standardized zero-shot prompt (in Turkish) that (1) assigns the role of an expert periodontologist and dentomaxillofacial radiologist, (2) asks the model to examine the implant and marginal bone level in the attached panoramic radiograph section, (3) instructs it to grade bone loss as a proportion of total implant length, and (4) requires the output to be only one of Score 0, 1, 2 or 3 with no additional explanation. Model outputs will be recorded directly without modification.
SAMPLE SIZE An a priori power analysis (G*Power 3.1; exact binomial test, two-tailed, effect size g = 0.071, alpha = 0.05, power = 0.80, constant proportion = 0.50) based on Liu et al. (BMC Oral Health 2022;22:11) yielded a minimum of 392 implant sites per group; a balanced dataset of at least 784 implant sites (392 with and 392 without marginal bone loss) will be collected. Because one radiograph may contain several implants, the number of patients/radiographs will be smaller than the number of implant sites.
STATISTICAL ANALYSIS Sensitivity, specificity, positive and negative predictive values and overall accuracy of each model will be calculated against the reference standard, with 95% confidence intervals. Agreement between model outputs and the reference standard will be assessed with kappa statistics, differences between model and reference classifications with McNemar's test, and discriminative performance with ROC analysis. Analyses will be performed in SPSS and Python.
ETHICS Approved by the Ankara University Faculty of Dentistry Ethics Committee for Non-Clinical Scientific Research (decision no. 25/12). Informed consent was waived because the study is a retrospective archive review with no patient contact or intervention. No external funding.
- Conditions
- Peri-implantitis, Alveolar Bone Loss, Dental Implant
- Being tested
- Multimodal large language model evaluation (ChatGPT, Gemini, Claude, DeepSeek, Grok)
- Keywords
- large language model, artificial intelligence, vision-language model, panoramic radiography, marginal bone loss, diagnostic accuracy, peri-implant bone loss
- Medical subject headings
- Peri-Implantitis, Alveolar Bone Loss
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Multimodal large language model evaluation (ChatGPT, Gemini, Claude, DeepSeek, Grok)Diagnostic test
Each anonymized ROI image is uploaded to five multimodal large language models using an identical standardized zero-shot prompt; the model assigns a Froum and Rosen score (0-3). Outputs are compared with the clinician reference standard.
| Study group | Role | What they receive |
|---|---|---|
| Implant sites with marginal bone loss | Anonymized, standardized region-of-interest (ROI) images of functionally loaded dental implants on panoramic radiographs showing peri-implant marginal bone loss (Froum and Rosen Score 1, 2 or 3) according to the consensus of three blinded clinicians. Target: 392 implant sites. Receives: Diagnostic Test: Multimodal large language model evaluation (ChatGPT, Gemini, Claude, DeepSeek, Grok) | |
| Implant sites without marginal bone loss | Anonymized, standardized ROI images of functionally loaded dental implants on panoramic radiographs with no visible peri-implant marginal bone loss (Froum and Rosen Score 0) according to the consensus of three blinded clinicians. Target: 392 implant sites. Receives: Diagnostic Test: Multimodal large language model evaluation (ChatGPT, Gemini, Claude, DeepSeek, Grok) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity and specificity of each large language model for detecting peri-implant marginal bone loss
For each of the five models (ChatGPT, Gemini, Claude, DeepSeek, Grok): sensitivity and specificity for the presence of marginal bone loss (Froum and Rosen Score 1-3 vs Score 0) on anonymized panoramic ROI images, calculated against the consensus reference standard of three blinded clinicians, with 95% confidence intervals.
Measured: Single time point, at completion of model evaluation (approximately 8 weeks after study start); retrospective images, no follow-up
Secondary outcomes (4)
Positive predictive value, negative predictive value and overall accuracy of each model
PPV, NPV and overall accuracy of each of the five models for detecting marginal bone loss (present/absent), calculated against the reference standard with 95% confidence intervals.
Measured: Single time point, at completion of model evaluation (approximately 8 weeks after study start)
Agreement between model severity grading and the reference standard
Agreement of the 4-level Froum and Rosen scores (0-3) assigned by each model with the consensus reference score, assessed with weighted kappa; disagreement between model and reference classification tested with McNemar's test.
Measured: Single time point, at completion of model evaluation (approximately 8 weeks after study start)
Area under the ROC curve of each model
Discriminative performance of each model for marginal bone loss (present/absent), expressed as area under the receiver operating characteristic curve with 95% confidence interval.
Measured: Single time point, at completion of model evaluation (approximately 8 weeks after study start)
Inter-rater reliability of the three blinded clinicians
Fleiss' kappa for the independent Froum and Rosen scores (0-3) assigned by the three clinicians before consensus.
Measured: Single time point, at completion of clinician scoring (approximately 6 weeks after study start)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Oct 2026
- Study start27 Oct 2026 (estimated)
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Jan 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Peri-implantitisStudies of the same condition, open and under way.
- More trials run by Ankara UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 784 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Ankara University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, İlkim Karadağ, Ankara University
- Investigators
- Ilkim Karadag, PhD, Principal investigator, Ankara University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07907900
- Sponsor's protocol ID
- AUDHF-PERIO-LLM-2026
- Other IDs
- 25/12
- Status last verified
- Oct 2026
- First submitted
- 26 Oct 2026
- Last updated
- 30 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The ethics committee approval and study protocol state that data will be reported only in aggregate (statistical) form and will not be shared with third parties. The anonymized radiographic images and score data will therefore not be shared as individual participant data.
Publications
Liu M, Wang S, Chen H, Liu Y. A pilot study of a deep learning approach to detect marginal bone loss around implants. BMC Oral Health. 2022 Jan 16;22(1):11. doi: 10.1186/s12903-021-02035-8.
PubMed 35034611Froum SJ, Rosen PS. A proposed classification for peri-implantitis. Int J Periodontics Restorative Dent. 2012 Oct;32(5):533-40.
PubMed 22754901
Study at a glance
From the registry record, last updated by the sponsor on 30 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Diagnostic Accuracy of Large Language Models in the Evaluation of Peri-Implant Marginal Bone Loss on Panoramic Radiographs: A Multi-Observer Retrospective Study
- ClinicalTrials.gov ID
- NCT07907900
- Acronym / sponsor ID
- AUDHF-PERIO-LLM-2026
- Conditions
- Peri-implantitis, Alveolar Bone Loss, Dental Implant
- Keywords
- large language model, artificial intelligence, vision-language model, panoramic radiography, marginal bone loss, diagnostic accuracy, peri-implant bone loss
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 784 participants (estimated)
- Lead sponsor
- Ankara UniversityUniversity, hospital or other
- Study start
- 27 Oct 2026 (estimated)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Jan 2027 (estimated)
- First posted
- 30 Oct 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Dental implants are a routine treatment for missing teeth, but the number of implants affected by peri-implantitis, a progressive inflammatory disease of the tissues around implants, is increasing. The main radiographic sign of peri-implantitis is marginal bone loss around the implant. Early detection and correct grading of this bone loss in proportion to implant length are important for treatment planning and prognosis.
Panoramic radiographs are widely used for general screening in dental practice, but judging bone loss on them is subjective, time-consuming and depends on the clinician's experience. Multimodal large language models (vision-language models) are increasingly able to interpret images, yet their ability to evaluate peri-implant bone loss on panoramic radiographs has not been tested.
This single-center, retrospective, observational diagnostic accuracy study will evaluate how accurately five publicly available large language models (ChatGPT, Gemini, Claude, DeepSeek and Grok) detect and grade peri-implant marginal bone loss on anonymized, cropped panoramic radiographs of functionally loaded dental implants, compared with a reference standard established by three blinded clinicians (two periodontologists and one oral and maxillofacial radiologist). Bone loss will be graded with the Froum and Rosen (2012) classification: Score 0 (no visible bone loss), Score 1 (<25% of implant length), Score 2 (25-50%) and Score 3 (>50%).
Approximately 784 implant sites (392 with and 392 without marginal bone loss) from existing panoramic radiographs in the hospital archive will be included. No new radiographs will be taken, no patient will be contacted and no treatment will be given; informed consent has been waived by the ethics committee. All images will be fully anonymized before evaluation.
Detailed description
DESIGN AND SETTING Single-center, retrospective, observational diagnostic accuracy study conducted at Ankara University Faculty of Dentistry (Departments of Periodontology and Oral and Maxillofacial Radiology), Ankara, Türkiye. The study is reported according to STARD 2015.
IMAGE ACQUISITION AND ANONYMIZATION Panoramic radiographs taken during routine diagnosis and treatment will be retrieved retrospectively from the hospital information system/PACS archive. Before any evaluation, the data curator will remove all patient identifiers (name, national ID number, protocol number, acquisition date). Images will be exported in PNG format and each implant with its surrounding bone will be cropped to a standardized region of interest (ROI) using ImageJ. Each radiograph and each ROI will receive a random sequential code (e.g., R-001, IMP-001). Only anonymized, coded ROI images will be used in all subsequent steps.
REFERENCE STANDARD Three blinded investigators will independently grade each ROI according to the Froum and Rosen (2012) peri-implantitis classification, which expresses marginal bone loss as a proportion of implant length: Score 0, no visible marginal bone loss; Score 1 (mild), bone loss <25% of implant length; Score 2 (moderate), 25-50%; Score 3 (severe), >50%. A proportional index was chosen instead of millimetric measurement because it is robust to magnification differences on panoramic radiographs and accounts for implant length. Inter-rater reliability will be assessed with Fleiss' kappa. Disagreements will be resolved by consensus; the consensus score will serve as the reference standard ("ground truth").
INDEX TESTS Anonymized ROI images will be uploaded to the current secure/enterprise interfaces of five multimodal large language models (ChatGPT, Gemini, Claude, DeepSeek, Grok) with settings that prevent use of the images for model training. To avoid prompt-engineering bias, all models will receive an identical standardized zero-shot prompt (in Turkish) that (1) assigns the role of an expert periodontologist and dentomaxillofacial radiologist, (2) asks the model to examine the implant and marginal bone level in the attached panoramic radiograph section, (3) instructs it to grade bone loss as a proportion of total implant length, and (4) requires the output to be only one of Score 0, 1, 2 or 3 with no additional explanation. Model outputs will be recorded directly without modification.
SAMPLE SIZE An a priori power analysis (G*Power 3.1; exact binomial test, two-tailed, effect size g = 0.071, alpha = 0.05, power = 0.80, constant proportion = 0.50) based on Liu et al. (BMC Oral Health 2022;22:11) yielded a minimum of 392 implant sites per group; a balanced dataset of at least 784 implant sites (392 with and 392 without marginal bone loss) will be collected. Because one radiograph may contain several implants, the number of patients/radiographs will be smaller than the number of implant sites.
STATISTICAL ANALYSIS Sensitivity, specificity, positive and negative predictive values and overall accuracy of each model will be calculated against the reference standard, with 95% confidence intervals. Agreement between model outputs and the reference standard will be assessed with kappa statistics, differences between model and reference classifications with McNemar's test, and discriminative performance with ROC analysis. Analyses will be performed in SPSS and Python.
ETHICS Approved by the Ankara University Faculty of Dentistry Ethics Committee for Non-Clinical Scientific Research (decision no. 25/12). Informed consent was waived because the study is a retrospective archive review with no patient contact or intervention. No external funding.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Implant sites with marginal bone loss | – | Diagnostic Test: Multimodal large language model evaluation (ChatGPT, Gemini, Claude, DeepSeek, Grok)Anonymized, standardized region-of-interest (ROI) images of functionally loaded dental implants on panoramic radiographs showing peri-implant marginal bone loss (Froum and Rosen Score 1, 2 or 3) according to the consensus of three blinded clinicians. Target: 392 implant sites. |
| Implant sites without marginal bone loss | – | Diagnostic Test: Multimodal large language model evaluation (ChatGPT, Gemini, Claude, DeepSeek, Grok)Anonymized, standardized ROI images of functionally loaded dental implants on panoramic radiographs with no visible peri-implant marginal bone loss (Froum and Rosen Score 0) according to the consensus of three blinded clinicians. Target: 392 implant sites. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Anonymized panoramic radiographs retrieved retrospectively from the PACS archive of Ankara University Faculty of Dentistry, taken for diagnostic purposes in adult patients (18 years or older) of either sex who have at least one functionally loaded dental implant. The unit of analysis is the implant site (standardized cropped region of interest); one radiograph may contribute more than one implant site. Consecutive eligible radiographs will be screened until at least 392 implant sites with and 392 implant sites without marginal bone loss are obtained.
Key inclusion criteria (4 listed)
- Panoramic radiographs of adult patients (18 years or older) obtained during routine clinical care at Ankara University Faculty of Dentistry and stored in the hospital PACS archive
- At least one dental implant visible on the radiograph (with or without marginal bone loss)
- Bone-level implant in function, i.e., prosthetic loading completed
- Diagnostic image quality (contrast and sharpness) sufficient to clearly evaluate implant borders, threads, implant platform and the adjacent crestal bone
Key exclusion criteria (5 listed)
- Records of patients younger than 18 years
- High distortion due to patient movement, positioning error or device-related causes, or radiographic artifacts over the implant body or marginal bone that prevent evaluation
- Implants not yet prosthetically loaded (submerged or healing-abutment stage)
- Dense radiopaque bone graft material around the implant masking the natural marginal bone level
- Cystic lesions or tumoral formations around the implant that could be confused with, or completely distort, ordinary peri-implant marginal bone loss
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Multimodal large language model evaluation (ChatGPT, Gemini, Claude, DeepSeek, Grok)Each anonymized ROI image is uploaded to five multimodal large language models using an identical standardized zero-shot prompt; the model assigns a Froum and Rosen score (0-3). Outputs are compared with the clinician reference standard. | Diagnostic test | Implant sites with marginal bone loss, Implant sites without marginal bone loss |
Primary outcome
Sensitivity and specificity of each large language model for detecting peri-implant marginal bone loss
Time frame: Single time point, at completion of model evaluation (approximately 8 weeks after study start); retrospective images, no follow-up
For each of the five models (ChatGPT, Gemini, Claude, DeepSeek, Grok): sensitivity and specificity for the presence of marginal bone loss (Froum and Rosen Score 1-3 vs Score 0) on anonymized panoramic ROI images, calculated against the consensus reference standard of three blinded clinicians, with 95% confidence intervals.
4 secondary outcomes
Positive predictive value, negative predictive value and overall accuracy of each model
Time frame: Single time point, at completion of model evaluation (approximately 8 weeks after study start)
Agreement between model severity grading and the reference standard
Time frame: Single time point, at completion of model evaluation (approximately 8 weeks after study start)
Area under the ROC curve of each model
Time frame: Single time point, at completion of model evaluation (approximately 8 weeks after study start)
Inter-rater reliability of the three blinded clinicians
Time frame: Single time point, at completion of clinician scoring (approximately 6 weeks after study start)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Oct 2026.
- Background: PubMed 35034611Liu M, Wang S, Chen H, Liu Y. A pilot study of a deep learning approach to detect marginal bone loss around implants. BMC Oral Health. 2022 Jan 16;22(1):11. doi: 10.1186/s12903-021-02035-8.
- Background: PubMed 22754901Froum SJ, Rosen PS. A proposed classification for peri-implantitis. Int J Periodontics Restorative Dent. 2012 Oct;32(5):533-40.
- Individual participant dataSharing planned: No. The ethics committee approval and study protocol state that data will be reported only in aggregate (statistical) form and will not be shared with third parties. The anonymized radiographic images and score data will therefore not be shared as individual participant data.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Ilkim Karadag, PhD principal investigator
Ankara University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Peri-implantitis and Alveolar Bone Loss, run by Ankara University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 30 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Ilkim Karadag, PhD (+903122965690, karadag@ankara.edu.tr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity and specificity of each large language model for detecting peri-implant marginal bone loss (time frame: Single time point, at completion of model evaluation (approximately 8 weeks after study start); retrospective images, no follow-up). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07907900, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Oct 2026.