PeriodontitisNCT07086625
AI-Based Radiographic Detection of Periodontal Defects
An observational study of Periodontitis, run by University of Cagliari at 1 site in Italy.
Trial summary
The primary objective of the study is to develop and validate a machine learning model for the automatic identification of periodontal vertical bone defects, improving diagnostic accuracy and efficiency. The study comprises three phases: 1. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Periodontitis
- How many people
- 500 participants
- Where
- 1 siteItaly
- Study dates
- 1 Jan 2024to 31 Mar 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: A dataset of intraoral radiographs
Who may be able to join
- Intraoral radiographs showing presence of periodontal infrabony defects
Who cannot join
- Intraoral radiographs showing without detectable presence of periodontal infrabony defects
Trial locations and contacts
1 site in 1 country.
About this study
Official title: AI-Based Radiographic Detection of Periodontal Infrabony and Furcation Defects: A Diagnostic Model Study
The primary objective of the study is to develop and validate a machine learning model for the automatic identification of periodontal vertical bone defects, improving diagnostic accuracy and efficiency.
The study comprises three phases:
- Public dataset annotation: Approximately 7,000 intraoral radiographs will be manually annotated by experts to classify periodontal bone defects (1-wall, 2+ walls, craters, furcation involvement).
- Model training: A deep learning algorithm will be trained on the annotated images to learn automatic recognition of the defects.
- Clinical validation: The model will be tested on a dataset of 150 anonymized radiographs from 20-30 patients treated at AOU (Azienda Ospedaliero Universitaria) Cagliari, comparing its performance to expert dental evaluations.
Read the detailed description
To address the challenge of detecting periodontal osseous defects, the study will employ the YOLOv8 (You Only Look Once versione 8) framework, a state-of-the-art deep learning model optimized for object detection tasks. This architecture is known for its balance between accuracy and inference speed, making it suitable for clinical applications that require efficient processing.
The YOLOv8l (large) variant will be selected to maximize detection accuracy, given the complexity of the task. The architecture will include:
- Backbone: a convolutional neural network (CNN) for multi-scale feature extraction;
- Neck: a feature pyramid network (FPN) to integrate spatial and semantic information across layers;
- Head: a detection module responsible for class probabilities and bounding box predictions.
Model Training
The training will be performed on a dataset consisting of approximately 406 images for training, 58 for validation, and 117 for testing. Annotations will include bounding boxes for four types of defects: 1-wall, 2+ walls, craters, and furcation involvement. The dataset will be formatted according to YOLO standards.
Key training parameters will include:
- Input resolution: 640 × 640 pixels
- Batch size: 16
- Optimizer: AdamW with a learning rate of 0.0014
- Epochs: 100
- Loss function: a combination of box, class, and distribution focal loss (DFL)
- Hardware: NVIDIA GeForce RTX 4090 with mixed precision training
Inference and Evaluation
Inference will be conducted on the test set, and model performance will be evaluated using standard object detection metrics. These will include:
- Intersection over Union (IoU) to measure the overlap between predicted and ground truth bounding boxes
- Precision and recall, computed based on true positives, false positives, and false negatives
- Confidence threshold (initially set at 0.25) to filter out low-confidence predictions
- Precision-recall curves, to visualize the trade-off between precision and recall at varying thresholds
Performance will be summarized using mean Average Precision (mAP):
- mAP@0.5: mean average precision at a fixed IoU threshold of 0.5
- mAP@0.5:0.95: mean average precision averaged across multiple IoU thresholds (from 0.5 to 0.95 in 0.05 increments)
The model's detection capabilities will be assessed across all four classes of periodontal bone defects, providing a comprehensive evaluation of its diagnostic potential.
- Conditions
- Periodontitis
- Keywords
- Periodontal infrabony defects
- Medical subject headings
- Periodontitis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| intraoral radiographs | Intraoral radiographs images showing periodontal infrabony defects |
What is being measured
The primary outcome is the main question the study is designed to answer.
Intersection over Union (IoU)
The IoU measures the overlap between a predicted bounding box and a ground truth bounding box. It is defined as: Area of Overlap/Area of Union; where the area of overlap is the intersection of the predicted and ground truth boxes, and the area of union is the total area covered by both boxes.
Measured: Baseline
Precision (P)
The fraction of true positives (TP) among all predictions:
T P/T P + F P High precision indicates that the model makes few false positive (FP) predictions.
Measured: Baseline
Recall (R)
The fraction of true positives among all ground truth objects:
T P/T P + F N (false negatives) High recall indicates that the model detects most ground truth objects.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Jul 2025
- Study start1 Jan 2024
- Primary completion31 Dec 2024
- Study completion31 Mar 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for PeriodontitisStudies of the same condition, open and under way.
- 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 500 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Cagliari
- Sponsor type
- University, hospital or other
- Collaborators
- University of L'Aquila
- Responsible party
- Principal investigator, Nicola Alberto Valente, DDS, MS, PhD, University of Cagliari
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07086625
- Sponsor's protocol ID
- PerioDefects-AI
- Status last verified
- Jul 2025
- First submitted
- 11 Jul 2025
- Last updated
- 25 Jul 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: All radiographs are anonymized
Study at a glance
From the registry record, last updated by the sponsor on 25 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-Based Radiographic Detection of Periodontal Infrabony and Furcation Defects: A Diagnostic Model Study
- ClinicalTrials.gov ID
- NCT07086625
- Acronym / sponsor ID
- PerioDefects-AI
- Conditions
- Periodontitis
- Keywords
- Periodontal infrabony defects
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 500 participants (actual)
- Lead sponsor
- University of CagliariUniversity, hospital or other
- Collaborators
- University of L'Aquila
- Study start
- 1 Jan 2024 (actual)
- Primary completion
- 31 Dec 2024 (actual)
- Study completion
- 31 Mar 2025 (actual)
- First posted
- 25 Jul 2025 (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.
The primary objective of the study is to develop and validate a machine learning model for the automatic identification of periodontal vertical bone defects, improving diagnostic accuracy and efficiency.
The study comprises three phases:
- Public dataset annotation: Approximately 7,000 intraoral radiographs will be manually annotated by experts to classify periodontal bone defects (1-wall, 2+ walls, craters, furcation involvement).
- Model training: A deep learning algorithm will be trained on the annotated images to learn automatic recognition of the defects.
- Clinical validation: The model will be tested on a dataset of 150 anonymized radiographs from 20-30 patients treated at AOU (Azienda Ospedaliero Universitaria) Cagliari, comparing its performance to expert dental evaluations.
Detailed description
To address the challenge of detecting periodontal osseous defects, the study will employ the YOLOv8 (You Only Look Once versione 8) framework, a state-of-the-art deep learning model optimized for object detection tasks. This architecture is known for its balance between accuracy and inference speed, making it suitable for clinical applications that require efficient processing.
The YOLOv8l (large) variant will be selected to maximize detection accuracy, given the complexity of the task. The architecture will include:
- Backbone: a convolutional neural network (CNN) for multi-scale feature extraction;
- Neck: a feature pyramid network (FPN) to integrate spatial and semantic information across layers;
- Head: a detection module responsible for class probabilities and bounding box predictions.
Model Training
The training will be performed on a dataset consisting of approximately 406 images for training, 58 for validation, and 117 for testing. Annotations will include bounding boxes for four types of defects: 1-wall, 2+ walls, craters, and furcation involvement. The dataset will be formatted according to YOLO standards.
Key training parameters will include:
- Input resolution: 640 × 640 pixels
- Batch size: 16
- Optimizer: AdamW with a learning rate of 0.0014
- Epochs: 100
- Loss function: a combination of box, class, and distribution focal loss (DFL)
- Hardware: NVIDIA GeForce RTX 4090 with mixed precision training
Inference and Evaluation
Inference will be conducted on the test set, and model performance will be evaluated using standard object detection metrics. These will include:
- Intersection over Union (IoU) to measure the overlap between predicted and ground truth bounding boxes
- Precision and recall, computed based on true positives, false positives, and false negatives
- Confidence threshold (initially set at 0.25) to filter out low-confidence predictions
- Precision-recall curves, to visualize the trade-off between precision and recall at varying thresholds
Performance will be summarized using mean Average Precision (mAP):
- mAP@0.5: mean average precision at a fixed IoU threshold of 0.5
- mAP@0.5:0.95: mean average precision averaged across multiple IoU thresholds (from 0.5 to 0.95 in 0.05 increments)
The model's detection capabilities will be assessed across all four classes of periodontal bone defects, providing a comprehensive evaluation of its diagnostic potential.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| intraoral radiographs | – | –Intraoral radiographs images showing periodontal infrabony defects |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- A dataset of intraoral radiographs
Key inclusion criteria (1 listed)
- Intraoral radiographs showing presence of periodontal infrabony defects
Key exclusion criteria (1 listed)
- Intraoral radiographs showing without detectable presence of periodontal infrabony defects
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Intersection over Union (IoU)
Time frame: Baseline
The IoU measures the overlap between a predicted bounding box and a ground truth bounding box. It is defined as: Area of Overlap/Area of Union; where the area of overlap is the intersection of the predicted and ground truth boxes, and the area of union is the total area covered by both boxes.
Precision (P)
Time frame: Baseline
The fraction of true positives (TP) among all predictions: T P/T P + F P High precision indicates that the model makes few false positive (FP) predictions.
Recall (R)
Time frame: Baseline
The fraction of true positives among all ground truth objects: T P/T P + F N (false negatives) High recall indicates that the model detects most ground truth objects.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Jul 2025.
- Individual participant dataSharing planned: No. All radiographs are anonymized
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
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 Periodontitis, run by University of Cagliari at 1 site in Italy.
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 1 inclusion and 1 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: finished, as of the sponsor's last update on 25 Jul 2025. 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 Italy. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Intersection over Union (IoU) (time frame: Baseline). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 25 Jul 2025.