ClinicalTrials.gg
Not yet recruitingNCT06071338Updated Oct 6, 2023

A Decision Support System Based on Classification Algorithms for the Diagnosis of Periodontal Disease

An observational study in Periodontal Diseases, sponsored by Ministry of Health, Saudi Arabia. Not yet recruiting. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2023-10-06.

Sponsored by Ministry of Health, Saudi Arabia · Observational

From the registry’s dates

  • Primary completion was expected by Dec 2024, 1 year 10 months ago, but the record still lists the study as not yet recruiting.
Study type
Observational
Model
Other
Time perspective
Cross-sectional
Enrollment
250
Ages
18 Years and older
Sex
All
01

Study summary

The study purposes For periodontal applications, such as diagnosing gingivitis and periodontal disease, artificial intelligence (AI) models have been developed; however, their accuracy and technological maturity are to be evolved. The applications of such technologies in the field of periodontics are walking baby steps worldwide. The Kingdom of Saudi Arabia is moving fast in technology adoption and implementation in different sectors. However, the healthcare sector, especially clinical-related, needs original research applied to Saudi subjects. The literature in the field of machine learning applications in dentistry is limited. Although AI models for periodontology applications are still being developed, they could serve as potent diagnostic instruments. The current study was planned to add to the current gap in the literature.

Read the detailed description

A cross-sectional study design will be applied. Two hundred fifty patients will be evaluated by an experienced periodontist and used as input variables. The final diagnosis output will be generated by considering relevant information from the patient's medical history, clinical dental examination, and radiographic exam. Of the sample of 250 patients, 20% of the participants will be assigned randomly to the test group, while the rest will be assigned to the training group before feeding it to the algorithms.

The study purposes For periodontal applications, such as diagnosing gingivitis and periodontal disease, artificial intelligence (AI) models have been developed; however, their accuracy and technological maturity are to be evolved. The applications of such technologies in the field of periodontics are walking baby steps worldwide. The Kingdom of Saudi Arabia is moving fast in technology adoption and implementation in different sectors. However, the healthcare sector, especially clinical-related, needs original research applied to Saudi subjects. The literature in the field of machine learning applications in dentistry is limited. Although AI models for periodontology applications are still being developed, they could serve as potent diagnostic instruments. The current study was planned to add to the current gap in the literature.

02

Conditions studied

  • Periodontal Diseases

Keywords

  • periodontal
  • disease
  • diagnosis
  • machine
  • learning
  • Saudi
03

In context

Periodontal Diseases

867 studies on the registry are indexed under Periodontal Diseases; 189 are open to participants now.

This study's planned enrollment of 250 is above the median of 99 across 302 observational studies indexed under Periodontal Diseases.

Browse Periodontal Diseases studies →

Lead sponsor

Ministry of Health, Saudi Arabia is the lead sponsor of 21 studies on the registry; 8 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years and older
Sexes eligible
All
Accepts healthy volunteers
Yes
Sampling method
Probability sample

Study population

Data will be collected randomly during the study period planned to end by the end of the year 2023. Data will be collected from participants attending the periodontal department at Abha Specialized Dental Center seeking periodontal treatment.

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

  • age 18 or more
  • no periodontal treatment has been done at least 6 months prior to the enrollment
  • seeking periodontal treatment

Exclusion criteria

Exclusion Criteria:

  • refuse to volunteer in the study
05

Study design

Observational model
Other
Time perspective
Cross-sectional
Enrollment
250 participants (estimated)
Target follow-up
1 Month
Patient registry
Yes

Interventions

  • Diagnostic testClinical Periodontal Examination

    No details will be published from the clinical assessment.

    Also known as: orthopantomography Assessment

06

What researchers measure

Primary outcomes

  1. Age

    \<30, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, \>80

    Time frame: One time measure; baseline during periodontal clinical examination.

  2. Gender

    Male; Female.

    Time frame: One time measure; baseline during periodontal clinical examination.

  3. Marital statue

    Married, widowed, divorced, never married

    Time frame: One time measure; baseline during periodontal clinical examination.

  4. Education

    \< secondary school, secondary school, graduate or equivalent, some college or associates degree, College graduate or higher

    Time frame: One time measure; baseline during periodontal clinical examination.

  5. Physical Activities

    Light, Moderate, Heavy

    Time frame: One time measure; baseline during periodontal clinical examination.

  6. Bleeding on Probing

    Yes; No

    Time frame: One time measure; baseline during periodontal clinical examination.

  7. Gingival Index

    Percentage

    Time frame: One time measure; baseline during periodontal clinical examination.

  8. Plaque Index

    Percentage

    Time frame: One time measure; baseline during periodontal clinical examination.

  9. Periodontal Probing Depth

    PPD at the site of greatest loss (number)

    Time frame: One time measure; baseline during periodontal clinical examination.

  10. Recession Type

    Yes; No

    Time frame: One time measure; baseline during periodontal clinical examination.

  11. Clinical Attachment Loss

    CAL at the site of greatest loss (number)

    Time frame: One time measure; baseline during periodontal clinical examination.

  12. Radiographic Bone Loss

    RBL at the site of greatest loss (percentage)

    Time frame: One time measure; baseline during periodontal clinical examination.

  13. Tooth Mobility

    Yes; No

    Time frame: One time measure; baseline during periodontal clinical examination.

  14. Missing Teeth

    Yes; No

    Time frame: One time measure; baseline during periodontal clinical examination.

  15. Furcation Involvement

    Yes; No

    Time frame: One time measure; baseline during periodontal clinical examination.

  16. Diabetes

    Healthy, \<7% HbA1c, ≥7% HbA1c

    Time frame: One time measure; baseline during periodontal clinical examination.

  17. Smoking

    None, \<10 cig/day, ≥10cig/day

    Time frame: One time measure; baseline during periodontal clinical examination.

  18. Body Mass Index

    Underweight, Healthy, Overweight, Obese

    Time frame: One time measure; baseline during periodontal clinical examination.

  19. Cardiovascular Disease

    Yes; No

    Time frame: One time measure; baseline during periodontal clinical examination.

07

Study locations

No study locations are listed for this record.

08

References and documents

Publications

  • Ozden FO, Ozgonenel O, Ozden B, Aydogdu A. Diagnosis of periodontal diseases using different classification algorithms: a preliminary study. Niger J Clin Pract. 2015 May-Jun;18(3):416-21. doi: 10.4103/1119-3077.151785. PubMed 25772929 ↗
  • Alqahtani HM, Koroukian SM, Stange K, Schiltz NK, Bissada NF. Identifying Factors Associated with Periodontal Disease Using Machine Learning. J Int Soc Prev Community Dent. 2022 Dec 30;12(6):612-622. doi: 10.4103/jispcd.JISPCD_188_22. eCollection 2022 Nov-Dec. PubMed 36777017 ↗

Individual participant data

Plan to share: No — No intention and no need to share individual patient data with research community.

09

Timeline

Tracking since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reached

Start is estimated for Oct 2023; primary completion for Dec 2024, 14 months later.

How these figures are worked out, and which studies are left out of them.

Registered
Sep 22, 2023
First posted
Oct 6, 2023
StartEst.
Oct 1, 2023
Primary completionEst.
Dec 30, 2024
CompletionEst.
Dec 30, 2024
Last update
Oct 6, 2023
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
No changes since tracking began. The registry record was last updated on Oct 6, 2023, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT06071338
Lead sponsor
Ministry of Health, Saudi Arabia
Responsible party
Abdulrahman Alshehri (Principal Investigator/Clinical Specialist, Ministry of Health, Saudi Arabia) — Principal investigator
First posted
Oct 6, 2023
Start date
Oct 1, 2023 (estimated)
Primary completion
Dec 30, 2024 (estimated)
Completion
Dec 30, 2024 (estimated)
Last update
Oct 6, 2023

Study contacts

Abdulrahman Al Shehri, BSD, MS
Contact
alshehriabdulrahman3@gmail.com
+966507090251
Abdulrahman Al Shehri, BDS, MS
Contact
alshehriabdulrahman3@gmail.com
+966507090251
Abdulrahman Al Shehri, BDS, MS
principal investigator · General Directorate of Health Affairs, Aseer Region, KSA.

Oversight

Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is not yet recruiting, as verified in Oct 2023. You cannot join it, but the record below documents what was studied.

Follow this study

Get an email when the registry record changes — status, dates, results — or when someone posts here.

Sign in to follow

Discussion

Questions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.

Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.

Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.

Start the discussion