Clinical trial NCT06071338: A Decision Support System Based on Classification Algorithms for the Diagnosis of Periodontal Disease
Official title: A Decision Support System Based on Classification Algorithms for the Diagnosis of Periodontal Disease
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Periodontal Diseases
Keywords: periodontal
disease
diagnosis
machine
learning
Saudi
Lead sponsor: Ministry of Health, Saudi Arabia
Sponsor class: OTHER_GOV
First posted: 2023-10-06
Enrollment: 250
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
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.

Brief 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.

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.

Eligibility criteria: 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:

* refuse to volunteer in the study

Primary outcomes:
measure: Age
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Gender
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Marital statue
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Education
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Physical Activities
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Bleeding on Probing
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Gingival Index
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Plaque Index
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Periodontal Probing Depth
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Recession Type
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Clinical Attachment Loss
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Radiographic Bone Loss
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Tooth Mobility
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Missing Teeth
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Furcation Involvement
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Diabetes
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Smoking
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Body Mass Index
timeFrame: One time measure; baseline during periodontal clinical examination.
measure: Cardiovascular Disease
timeFrame: One time measure; baseline during periodontal clinical examination.
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
affiliation: General Directorate of Health Affairs, Aseer Region, KSA.
name: Abdulrahman Al Shehri, BDS, MS
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.