Association for Dental Education in Europe

Learning together to improve oral health and quality of life

AI: Analysis of Outcomes of Oral Healthcare at a US Dental School

Monday, 9th September 2024 - 11:30 to 13:00
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Timezone: 

CEST (Brussels time)

Duration: 

90 minutes

Venue: 

HP8

Session synopsis: 
Utilizing AI to develop a prediction model for Patient Wellness Report at Temple University School of Dentistry.
Background

Temple University School of Dentistry has developed and implemented a Patient Wellness Report (PWR) for assessing oral, dental, quality of life, health literacy, and general health outcomes as part of comprehensive oral health care . The PWR measures 14 wellness dimensions (scale: good-fair-poor) to monitor improvements in all outcomes. Determining its effectiveness in improving dental and oral disease outcomes is critical, especially in identifying factors responsible for disease improvement versus disease progression (DIVP).

Objectives

Using Artificial Intelligence (AI) prediction methods, this analysis evaluates the effectiveness of oral and dental care on four measures of dental caries and periodontal diseases.

Materials and methods

PWR datasets of 7,344 patients (caries and periodontal disease) between Jan-2022 – Dec-2023 were obtained. Three outcome categories and a rank-based system were created (see Abstract 1 for more information) for binary classification to determine DIVP. AI models (Random Forest, Decision Tree, Naïve Bayes, XGBoost) and de-biasing methods (cross-validations, sampling) were utilized for testing and training the prediction model. The performance of the models was evaluated using sensitivity, specificity, precision, recall, and f-1 measures.

Results

The XGBoost model performed best with a 75% f-1 score, 83% precision, and 72% recall. For caries measures, age, fluoride, recall exams, dietary advice, number of bitewing radiographs exposed, and number of periodontal treatments were strong predictors of disease improvement. For disease progression, the number of restorations and higher number of treatment codes were major predictors. For periodontal disease, the number of periodontal treatment codes, dietary counselling, prosthetic prostheses , and recall examples were associated with disease improvement, and older age, poor oral hygiene, and higher number of restorations were associated with disease progression.

Discussion and conclusion

This study demonstrated the successful application of AI to build the prediction model using PWRs in a dental school setting. We found fluoride, recall exams, periodontal treatments, and dietary advice were helpful in disease outcomes. This study provides recommendations for other schools to utilize such tools to improve the outcomes of dental diseases.

Programme: 
11:30

Welcome and introduction

11:35

Artificial Intelligence (AI): Analysis of Outcomes of Oral Healthcare at a US Dental School Part I:
Descriptive Analysis

11:45

Artificial Intelligence (AI): Analysis of Outcomes of Oral Healthcare at a US Dental School Part II:
AI methods and their potential application in dental education and patient care

12:55

Artificial Intelligence (AI): Analysis of Outcomes of Oral Healthcare at a US Dental School Part III:
Utilizing AI to develop a prediction model

12:05

Artificial Intelligence (AI):
Analysis of Outcomes of Oral Healthcare at a US Dental School: Part IV: Comparative analysis of outcomes using the generalized linear models

12:15

Artificial Intelligence (AI): Analysis of Outcomes of Oral Healthcare at a US Dental School Part V:
Discussion of results from AI and GLM analyses

12:30

Discussion, Q&A and take away message

13:00

End

Chair: 

Amid I. Ismail

Dean
Temple University Kornberg School of Dentistry, Philadelphia, Pennsylvania, USA

Dr. Amid I. Ismail, BDS, MPH, DrPH, MBA, is the Laura H. Carnell Professor and Dean of Temple University Kornberg School of Dentistry, Philadelphia,...Read More

Speakers: 

Sungwoo Lim

Associate professor at Oral health Science
Kornberg School of Dentistry, Temple University

Dr. Sungwoo Lim, DrPH, MA, MS, is an adjunct associate professor at Oral health Science, Kornberg School of Dentistry, Temple University. Trained as...Read More

Jay Patel

Director of the Center for Dental Informatics and AI
Temple University Kornberg School of Dentistry

Dr. Jay Patel (BDS, MS, PhD in informatics) is an Assistant Professor and the Director of the Center for Dental Informatics and AI at the Temple...Read More

Related documents: 
AttachmentSize
PDF icon Abstracts for each session item 365.62 KB