LAHORE, Pakistan: Because treating periodontal disease requires a nuanced approach, a means of predicting the most likely treatment requirements could help dental professionals tailor treatments to individual patients. In a new study, researchers in Pakistan have evaluated whether using a self-developed machine learning model—a form of artificial intelligence (AI)—could help predict the level of severity of even the most complex courses of periodontal disease after treatment. Though the data employed was limited, they found that AI could influence the periodontal disease treatment carried out, indicating a greater role for machine learning in patient care.
In recent years, the use of AI in healthcare has been studied a great deal, and its potential to improve diagnosis and treatment outcomes has been demonstrated. For example, it has been successfully applied to diagnosis of periodontal disease using panoramic radiographs. However, there has been little research regarding its use to predict the course and outcomes of periodontal disease.
After generating a synthetic data set of 1,000 patients, focusing on variables like age, smoking status and disease severity before and after treatment, the researchers in this study employed a linear regression machine learning model for predictive analysis. The artificial patients were aged 20–80 years and had a median age of 45 years. Half of them were smokers, and roughly half had received periodontal treatment. The severity of periodontal disease ranged from 0 (healthy) to 10 (severe), and post-treatment observations showed a general decrease in disease severity.
Correlation analyses found no significant relationship between smoking habits, age and disease severity either before or after treatment. There was a weak correlation between age and treatment outcome, a surprising lack of a significant relationship between smoking and post-treatment disease severity, and a positive correlation between disease severity pre- and post-treatment. Notably, those with severe disease before treatment often showed severe disease post-treatment too, suggesting that more severe cases might be more difficult to treat effectively. The model illuminated the nuanced interactions of demographic and disease variables, but had limited predictive success, in part because the research assumed that the treatment given was universally effective, and it did not consider clinician’s skill, for example.
Clinically, the findings underscore the need for personalised care, factoring in individual patient nuances. From an AI perspective, the study highlights challenges in healthcare predictions, emphasising the potential and need for continuous AI refinement. The study authors recommended that future research should address the study limitations, such as the artificially generated data, and possibly incorporate advanced AI methodologies, as we inch closer to AI-driven predictive healthcare. They additionally suggested that future research could explore other ways of training models, like gradient boosting or neural networks, for comparison.
The study, titled “Role of artificial intelligence in periodontology”, was published on 27 May 2023 in Pakistan Journal of Medical and Health Sciences.
Tags:
Artificial intelligence (AI) presents dental clinicians with a multitude of helpful aids, and researchers are continually expanding the capabilities that AI...
ISTANBUL, Turkey: Digital smile design (DSD) programs have become instrumental in dental treatment, enabling dentists and patients to plan treatments in ...
HONK KONG: The applications of artificial intelligence (AI) in dentistry have been widely explored in recent years. However, a recent study is one of the ...
ESPOO/HELSINKI/TAMPERE, Finland: Studies have shown that artificial intelligence (AI) can recognise structural patterns in medical imaging data. However, in...
HONG KONG: CAD/CAM technology has greatly improved the productivity of dental prostheses but still has its drawbacks when it comes to the design of crowns....
The May 2019 edition of The Atlantic magazine contained an article titled “The truth about dentistry”. In it, the author visualised dentists—not a ...
FARMINGTON, Conn., US: Many factors may influence therapeutic decisions in orthodontics. For example, orthodontists may sometimes inaccurately interpret ...
Live webinar
Tue. 6 August 2024
6:00 pm EST (New York)
Live webinar
Tue. 6 August 2024
8:00 pm EST (New York)
Dr. Cameron Shahbazian DMD MBA
Live webinar
Tue. 13 August 2024
7:00 pm EST (New York)
Live webinar
Wed. 14 August 2024
12:30 pm EST (New York)
Live webinar
Wed. 21 August 2024
9:00 am EST (New York)
Dr. Jim Lai DMD, MSc(Perio), EdD, FRCD(C)
Live webinar
Thu. 22 August 2024
4:00 pm EST (New York)
Live webinar
Wed. 28 August 2024
8:00 pm EST (New York)
To post a reply please login or register