Tracking periodontal disease with electronic dental records


Indianapolis, USA: Despite advances in periodontal disease research and treatment, periodontal disease remains a growing health problem in the United States. To address this topic, researchers at the Regenstrieff Institute and the Indiana University School of Dentistry in Indianapolis developed algorithms to track changes in periodontal disease through electronic dental records. This approach can help dental professionals track disease progression and diagnose disease early (when the disease may still be reversible), thereby reducing the risk of other systemic diseases associated with periodontal disease.

For their study, the researchers used data from 28,908 patients who underwent comprehensive oral evaluations at dental school clinics between 2009 and 2014. information and categorized them into three groups—patients with progressive disease, patients with improved disease, and patients with no change in disease. The algorithm was applied to 15 years of electronic dental record data to produce the final patient population. Both algorithms show a high accuracy of 98% and are publicly available for use by other researchers.

Gum disease is often underdiagnosed and is reversible if caught early, before it affects the underlying structure and adversely affects tooth support. Enables dentists to use information from clinical records and dental information contained in the patient's electronic dental record Weekly charting data to track disease can enable diagnosis and provide hope,” said co-author Dr. Thanham Thyvalikath, director of the institution's Joint Dental Informatics Program.

“We are here to develop and establish a culture of recording and diagnosing cases in a structured way, just like what is done in medicine,” she added.

The widespread use of electronic dental record systems to record patient care information provides important opportunities to study the clinical course of periodontal disease and the impact of risk factors. “I think the advantage of our approach is that using routinely collected data we can automate and monitor the treatment of gum disease and changes that are only clinically visible, so we can catch gum disease in its early, potentially reversible stages. This is different from just This contrasts with other methods that utilize radiographs, which only show advanced gum disease.

The authors concluded that their study demonstrated the feasibility of using longitudinal electronic dental record data to track changes in periodontal disease and that their algorithm successfully used these data to classify three distinct patient groups. This approach can be used to study the clinical course of periodontal disease using artificial intelligence, including machine learning methods.

Additionally, Dr. Thyvalikakath commented on the importance of tracking periodontal disease for an interdisciplinary approach: “There is a bidirectional relationship between certain risk factors and gum disease. For example, having diabetes increases the risk of periodontal disease, and having gum disease increases the risk of periodontal disease. Periodontal disease can negatively impact the course of diabetes. There is a similar relationship between cardiovascular disease and periodontal disease. Recognizing, monitoring, and treating gum disease is an important part of a patient's overall health.

The study, “Development of automated computer algorithms to track periodontal disease changes in longitudinal electronic dental records,” was published in a special issue on March 8, 2023 Advances in using artificial intelligence for biomedical and dental diagnostics of diagnosis.

Label:




Source link

Leave a Reply

Your email address will not be published. Required fields are marked *