AI may identify specific sites with and without gingival inflammation as accurately as visua examination by human

Results

A total of 567 intraoral photographs were collected and labelled, of which 80% were used for training and 20% for validation. Regarding training datasets, there were total 113,745,208 pixels with 9,270,413; 5,711,027; and 4,596,612 pixels were labelled as healthy, diseased, and questionable respectively. Regarding validation datasets, there were 28,319,607 pixels with 1,732,031; 1,866,104; and 1,116,493 pixels were labelled as healthy, diseased, and questionable, respectively. AI correctly predicted 1,114,623 healthy and 1,183,718 diseased pixels with sensitivity of 0.92 and specificity of 0.94. The mean intersection-over-union of the system was 0.60 and above the commonly accepted threshold of 0.50.

Conclusions

Artificial intelligence could identify specific sites with and without gingival inflammation, with high sensitivity and high specificity that are on par with visual examination by human dentist. This system may be used for monitoring of the effectiveness of patients’ plaque control.

Reference:

Reinhard Chun Wang Chau, Guan-Hua Li, In Meei Tew, Khaing Myat Thu, Colman McGrath, Wai-Lun Lo, Wing-Kuen Ling, Richard Tai-Chiu Hsung, Walter Yu Hang Lam. Accuracy of Artificial Intelligence-Based Photographic Detection of Gingivitis, International Dental Journal, 2023, ISSN 0020-6539, https://doi.org/10.1016/j.identj.2023.03.007.

(https://www.sciencedirect.com/science/article/pii/S0020653923000606)