A proof of concept study to confirm the suitability of an intra oral scanner to record oral images for the non-invasive assessment of gingival inflammation. (February 2021) - Daly, Sinéad; Seong, Joon; Parkinson, Charles; Newcombe, Robert; Claydon, Nicholas; West, Nicola
Abstract: Objectives: To compare gingival inflammation scores obtained chairside using the non-invasive modified gingival index (MGI) with MGI scores from an intraoral scan (IOS) captured at the clinical visit but viewed 10 days later. Methods: Single visit, anterior teeth, observational, proof of concept study in healthy adult participants with a spectrum of gingival inflammation. One investigator performed both clinical and intraoral scan MGI assessments, a second repeated the MGI evaluation from the IOS. Results: 23 participants aged 18–72 yielded data for 552 gingival sites. There was agreement at 90 % of sites comparing clinical with IOS MGI scores. The commonest disagreements were MGI grade 0 read as 1 and 2 read as 3, the highest single probability of error occurring where a clinical score of 0 was scored 1 from the IOS: 0.118 and 0.129 for examiners 1 and 2 respectively. The second most common probability of error occurred where an IOS score of 3 was scored clinically as 2: 0.089 and 0.097 for examiners 1 and 2 respectively. MGI scores from the scans were similar for both examiners (91 % agreement), with no discrepancies of greater than 1 scale point. There was very close agreement between clinical MGI and IOS colour/texture scores. Conclusion: This study conclusively demonstrated that the MGI score from the scanned image was very similar to the MGI scored clinically. This study confirms that a digital IOS accurately captures gingival contour images allowing a clinician to determine health or degree of gingival inflammation from it using MGI scores. Clinical Significance Statement: This study confirms that IOS images of teeth and soft tissues are sufficiently accurate to allow the clinical evaluation of health or inflammatory gingival status using non-invasive indices. IOS has great potential for efficient and accurate data capture, for general practice and research facilitating remote evaluation and data verification.