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

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.