• R R Nagate, S Tikare, S Chaturvedi, N A AlQahtani, M A Kader, and S T Gokhale.
• Department of Periodontics and Community Dental Sciences, College of Dentistry, King Khalid University, Abha, Saudi Arabia.
• Niger J Clin Pract. 2019 Jan 1; 22 (1): 56-62.

Background/PurposeGingival biotype (GB) is a crucial factor in predicting the success of soft tissue periodontal and peri-implant surgical interventions. Consequently, contemplating noninvasive, less time-consuming procedure to anticipate it has become a part and parcel of the current practice. This article presents a novel algorithm to detect GB in the Saudi population based on the dentopapillary measurements taken on laboratory models. In addition, it targets to allocate a range of values for thick and thin biotypes.Materials And MethodsModel analysis was done on 160 patients to measure eight gingival parameters, and an algorithm was developed according to the results of multiple and linear regression analyses. Applying the dentopapillary parameters to the algorithm revealed a prediction of the biotype. Finally, the resultant values and the exact thickness were reassessed directly in a sample of patients using a modified caliper.ResultsThe regression analysis revealed an algorithm predicting biotypes among patients based on their measured dentopapillary values. Discriminant analysis was used to allocate the values to thin and thick biotypes to further demystify that they coincide with <0.7 mm and >1.5 mm, respectively. However, gingival thickness between 0.7 and 1.5 mm was considered intermediate biotype.ConclusionGB could be predicted based on the dentopapillary measurements taken on laboratory models, which may further reduce the chairside time and increase the success rate of the surgical procedures. Significant variations in the range of values of the thick and thin biotype were detected in the Saudi population compared to other races.Clinical SignificanceThe escalating invasion of interventional procedures in the dental practice necessitates measuring the GB as a predictor of procedure success. This study introduces an algorithm for detecting the GB and updates the range of values for thick and thin biotypes in the Saudi population that would consequently reduce chairside time.

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