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Background <p>Biological width (BW) safeguards the periodontal ligament and alveolar bone. A reduced BW value may lead to periodontal complications. This study aims to develop and validate digital risk prediction algorithms to assess whether BW is low in adults with good oral hygiene.</p> Methods <p>Four thousand four hundred twenty sites of 740 teeth from 55 adults with good oral hygiene were used for model development and validation. A mixed-effects logistic regression model was used to derive a risk prediction equation for low BW, incorporating CBCT, intraoral scanning (IOS), and periodontal probing data. Predictors included tooth type, tooth site, gingival margin (GM)-cementum-enamel junction (CEJ) distance, CEJ-alveolar ridge crest (ARC) distance, and gingival thickness (GT). The outcome measure was the classification of BW as low if it was less than 1.13&#xa0;mm, an operational threshold. Sulcus depth (SD) was measured clinically. GM-CEJ, GT, and D (the distance between GM and ARC) were measured digitally. BW was calculated by subtracting D and SD.</p> Results <p>The raw prediction model is represented by the equation logit(P-raw) = 8.40–2.17*GM-CEJ − 2.25*CEJ-ARC + βSite + βTooth+ (1 | PatientID), where P-raw denotes the probability of low BW. The re-calibration model is logit(P-cal) = 0.1028–0.7159* logit(P-raw). The intra-class correlation coefficient (ICC) was 0.165, indicating that 16.5% of the individual variation was attributable to the patient level. The raw model with an area under the curve (AUC) of 0.867 indicates good discrimination. The calibration curve suggested good internal calibration. The external feasibility test of the model yielded an AUC of 0.68, demonstrating moderate discrimination. The external feasibility test of the recalibration model indicated acceptable calibration. The model’s visual nomogram enables individual site risk calculation for low BW.</p> Conclusion <p>The developed and validated risk prediction models, based on CBCT and IOS data fusion, may assist to estimate low BW. The inclusion of predictors such as GM-CEJ, CEJ-ARC, tooth site, and tooth type assists clinicians in identifying high-risk tooth sites, thereby facilitating detection of periodontal tissue damage. Future research could develop linear regression models to solve limitations, including the definition of the cutoff and the limited external validation.</p>

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Development and validation of risk prediction algorithms based on cone beam computed tomography and intraoral scanning fusion data to estimate whether biological width is at a low value: a cross-sectional study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Ying Yuan,
  • Shuo Yang,
  • Xu Wang,
  • Lisha Di,
  • Wulin He

摘要

Background

Biological width (BW) safeguards the periodontal ligament and alveolar bone. A reduced BW value may lead to periodontal complications. This study aims to develop and validate digital risk prediction algorithms to assess whether BW is low in adults with good oral hygiene.

Methods

Four thousand four hundred twenty sites of 740 teeth from 55 adults with good oral hygiene were used for model development and validation. A mixed-effects logistic regression model was used to derive a risk prediction equation for low BW, incorporating CBCT, intraoral scanning (IOS), and periodontal probing data. Predictors included tooth type, tooth site, gingival margin (GM)-cementum-enamel junction (CEJ) distance, CEJ-alveolar ridge crest (ARC) distance, and gingival thickness (GT). The outcome measure was the classification of BW as low if it was less than 1.13 mm, an operational threshold. Sulcus depth (SD) was measured clinically. GM-CEJ, GT, and D (the distance between GM and ARC) were measured digitally. BW was calculated by subtracting D and SD.

Results

The raw prediction model is represented by the equation logit(P-raw) = 8.40–2.17*GM-CEJ − 2.25*CEJ-ARC + βSite + βTooth+ (1 | PatientID), where P-raw denotes the probability of low BW. The re-calibration model is logit(P-cal) = 0.1028–0.7159* logit(P-raw). The intra-class correlation coefficient (ICC) was 0.165, indicating that 16.5% of the individual variation was attributable to the patient level. The raw model with an area under the curve (AUC) of 0.867 indicates good discrimination. The calibration curve suggested good internal calibration. The external feasibility test of the model yielded an AUC of 0.68, demonstrating moderate discrimination. The external feasibility test of the recalibration model indicated acceptable calibration. The model’s visual nomogram enables individual site risk calculation for low BW.

Conclusion

The developed and validated risk prediction models, based on CBCT and IOS data fusion, may assist to estimate low BW. The inclusion of predictors such as GM-CEJ, CEJ-ARC, tooth site, and tooth type assists clinicians in identifying high-risk tooth sites, thereby facilitating detection of periodontal tissue damage. Future research could develop linear regression models to solve limitations, including the definition of the cutoff and the limited external validation.