Laura Ferreira Pinheiro Nicolielo1, Jeroen Van Dessel1, G Harry van Lenthe2, Ivo Lambrichts3, Reinhilde Jacobs1,4. 1. 1 Deparment Imaging & Pathology, OMFS-IMPATH research group, KU Leuven and Oral and Maxillofacial Surgery, University Hospitals Leuven , Leuven , Belgium. 2. 2 Department of Mechanical Engineering, Biomechanics Section , Leuven , Belgium. 3. 3 Morphology Group, Biomedical Research Institute, Hasselt University , Diepenbeek , Belgium. 4. 4 Department of Dental Medicine, Karolinska Institutet , Huddinge , Sweden.
Abstract
OBJECTIVE: : To develop and validate an automated classification method that determines the trabecular bone pattern at implant site based on three-dimensional bone morphometric parameters derived from CBCT images. METHODS: : 25 human cadaver mandibles were scanned using CBCT clinical scanning protocol. Volumes-of-interest comprising only the trabecular bone of the posterior regions were selected and segmented for three-dimensional morphometric parameters calculation. Three experts rated all bone regions into one of the three trabecular pattern classes (sparse, intermediate and dense) to generate a reference classification. Morphometric parameters were used to automatically classify the trabecular pattern with linear discriminant analysis statistical model. The discriminatory power of each morphometric parameter for automatic classification was indicated and the accuracy compared to the reference classification. Repeated-measures analysis of variances were used to statistically compare morphometric indices between the three classes. Finally, the outcome of the automatic classification was evaluated against a subjective classification performed independently by four different observers. RESULTS: : The overall correct classification was 83% for quantity-, 86% for structure-related parameters and 84% for the parameters combined. Cross-validation showed a 79% model prediction accuracy. Bone volume fraction (BV/TV) had the most discriminatory power in the automatic classification. Trabecular bone patterns could be distinguished based on most morphometric parameters, except for trabecular thickness (Tb.Th) and degree of anisotropy (DA). The interobserver agreement between the subjective observers was fair (0.25), while the test-retest agreement was moderate (0.46). In comparison with the reference standard, the overall agreement was moderate (0.44). CONCLUSION: : Automatic classification performed better than subjective classification with a prediction model comprising structure- and quantity-related morphometric parameters. ADVANCES IN KNOWLEDGE:: Computer-aided trabecular bone pattern assessment based on morphometric parameters could assist objectivity in clinical bone quality classification.
OBJECTIVE: : To develop and validate an automated classification method that determines the trabecular bone pattern at implant site based on three-dimensional bone morphometric parameters derived from CBCT images. METHODS: : 25 human cadaver mandibles were scanned using CBCT clinical scanning protocol. Volumes-of-interest comprising only the trabecular bone of the posterior regions were selected and segmented for three-dimensional morphometric parameters calculation. Three experts rated all bone regions into one of the three trabecular pattern classes (sparse, intermediate and dense) to generate a reference classification. Morphometric parameters were used to automatically classify the trabecular pattern with linear discriminant analysis statistical model. The discriminatory power of each morphometric parameter for automatic classification was indicated and the accuracy compared to the reference classification. Repeated-measures analysis of variances were used to statistically compare morphometric indices between the three classes. Finally, the outcome of the automatic classification was evaluated against a subjective classification performed independently by four different observers. RESULTS: : The overall correct classification was 83% for quantity-, 86% for structure-related parameters and 84% for the parameters combined. Cross-validation showed a 79% model prediction accuracy. Bone volume fraction (BV/TV) had the most discriminatory power in the automatic classification. Trabecular bone patterns could be distinguished based on most morphometric parameters, except for trabecular thickness (Tb.Th) and degree of anisotropy (DA). The interobserver agreement between the subjective observers was fair (0.25), while the test-retest agreement was moderate (0.46). In comparison with the reference standard, the overall agreement was moderate (0.44). CONCLUSION: : Automatic classification performed better than subjective classification with a prediction model comprising structure- and quantity-related morphometric parameters. ADVANCES IN KNOWLEDGE:: Computer-aided trabecular bone pattern assessment based on morphometric parameters could assist objectivity in clinical bone quality classification.
Authors: Mariana E Kersh; Philippe K Zysset; Dieter H Pahr; Uwe Wolfram; David Larsson; Marcus G Pandy Journal: J Biomech Date: 2013-08-09 Impact factor: 2.712
Authors: Jeroen Van Dessel; Laura Ferreira Pinheiro Nicolielo; Yan Huang; Walter Coudyzer; Benjamin Salmon; Ivo Lambrichts; Reinhilde Jacobs Journal: Eur J Oral Implantol Date: 2017 Impact factor: 3.123
Authors: David W Dempster; Juliet E Compston; Marc K Drezner; Francis H Glorieux; John A Kanis; Hartmut Malluche; Pierre J Meunier; Susan M Ott; Robert R Recker; A Michael Parfitt Journal: J Bone Miner Res Date: 2013-01 Impact factor: 6.741