OBJECTIVES: This study aimed to develop and test a tool for low bone mass pre-screening by combining periapical radiographs with clinical risk factors. METHODS: The study sample consisted of 60 post-menopausal women over 40 years of age who were referred for dental radiographs. These patients also had their bone mineral density measured at the lumbar spine and proximal femur using dual-energy X-ray absorptiometry. Radiographic density measurements and 14 morphological features were obtained from each dental radiograph using digital image processing software. The clinical variables considered were age and bone mass index. Classification and regression tree analysis (CART) was used to test the predictive power of clinical and radiographic risk factors for classifying individuals. RESULTS: CART indicated that the most important variables for classifying patients were age, number of terminal points/periphery, periphery/trabecular area, radiographic density and bone mass index. CONCLUSION: A combination of clinical and radiographic factors can be used to identify individuals with low bone mineral density, with higher accuracy than any one of these factors taken individually.
OBJECTIVES: This study aimed to develop and test a tool for low bone mass pre-screening by combining periapical radiographs with clinical risk factors. METHODS: The study sample consisted of 60 post-menopausal women over 40 years of age who were referred for dental radiographs. These patients also had their bone mineral density measured at the lumbar spine and proximal femur using dual-energy X-ray absorptiometry. Radiographic density measurements and 14 morphological features were obtained from each dental radiograph using digital image processing software. The clinical variables considered were age and bone mass index. Classification and regression tree analysis (CART) was used to test the predictive power of clinical and radiographic risk factors for classifying individuals. RESULTS:CART indicated that the most important variables for classifying patients were age, number of terminal points/periphery, periphery/trabecular area, radiographic density and bone mass index. CONCLUSION: A combination of clinical and radiographic factors can be used to identify individuals with low bone mineral density, with higher accuracy than any one of these factors taken individually.
Authors: M K Shrout; C F Hildebolt; B J Potter; T K Brunsden; T K Pilgram; M Dotson; N Yokoyama-Crothers; J Hauser; S Cohen; E Kardaris; R Civitelli; P Hanes Journal: J Periodontol Date: 2000-03 Impact factor: 6.993
Authors: D M Black; M Steinbuch; L Palermo; P Dargent-Molina; R Lindsay; M S Hoseyni; O Johnell Journal: Osteoporos Int Date: 2001 Impact factor: 4.507
Authors: L K Koh; W B Sedrine; T P Torralba; A Kung; S Fujiwara; S P Chan; Q R Huang; R Rajatanavin; K S Tsai; H M Park; J Y Reginster Journal: Osteoporos Int Date: 2001 Impact factor: 4.507
Authors: Piet Geusens; Marc C Hochberg; Danny J M van der Voort; Huibert Pols; Marjolein van der Klift; Ethel Siris; Mary E Melton; Jennifer Turpin; Christine Byrnes; Philip Ross Journal: Mayo Clin Proc Date: 2002-07 Impact factor: 7.616
Authors: D de Sá Cavalcante; M G da Silva Castro; A R P Quidute; M R A Martins; A M P L Cid; P G de Barros Silva; J Cadwell Williams; F S Neves; T R Ribeiro; F W G Costa Journal: Osteoporos Int Date: 2019-08-02 Impact factor: 4.507