STUDY DESIGN: Image analysis model development. OBJECTIVE: The objective of this study was to develop a novel clinical workflow tool that uses model-based shape recognition technology to allow efficient, semiautomated detailed annotation of each vertebra between T4 and L4 on plain lateral radiographs. SUMMARY OF BACKGROUND DATA: Identification of prevalent vertebral fractures, especially when not symptomatic, has been problematic despite their importance. There is a recognized need to increase the opportunities to detect vertebral fractures so that clinically beneficial therapeutic interventions can be initiated. METHODS: Radiographs obtained from 165 subjects in the Canadian Multicenter Osteoporosis Study (CaMos) were used to construct a vertebral shape model of the vertebral column from T4 to L4 using a statistical learning technique, as well as to estimate the accuracy and precision of this automated software tool for vertebral shape analysis. Radiographs showing scoliosis greater than 15 degrees were excluded. RESULTS: Vertebral contours defined by 95 points per vertebra, represented by 79,895 points in total, were assessed on 841 individual vertebrae. The mean absolute accuracy error calculated over each vertebra in each test image was 1.06 +/- 1.2 mm. This value corresponded to an average 3.4% of vertebral height. The mean precision error, reflecting interobserver variability, per vertebra of the resulting annotations was 0.61 +/- 0.73 mm. This value corresponded to an average 2.3% of vertebral height. Accuracy and precision error estimates did not differ notably by vertebral level. CONCLUSION: The results of the current study indicate that statistical modeling can provide a robust tool for the accurate and precise semiautomated annotation of vertebral body shape from T4 to L4 in patients who do not have scoliosis greater than 15 degrees . This method may prove useful as a clinical workflow tool to aid the physician in vertebral fracture assessment and might contribute to decision-making about pharmacologic treatment of osteoporosis.
STUDY DESIGN: Image analysis model development. OBJECTIVE: The objective of this study was to develop a novel clinical workflow tool that uses model-based shape recognition technology to allow efficient, semiautomated detailed annotation of each vertebra between T4 and L4 on plain lateral radiographs. SUMMARY OF BACKGROUND DATA: Identification of prevalent vertebral fractures, especially when not symptomatic, has been problematic despite their importance. There is a recognized need to increase the opportunities to detect vertebral fractures so that clinically beneficial therapeutic interventions can be initiated. METHODS: Radiographs obtained from 165 subjects in the Canadian Multicenter Osteoporosis Study (CaMos) were used to construct a vertebral shape model of the vertebral column from T4 to L4 using a statistical learning technique, as well as to estimate the accuracy and precision of this automated software tool for vertebral shape analysis. Radiographs showing scoliosis greater than 15 degrees were excluded. RESULTS: Vertebral contours defined by 95 points per vertebra, represented by 79,895 points in total, were assessed on 841 individual vertebrae. The mean absolute accuracy error calculated over each vertebra in each test image was 1.06 +/- 1.2 mm. This value corresponded to an average 3.4% of vertebral height. The mean precision error, reflecting interobserver variability, per vertebra of the resulting annotations was 0.61 +/- 0.73 mm. This value corresponded to an average 2.3% of vertebral height. Accuracy and precision error estimates did not differ notably by vertebral level. CONCLUSION: The results of the current study indicate that statistical modeling can provide a robust tool for the accurate and precise semiautomated annotation of vertebral body shape from T4 to L4 in patients who do not have scoliosis greater than 15 degrees . This method may prove useful as a clinical workflow tool to aid the physician in vertebral fracture assessment and might contribute to decision-making about pharmacologic treatment of osteoporosis.
Authors: Y M Kim; S Demissie; H K Genant; X Cheng; W Yu; E J Samelson; D P Kiel; M L Bouxsein Journal: Osteoporos Int Date: 2011-09-17 Impact factor: 4.507
Authors: Ling Oei; Fernando Rivadeneira; Felisia Ly; Stephan J Breda; M Carola Zillikens; Albert Hofman; André G Uitterlinden; Gabriel P Krestin; Edwin H G Oei Journal: Eur Radiol Date: 2012-08-15 Impact factor: 5.315
Authors: Brett T Allaire; M Clara DePaolis Kaluza; Alexander G Bruno; Elizabeth J Samelson; Douglas P Kiel; Dennis E Anderson; Mary L Bouxsein Journal: Eur Spine J Date: 2016-10-18 Impact factor: 3.134
Authors: Ling Oei; Felisia Ly; Salih El Saddy; Ater A Makurthou; Albert Hofman; Frank J A van Rooij; André G Uitterlinden; M Carola Zillikens; Fernando Rivadeneira; Edwin H G Oei Journal: Quant Imaging Med Surg Date: 2013-10
Authors: M J van Dort; E A P M Romme; F W J M Smeenk; P P P M Geusens; E F M Wouters; J P van den Bergh Journal: Osteoporos Int Date: 2018-02-12 Impact factor: 4.507
Authors: M J van Dort; J H M Driessen; P Geusens; E A P M Romme; F W J M Smeenk; E F M Wouters; J P W van den Bergh Journal: Osteoporos Int Date: 2019-06-03 Impact factor: 4.507