Automatic method for vertebral morphometry measurements
| Autor: | Francesco Conversano, Antonella Grimaldi, Sergio Casciaro, Maurizio Muratore, E. Quarta, Paola Pisani, Marco Peccarisi, Ernesto Casciaro, R. Franchini |
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| Rok vydání: | 2016 |
| Předmět: |
Mean squared error
Correlation coefficient business.industry Radiography Atomic and Molecular Physics and Optics Pearson product-moment correlation coefficient 030218 nuclear medicine & medical imaging Vertebra 03 medical and health sciences Vertebral morphometry symbols.namesake 0302 clinical medicine medicine.anatomical_structure medicine symbols Electrical and Electronic Engineering Medical diagnosis Bland–Altman plot business Nuclear medicine 030217 neurology & neurosurgery Mathematics |
| Zdroj: | IET Science, Measurement & Technology. 10:327-334 |
| ISSN: | 1751-8830 |
| DOI: | 10.1049/iet-smt.2015.0172 |
| Popis: | Aim of this study was to perform a detailed clinical validation of a novel fully automatic method for vertebral morphometry. About 80 spine lateral radiographs were evaluated both automatically, by the proposed algorithm, and manually, by an experienced radiologist. The following metrics were used for algorithm performance assessment: sensitivity and specificity in vertebra detection; errors in the localisation of characteristic points of vertebral border; errors in the measurement of six diagnostic parameters; level of agreement and correlation between manual and automatic morphometric measurements; overall accuracy of automatic diagnoses with respect to manual ones. Obtained results showed a very good performance in vertebra detection (sensitivity = 89.1% and specificity = 100.0%). Average errors in the localisation of vertebral characteristic points were always smaller than 3 mm (range 0.85–2.79 mm), causing relative errors in diagnostic parameter values ranging from −5.01 to +6.10%. Bland–Altman analysis documented a mean error in automatic measurements of diagnostic ratios of 0.01 ± 0.18 (bias ± 2 SDs), while Pearson's correlation coefficient resulted r = 0.71 (p < 0.001). Finally, an optimal diagnostic coincidence (92.8%) was found between automatic and manual diagnoses. Therefore, the adopted method has a potential for an effective employment in clinical routine for reliable diagnosis of vertebral fractures. |
| Databáze: | OpenAIRE |
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