Open AccessAutomatic method for vertebral morphometry measurements
Author(s) -
Franchini Roberto,
Conversano Francesco,
Pisani Paola,
Casciaro Ernesto,
Peccarisi Marco,
Quarta Eugenio,
Grimaldi Antonella,
Muratore Maurizio,
Casciaro Sergio
Publication year - 2016
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2015.0172
Subject(s) - computer science , artificial intelligence
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.