Estimating patient age on radiographs can be a little burdensome for radiologists and subject to high inter-reader variability. Artificial intelligence (AI) can make this task easier, however, by analysing data from skeletal radiographs, according to research published in the American Journal of Roentgenology.
A research team from Korea trained a deep-learning algorithm that yielded estimates of bone age that correlated significantly with a reference bone age. When used as a second reader in testing that mimicked daily clinical practice, the software also helped two radiologists read the cases an average of 29% faster.
“[Our] automatic software system showed reliably accurate bone age estimations and appeared to enhance efficiency by reducing reading times without compromising the diagnostic accuracy,” wrote the group led by Jeong Rye Kim of Asan Medical Center in Seoul.
A burdensome read
Bone age estimation is a critical task for determining developmental status and predicting ultimate height in paediatric patients, particularly those with growth disorders and endocrine abnormalities. The most popular method for estimating bone age is the Greulich-Pyle method, which involves comparing the patient’s left-hand wrist radiograph with standard radiographs in the Greulich-Pyle atlas.
“However, the process of bone age estimation, which comprises a simple comparison of multiple images, can be repetitive and time-consuming and is thus sometimes burdensome to radiologists,” the authors wrote. “Moreover, the accuracy depends on the radiologist’s experience and tends to be subjective.”
Concerns over inter-reader variability have led to the development of a variety of automatic computerized bone age assessment methods over the past 25 years, including computer-assisted skeletal age scores, computer-aided skeletal maturation assessment systems, and the BoneXpert CAD software (Visiana), according to the…