Skeletal maturity progresses through discrete phases. Pediatrics use this fact to determine the bone age (BAAs), which is compared to chronological age in the evaluation of endocrine, metabolic, and growth disorders. Skeletal age assessment is a common clinical practice to investigate endocrinology, genetics, and growth disorders in children. It is generally performed by radiological examination of the left hand by using either the Greulich and Pyle (G&P) method or the Tanner-Whitehouse (TW) one. The G&P method is the approach used by most radiologists because of its simplicity and speed. It is based on the comparison between the whole X-ray 15 scan and a reference atlas. However, both clinical procedures show several limitations, from the examination effort of radiologists to (most importantly) significant intra- and inter-operator variability. In thisProject, we propose a fully automated deep learning pipeline to segment a region of interest, standardize and pre-process input radiographs, and perform BAA
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