title: Fully Automated Deep Learning System for Bone Age Assessment.
abstract: Skeletal maturity progresses through discrete phases, a fact that is used routinely in pediatrics where bone age assessments (BAAs) are compared to chronological age in the evaluation of endocrine and metabolic disorders. While central to many disease evaluations, little has changed to improve the tedious process since its introduction in 1950. In this study, we propose a fully automated deep learning pipeline to segment a region of interest, standardize and preprocess input radiographs, and perform BAA. Our models use an ImageNet pretrained, fine-tuned convolutional neural network (CNN) to achieve 57.32 and 61.40% accuracies for the female and male cohorts on our held-out test images. Female test radiographs were assigned a BAA within 1 year 90.39% and within 2 years 98.11% of the time. Male test radiographs were assigned 94.18% within 1 year and 99.00% within 2 years. Using the input occlusion method, attention maps were created which reveal what features the trained model uses to perform BAA. These correspond to what human experts look at when manually performing BAA. Finally, the fully automated BAA system was deployed in the clinical environment as a decision supporting system for more accurate and efficient BAAs at much faster interpretation time (<2 s) than the conventional method.
authors.affiliation: Massachusetts General Hospital and Harvard Medical School, Radiology, 25 New Chardon Street, Suite 400B, Boston, MA, 02114, USA.
authors.forename: Hyunkwang
authors.initials: H
authors.lastname: Lee
authors.affiliation: Massachusetts General Hospital and Harvard Medical School, Radiology, 25 New Chardon Street, Suite 400B, Boston, MA, 02114, USA.
authors.forename: Shahein
authors.initials: S
authors.lastname: Tajmir
authors.affiliation: Massachusetts General Hospital and Harvard Medical School, Radiology, 25 New Chardon Street, Suite 400B, Boston, MA, 02114, USA.
authors.forename: Jenny
authors.initials: J
authors.lastname: Lee
authors.affiliation: Massachusetts General Hospital and Harvard Medical School, Radiology, 25 New Chardon Street, Suite 400B, Boston, MA, 02114, USA.
authors.forename: Maurice
authors.initials: M
authors.lastname: Zissen
authors.affiliation: Massachusetts General Hospital and Harvard Medical School, Radiology, 25 New Chardon Street, Suite 400B, Boston, MA, 02114, USA.
authors.forename: Bethel Ayele
authors.initials: BA
authors.lastname: Yeshiwas
authors.affiliation: Massachusetts General Hospital and Harvard Medical School, Radiology, 25 New Chardon Street, Suite 400B, Boston, MA, 02114, USA.
authors.forename: Tarik K
authors.initials: TK
authors.lastname: Alkasab
authors.affiliation: Massachusetts General Hospital and Harvard Medical School, Radiology, 25 New Chardon Street, Suite 400B, Boston, MA, 02114, USA.
authors.forename: Garry
authors.initials: G
authors.lastname: Choy
authors.affiliation: Massachusetts General Hospital and Harvard Medical School, Radiology, 25 New Chardon Street, Suite 400B, Boston, MA, 02114, USA. sdo@mgh.harvard.edu.
authors.forename: Synho
authors.initials: S
authors.lastname: Do
mesh_terms: D000293:Adolescent; D000328:Adult; D000365:Age Determination by Skeleton / Q000379:methods*; D002648:Child; D020000:Decision Support Systems, Clinical; D005260:Female; D006225:Hand / Q000000981:diagnostic imaging; D006801:Humans; D000069550:Machine Learning; D008297:Male; D016571:Neural Networks, Computer; D012984:Software
keywords: Artificial intelligence; Artificial neural networks (ANNs); Automated measurement; Automated object detection; Bone-age; Classification; Clinical workflow; Computer vision; Computer-aided diagnosis (CAD); Data collection; Decision support; Digital X-ray radiogrammetry; Efficiency; Machine learning; Structured reporting