TY - JOUR
T1 - Automating Scoliosis Measurements in Radiographic Studies with Machine Learning
T2 - Comparing Artificial Intelligence and Clinical Reports
AU - Ha, Audrey Y.
AU - Do, Bao H.
AU - Bartret, Adam L.
AU - Fang, Charles X.
AU - Hsiao, Albert
AU - Lutz, Amelie M.
AU - Banerjee, Imon
AU - Riley, Geoffrey M.
AU - Rubin, Daniel L.
AU - Stevens, Kathryn J.
AU - Wang, Erin
AU - Wang, Shannon
AU - Beaulieu, Christopher F.
AU - Hurt, Brian
N1 - Publisher Copyright:
© 2022, The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.
PY - 2022/6
Y1 - 2022/6
N2 - Scoliosis is a condition of abnormal lateral spinal curvature affecting an estimated 2 to 3% of the US population, or seven million people. The Cobb angle is the standard measurement of spinal curvature in scoliosis but is known to have high interobserver and intraobserver variability. Thus, the objective of this study was to build and validate a system for automatic quantitative evaluation of the Cobb angle and to compare AI generated and human reports in the clinical setting. After IRB was obtained, we retrospectively collected 2150 frontal view scoliosis radiographs at a tertiary referral center (January 1, 2019, to January 1, 2021, ≥ 16 years old, no hardware). The dataset was partitioned into 1505 train (70%), 215 validation (10%), and 430 test images (20%). All thoracic and lumbar vertebral bodies were segmented with bounding boxes, generating approximately 36,550 object annotations that were used to train a Faster R-CNN Resnet-101 object detection model. A controller algorithm was written to localize vertebral centroid coordinates and derive the Cobb properties (angle and endplate) of dominant and secondary curves. AI-derived Cobb angle measurements were compared to the clinical report measurements, and the Spearman rank-order demonstrated significant correlation (0.89, p < 0.001). Mean difference between AI and clinical report angle measurements was 7.34° (95% CI: 5.90–8.78°), which is similar to published literature (up to 10°). We demonstrate the feasibility of an AI system to automate measurement of level-by-level spinal angulation with performance comparable to radiologists.
AB - Scoliosis is a condition of abnormal lateral spinal curvature affecting an estimated 2 to 3% of the US population, or seven million people. The Cobb angle is the standard measurement of spinal curvature in scoliosis but is known to have high interobserver and intraobserver variability. Thus, the objective of this study was to build and validate a system for automatic quantitative evaluation of the Cobb angle and to compare AI generated and human reports in the clinical setting. After IRB was obtained, we retrospectively collected 2150 frontal view scoliosis radiographs at a tertiary referral center (January 1, 2019, to January 1, 2021, ≥ 16 years old, no hardware). The dataset was partitioned into 1505 train (70%), 215 validation (10%), and 430 test images (20%). All thoracic and lumbar vertebral bodies were segmented with bounding boxes, generating approximately 36,550 object annotations that were used to train a Faster R-CNN Resnet-101 object detection model. A controller algorithm was written to localize vertebral centroid coordinates and derive the Cobb properties (angle and endplate) of dominant and secondary curves. AI-derived Cobb angle measurements were compared to the clinical report measurements, and the Spearman rank-order demonstrated significant correlation (0.89, p < 0.001). Mean difference between AI and clinical report angle measurements was 7.34° (95% CI: 5.90–8.78°), which is similar to published literature (up to 10°). We demonstrate the feasibility of an AI system to automate measurement of level-by-level spinal angulation with performance comparable to radiologists.
KW - Artificial intelligence
KW - Cobb angle
KW - Convolutional neural network
KW - Deep learning
KW - Scoliosis
KW - Spine
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U2 - 10.1007/s10278-022-00595-x
DO - 10.1007/s10278-022-00595-x
M3 - Article
C2 - 35149938
AN - SCOPUS:85124590270
SN - 0897-1889
VL - 35
SP - 524
EP - 533
JO - Journal of Digital Imaging
JF - Journal of Digital Imaging
IS - 3
ER -