Development and validation of a keypoint region-based convolutional neural network to automate thoracic Cobb angle measurements using whole-spine standing radiographs.

purposeAdolescent idiopathic scoliosis (AIS) affects a significant portion of the adolescent population, leading to severe spinal deformities if untreated. Diagnosis, surgical planning, and assessment of outcomes are determined primarily by the Cobb angle on anteroposterior spinal radiographs. Screening for scoliosis enables early interventions and improved outcomes. However, screenings are often conducted through school entities where a trained radiologist may not be available to accurately interpret the imaging results.

methodsIn this study, we developed an artificial intelligence tool utilizing a keypoint region-based convolutional neural network (KR-CNN) for automated thoracic Cobb angle (TCA) measurement. The KR-CNN was trained on 609 whole-spine radiographs of AIS patients and validated using our institutional AIS registry, which included 83 patients who underwent posterior spinal fusion with both preoperative and postoperative anteroposterior X-ray images.

resultsThe KR-CNN model demonstrated superior performance metrics, including a mean absolute error (MAE) of 2.22, mean squared error (MSE) of 9.1, symmetric mean absolute percentage error (SMAPE) of 4.29, and intraclass correlation coefficient (ICC) of 0.98, outperforming existing methods.

conclusionThis method will enable fast and accurate screening for AIS and assessment of postoperative outcomes and provides a development framework for further automation and validation of spinopelvic measurements.