An artificial intelligence powered platform for auto-analyses of spine alignment irrespective of image quality with prospective validation. (January 2022)

• Record Type: Journal Article
• Authors: Meng, Nan; Cheung, Jason P.Y.; Wong, Kwan-Yee K.; Dokos, Socrates; Li, Sofia; Choy, Richard W.; To, Samuel; Li, Ricardo J.; Zhang, Teng
• Is Part Of: EClinicalMedicine. Volume 43(2022)
• Publication Date: 2022-01
• Subjects: Adolescent idiopathic scoliosis -- Spine malalignment -- Artificial intelligence -- Deep learning -- Open platform; Medicine -- Research -- Periodicals; Medical policy -- Periodicals; Clinical Medicine; Health Policy; Public Health; Medical policy; Medicine -- Research; Periodical; Electronic journals; Periodicals; 613
• DOI: 10.1016/j.eclinm.2021.101252
• Languages: English
• ISSNs: 2589-5370

Abstract

Summary: Background: Assessment of spine alignment is crucial in the management of scoliosis, but current auto-analysis of spine alignment suffers from low accuracy. We aim to develop and validate a hybrid model named SpineHRNet+, which integrates artificial intelligence (AI) and rule-based methods to improve auto-alignment reliability and interpretability. Methods: From December 2019 to November 2020, 1, 542 consecutive patients with scoliosis attending two local scoliosis clinics (The Duchess of Kent Children's Hospital at Sandy Bay in Hong Kong; Queen Mary Hospital in Pok Fu Lam on Hong Kong Island) were recruited. The biplanar radiographs of each patient were collected with our medical machine EOS™. The collected radiographs were recaptured using smartphones or screenshots, with deidentified images securely stored. Manually labelled landmarks and alignment parameters by a spine surgeon were considered as ground truth (GT). The data were split 8:2 to train and internally test SpineHRNet+, respectively. This was followed by a prospective validation on another 337 patients. Quantitative analyses of landmark predictions were conducted, and reliabilities of auto-alignment were assessed using linear regression and Bland-Altman plots. Deformity severity and sagittal abnormality classifications were evaluated by confusion matrices. Findings: SpineHRNet+ achieved accurate landmark detection with mean Euclidean distance errors of 2·78 and 5·52 pixels on posteroanterior and lateral radiographs, respectively. The mean angle errors between predictions and GT were 3·18° and 6·32° coronally and sagittally. All predicted alignments were strongly correlated with GT ( p < 0·001, R 2 > 0·97), with minimal overall difference visualised via Bland-Altman plots. For curve detections, 95·7% sensitivity and 88·1% specificity was achieved, and for severity classification, 88·6–90·8% sensitivity was obtained. For sagittal abnormalities, greater than 85·2–88·9% specificity and sensitivity were achieved. Interpretation: The auto-analysis provided by SpineHRNet+ was reliable and continuous and it might offer the potential to assist clinical work and facilitate large-scale clinical studies. Funding: RGC Research Impact Fund (R5017–18F), Innovation and Technology Fund (ITS/404/18), and the AOSpine East Asia Fund (AOSEA(R)2019–06).