ArticleNPJ digital medicine2026
Hierarchical deep learning pipeline for robust cervical parameter measurement in radiographs with C7 obscuration.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
5 authors.
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Abstract
We developed and externally validated a hierarchical deep learning pipeline that automates cervical sagittal measurements, explicitly addressing C7 obscuration on lateral radiographs. The model combines a global keypoint detector with C2/C7 specialists localized via a multilayer perceptron to refine landmarks on high‑resolution patches. Trained on 5604 images and tested internally and on a challenging external cohort enriched for C7 obscuration (82%), it achieved excellent agreement with ground truth. Externally, intraclass correlation coefficients (ICCs) were 0.97 for lordosis (mean absolute error [MAE] 2.6°), >0.99 for C2 slope (MAE 0.8°), and 0.93 for C7 slope (MAE 2.3°), with minimal bias and narrower limits of agreement than a single‑stage baseline. The model showed near-perfect repeatability (ICC > 0.99) and higher artificial intelligence-expert agreement (ICC 0.81-0.84) for C7 slope than inter-expert reliability (ICC 0.67). In failure cases, the pipeline corrected large global model errors (e.g., 10.22°- 0.22°). This robust, coarse‑to‑fine approach advances reliable, generalizable cervical alignment assessment in real‑world conditions.
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