ArticleScientific reports2022
Development of artificial intelligence for automated measurement of cervical lordosis on lateral radiographs.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Feasibility of Drill-Tip Position Estimation During Cortical Bone Drilling Using Force and Torque Signals.Sensors (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Spine Neuroimaging: Diagnostic and Prognostic Utility of Novel Biomarkers in Lower Back Pain.Journal of clinical medicine · 2026Review
- Artificial intelligence-driven data expansion for the validation of spinopelvic parameter correlations in asymptomatic subjects.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Comparison of multistage and single-stage framework for automated landmark localization and radiologic measurement: a case of the C1-2 complex on cervical spine lateral radiographs.BMC medical imaging · 2026Article
- Acoustic cues into a surgeon-assist physical AI for detecting bone penetration during spinal surgery.Scientific reports · 2026Article
- Hierarchical deep learning pipeline for robust cervical parameter measurement in radiographs with C7 obscuration.NPJ digital medicine · 2026Article
- Cross-validation of two independent methods to analyze the sequence of segmental contributions in the cervical spine in extension cineradiographic recordings.BMC musculoskeletal disorders · 2025Article
- Current Applications and Future Directions of Technologies Used in Adult Deformity Surgery for Personalized Alignment: A Narrative Review.Journal of personalized medicine · 2025Review
- Research on a deep learning-based model for measurement of X-ray imaging parameters of atlantoaxial joint.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images.Sensors (Basel, Switzerland) · 2024Article
- AI-Driven Segmentation and Automated Analysis of the Whole Sagittal Spine from X-ray Images for Spinopelvic Parameter Evaluation.Bioengineering (Basel, Switzerland) · 2023Article
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Authors and funding
11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Cervical sagittal alignment is an essential parameter for the evaluation of spine disorders. Manual measurement is time-consuming and burdensome to measurers. Artificial intelligence (AI) in the form of convolutional neural networks has begun to be used to measure x-rays. This study aimed to develop AI for automated measurement of lordosis on lateral cervical x-rays. We included 4546 cervical x-rays from 1674 patients. For all x-rays, the caudal endplates of C2 and C7 were labeled based on consensus among well-experienced spine surgeons, the data for which were used as ground truth. This ground truth was split into training data and test data, and the AI model learned the training data. The absolute error of the AI measurements relative to the ground truth for 4546 x-rays was determined by fivefold cross-validation. Additionally, the absolute error of AI measurements was compared with the error of other 2 surgeons' measurements on 415 radiographs of 168 randomly selected patients. In fivefold cross-validation, the absolute error of the AI model was 3.3° in the average and 2.2° in the median. For comparison of other surgeons, the mean absolute error for measurement of 168 patients was 3.1° ± 3.4° for the AI model, 3.9° ± 3.4° for Surgeon 1, and 3.8° ± 4.7° for Surgeon 2. The AI model had a significantly smaller error than Surgeon 1 and Surgeon 2 (P = 0.002 and 0.036). This algorithm is available at ( https://ykszk.github.io/c2c7demo/ ). The AI model measured cervical spine alignment with better accuracy than surgeons. AI can assist in routine medical care and can be helpful in research that measures large numbers of images. However, because of the large errors in rare cases such as highly deformed ones, AI may, in principle, be limited to assisting humans.
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