Conditional generative adversarial network-assisted system for radiation-free evaluation of scoliosis using a single smartphone photograph: a model development and validation study
Bibliographic record
Abstract
Background: Adolescent idiopathic scoliosis (AIS) is the most common spinal disorder in children, characterized by insidious onset and rapid progression, which can lead to severe consequences if not detected in a timely manner. Currently, the diagnosis of AIS primarily relies on X-ray imaging. However, due to limitations in healthcare access and concerns over radiation exposure, this diagnostic method cannot be widely adopted. Therefore, we have developed and validated a screening system using deep learning technology, capable of generating virtual X-ray images (VXI) from two-dimensional Red Green Blue (2D-RGB) images captured by a smartphone or camera to assist spine surgeons in the rapid, accurate, and non-invasive assessment of AIS. Methods: We included 2397 patients with AIS and 48 potential patients with AIS who visited four medical institutions in mainland China from June 11th 2014 to November 28th 2023. Participants data included standing full-spine X-ray images captured by radiology technicians and 2D-RGB images taken by spine surgeons using a camera. We developed a deep learning model based on conditional generative adversarial networks (cGAN) called Swin-pix2pix to generate VXI on retrospective training (n = 1842) and validation (n = 100) dataset, then validated the performance of VXI in quantifying the curve type and severity of AIS on retrospective internal (n = 100), external (n = 135), and prospective test datasets (n = 268). The prospective test dataset included 268 participants treated in Nanjing, China, from April 19th, 2023, to November 28th, 2023, comprising 220 patients with AIS and 48 potential patients with AIS. Their data underwent strict quality control to ensure optimal data quality and consistency. Findings: Our Swin-pix2pix model generated realistic VXI, with the mean absolute error (MAE) for predicting the main and secondary Cobb angles of AIS significantly lower than other baseline cGAN models, at 3.2° and 3.1° on prospective test dataset. The diagnostic accuracy for scoliosis severity grading exceeded that of two spine surgery experts, with accuracy of 0.93 (95% CI [0.91, 0.95]) in main curve and 0.89 (95% CI [0.87, 0.91]) in secondary curve. For main curve position and curve classification, the predictive accuracy of the Swin-pix2pix model also surpassed that of the baseline cGAN models, with accuracy of 0.93 (95% CI [0.90, 0.95]) for thoracic curve and 0.97 (95% CI [0.96, 0.98]), achieving satisfactory results on three external datasets as well. Interpretation: Our developed Swin-pix2pix model holds promise for using a single photo taken with a smartphone or camera to rapidly assess AIS curve type and severity without radiation, enabling large-scale screening. However, limited data quality and quantity, a homogeneous participant population, and rotational errors during imaging may affect the applicability and accuracy of the system, requiring further improvement in the future. Funding: National Key R&D Program of China, Natural Science Foundation of Jiangsu Province, China Postdoctoral Science Foundation, Nanjing Medical Science and Technology Development Foundation, Jiangsu Provincial Key Research and Development Program, and Jiangsu Provincial Medical Innovation Centre of Orthopedic Surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".