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Updated: Nov 21, 2025
Cell-based Assay Protocol for the Prognostic Prediction of Idiopathic Scoliosis Using Cellular Dielectric Spectroscopy
Published on: October 16, 2013
Classification of neurofibromatosis-related dystrophic or nondystrophic scoliosis based on image features using
Zhong He1, Yimu Wang2, Xiaodong Qin1
1Department of Spine Surgery, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.
Purpose:
We developed a system that can automatically classify cases of scoliosis secondary to neurofibromatosis type 1 (NF1-S) using deep learning algorithms (DLAs) and improve the accuracy and effectiveness of classification, thereby assisting surgeons with the auxiliary diagnosis.
Methods:
Comprehensive experiments in NF1 classification were performed based on a dataset consisting 211 NF1-S (131 dystrophic and 80 nondystrophic NF1-S) patients. Additionally, 100 congenital scoliosis (CS), 100 adolescent idiopathic scoliosis (AIS) patients, and 114 normal controls were used for experiments in primary classification. For identification of NF1-S with nondystrophic or dystrophic curves, we devised a novel network (i.e., Bilateral convolutional neural network [CNN]) utilizing a bilinear-like operation to discover the similar interest features between whole spine AP and lateral x-ray images. The performance of Bilateral CNN was compared with spine surgeons, conventional DLAs (i.e., VGG-16, ResNet-50, and Bilinear CNN [BCNN]), recently proposed DLAs (i.e., ShuffleNet, MobileNet, and EfficientNet), and Two-path BCNN which was the extension of BCNN using AP and lateral x-ray images as inputs.
Results:
In NF1 classification, our proposed Bilateral CNN with 80.36% accuracy outperformed the other seven DLAs ranging from 61.90% to 76.19% with fivefold cross-validation. It also outperformed the spine surgeons (with an average accuracy of 77.5% for the senior surgeons and 65.0% for the junior surgeons). Our method is highly generalizable due to the proposed methodology and data augmentation. Furthermore, the heatmaps extracted by Bilateral CNN showed curve pattern and morphology of ribs and vertebrae contributing most to the classification results. In primary classification, our proposed method with an accuracy of 87.92% also outperformed all the other methods with varied accuracies between 52.58% and 83.35% with fivefold cross-validation.
Conclusions:
The proposed Bilateral CNN can automatically capture representative features for classifying NF1-S utilizing AP and lateral x-ray images, leading to a relatively good performance. Moreover, the proposed method can identify other spine deformities for auxiliary diagnosis.
