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Purpose <p>This study aimed to develop a multimodal model that integrates morphological and functional features to address pulmonary function decline associated with three-dimensional structural changes in Adolescent Idiopathic Scoliosis (AIS). The proposed model predicts pulmonary dysfunction by combining clinical indicators with X-rays, which directly depict the morphological deformities characteristic of AIS.</p> Methods <p>This study included 178 patients with AIS who underwent standing posteroanterior radiography and pulmonary function testing as part of preoperative evaluation. A dual-stream deep learning model was developed, employing an EfficientNet backbone to extract features from radiographs and a multilayer perceptron (MLP) for structured clinical data. These inputs were integrated using Feature-wise Linear Modulation (FiLM) within a multi-task learning framework to jointly predict binary abnormalities (&lt; 80% of the predicted value) in FVC and FEV1. Model performance was evaluated using AUC through five-fold cross-validation and benchmarked against conventional machine learning models trained exclusively on tabular data.</p> Results <p>The optimal multimodal configuration (EfficientNet-B0 combined with MLP using FiLM fusion) achieved AUCs of 0.814 ± 0.031 for FVC and 0.841 ± 0.033 for FEV1, yielding a macro-AUC of 0.827 ± 0.022. This configuration outperformed logistic regression models trained on tabular features (0.719 ± 0.051 for FVC and 0.710 ± 0.057 for FEV1).</p> Conclusion <p>The multimodal deep learning model that integrates holistic radiographic analysis with clinical data demonstrated superior accuracy in predicting pulmonary dysfunction in patients with AIS compared with conventional methods. This data-driven approach shows promise for improving clinical risk assessment and supporting personalized decision-making.</p>

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Spinal morphology–based multimodal AI for predicting pulmonary dysfunction in adolescent idiopathic scoliosis

  • 期刊论文

  • 发表日期：

    2026年3月27日

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  • Yunseo Kim,
  • Se-Jun Park,
  • Jin-Sung Park,
  • Yong-Gon Seo,
  • Sungyoon Choi,
  • Myung Jin Chung,
  • Hakje Yoo,
  • Dong-Ho Kang

摘要

Purpose

This study aimed to develop a multimodal model that integrates morphological and functional features to address pulmonary function decline associated with three-dimensional structural changes in Adolescent Idiopathic Scoliosis (AIS). The proposed model predicts pulmonary dysfunction by combining clinical indicators with X-rays, which directly depict the morphological deformities characteristic of AIS.

Methods

This study included 178 patients with AIS who underwent standing posteroanterior radiography and pulmonary function testing as part of preoperative evaluation. A dual-stream deep learning model was developed, employing an EfficientNet backbone to extract features from radiographs and a multilayer perceptron (MLP) for structured clinical data. These inputs were integrated using Feature-wise Linear Modulation (FiLM) within a multi-task learning framework to jointly predict binary abnormalities (< 80% of the predicted value) in FVC and FEV1. Model performance was evaluated using AUC through five-fold cross-validation and benchmarked against conventional machine learning models trained exclusively on tabular data.

Results

The optimal multimodal configuration (EfficientNet-B0 combined with MLP using FiLM fusion) achieved AUCs of 0.814 ± 0.031 for FVC and 0.841 ± 0.033 for FEV1, yielding a macro-AUC of 0.827 ± 0.022. This configuration outperformed logistic regression models trained on tabular features (0.719 ± 0.051 for FVC and 0.710 ± 0.057 for FEV1).

Conclusion

The multimodal deep learning model that integrates holistic radiographic analysis with clinical data demonstrated superior accuracy in predicting pulmonary dysfunction in patients with AIS compared with conventional methods. This data-driven approach shows promise for improving clinical risk assessment and supporting personalized decision-making.