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Deep-learning framework for osteoporosis screening on low-dose X-rays: Addressing image quality variability and

Hiroki Katagiri1, Gaku Koyano1, Junya Katayanagi1

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Summary

This study validated the AIXA Osteo artificial intelligence (AI) model for osteoporosis screening in Japan. Image preprocessing and a feature fusion module significantly improved diagnostic accuracy and T-score prediction in diverse clinical settings.

Keywords:
Deep learningExternal validationImage preprocessingLow-dose X-rayOsteoporosisReference database

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Osteoporosis Diagnosis

Background:

  • The AIXA Osteo model (X1AI-Osteo) showed promise for osteoporosis evaluation and T-score prediction in Taiwan.
  • Its performance in different clinical settings and with varied image data remains unverified.

Purpose of the Study:

  • To externally validate the AIXA Osteo model using real-world Japanese data.
  • To address image quality issues and database discrepancies for cross-domain AI application in osteoporosis screening.

Main Methods:

  • Employed a structure-preserving CycleGAN and inpainting model to enhance radiograph quality and restore missing areas.
  • Utilized a feature fusion module to reconcile T-scores between NHANES III and Asian reference standards.
  • Externally validated the model on a dataset of 300 participants from a Japanese clinical environment.

Main Results:

  • Preprocessing significantly improved the model's area under the curve (92.9% to 97.2%), sensitivity (88.6% to 95.7%), and positive predictive value (80.5% to 90.5%).
  • The consistency correlation coefficient for T-score prediction between the model and dual-energy X-ray absorptiometry improved from 0.956 to 0.994.
  • The enhancements demonstrated robust performance in a heterogeneous clinical environment.

Conclusions:

  • The proposed image preprocessing and feature fusion framework enhance the reliability of AI-driven osteoporosis screening using X-rays.
  • This approach facilitates the application of AI for osteoporosis detection across diverse and challenging clinical environments.
  • The study confirms the potential of AIXA Osteo for widespread clinical adoption in osteoporosis management.