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Deep embeddings for novelty detection in myopathy.

Philippe Burlina1, Neil Joshi2, Seth Billings2

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|December 25, 2018
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This study introduces a deep learning method for detecting anomalies in ultrasound images to screen for myopathies. The approach shows promise as a baseline for future clinical tools in early disease detection.

Keywords:
Deep embeddingsDeep learningMuscular diseasesNovelty detection

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurology

Background:

  • Myopathies are a group of muscle diseases that can be challenging to diagnose, particularly rare presentations.
  • Ultrasound imaging offers a non-invasive method for muscle assessment, but anomaly detection requires sophisticated techniques.
  • Early and accurate diagnosis of myopathies, such as myositis, is crucial for timely treatment and management.

Purpose of the Study:

  • To develop and evaluate a deep learning-based approach for unsupervised novelty detection in ultrasound images for myopathy screening.
  • To establish a benchmark for automated anomaly detection in muscle ultrasound to aid in the diagnosis of myopathic diseases.
  • To investigate the efficacy of deep embeddings and novelty scoring methods for identifying myositis in ultrasound data.

Main Methods:

  • Development of a fully annotated ultrasound image dataset (Myositis3K) comprising 3586 images from 89 individuals (54 with myositis, 35 controls).
  • Application of unsupervised novelty detection (ND) techniques utilizing deep embeddings and various novelty scoring algorithms.
  • Comparative analysis of developed ND algorithms against human clinician performance, supervised classification, and generative unsupervised methods.

Main Results:

  • The best-performing unsupervised novelty detection approach achieved an Area Under the Receiver Operating Characteristic Curve (ROC AUC) of 0.7192 (95% CI: 0.0164).
  • This performance provides a promising baseline for automated prescreening of myopathies using ultrasound.
  • The study demonstrated the potential of deep learning for identifying subtle anomalies indicative of myopathic disease.

Conclusions:

  • Unsupervised novelty detection using deep learning is a viable strategy for anomaly detection in myopathy screening via ultrasound.
  • The developed Myositis3K dataset and the proposed deep learning framework serve as a foundation for future clinical diagnostic tools.
  • Further research and validation are warranted to refine these methods for widespread clinical application in diagnosing inflammatory muscle diseases.