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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Artificial Intelligence System for Automatic Quantitative Analysis and Radiology Reporting of Leg Length Radiographs.

Nathan Larson1, Chantal Nguyen2, Bao Do3

  • 1Computer Science Department, Brigham Young University, Campus Dr, Provo, UT, 3361 TMCB84604, USA.

Journal of Digital Imaging
|July 6, 2022
PubMed
Summary

An AI system accurately analyzes leg length radiographs, measuring lengths and angles, and detecting hardware. This deep learning approach shows potential for improving radiologist workflow in orthopedic imaging.

Keywords:
Artificial IntelligenceDeep LearningLeg Length DiscrepancyRadiography

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Area of Science:

  • Orthopedic imaging
  • Artificial intelligence in medicine
  • Radiology workflow optimization

Background:

  • Leg length discrepancies are common orthopedic issues impacting function.
  • Bilateral leg length radiographs are standard for assessment.
  • Accurate interpretation is crucial for effective treatment.

Purpose of the Study:

  • To evaluate an AI-based image analysis system for interpreting long leg length radiographs.
  • To determine the accuracy of AI in measuring bone lengths, angles, and detecting orthopedic hardware.
  • To assess the potential of AI in improving radiologist efficiency.

Main Methods:

  • Developed an end-to-end AI system using fasterRCNN-ResNet101 and EfficientNet-D0 models.
  • Trained the system on 1,726 extremities from a tertiary referral center.
  • Validated performance on a 220-image test set annotated by radiologists.

Main Results:

  • AI demonstrated high recall (0.98) and precision (0.96) in landmark detection.
  • Excellent correlation (>0.99) for length measurements with <1% error.
  • High correlation for angles (0.98, 0.86) with <1° mean absolute error.
  • 99.8% accuracy in orthopedic hardware detection.

Conclusions:

  • Deep learning enables feasible automatic quantitative and qualitative analysis of leg length radiographs.
  • The AI system shows high accuracy comparable to radiologists.
  • This technology holds significant potential for enhancing radiologist workflow and patient care.