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Automated Analysis of Alignment in Long-Leg Radiographs by Using a Fully Automated Support System Based on Artificial

Justus Schock¹, Daniel Truhn¹, Daniel B Abrar¹

¹Department of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany (J.S., D.B.A., S.N.); Institute of Computer Vision and Imaging, RWTH University Aachen, Pauwelsstrasse 30, 52072 Aachen, Germany (J.S., D.M.); Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (D.T., M.P., F.M., C.K., S.N.); and Faculty of Mathematics and Natural Sciences, Institute of Informatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany (S.C.).

Radiology. Artificial Intelligence

|May 3, 2021

Summary

This summary is machine-generated.

This study developed a deep learning method for analyzing lower-extremity alignment from long-leg radiographs (LLRs). The automated analysis accurately quantifies alignment and significantly speeds up clinical workflows.

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Area of Science:

Orthopedic surgery
Medical imaging analysis
Artificial intelligence in medicine

Background:

Accurate quantitative analysis of lower-extremity alignment is crucial for diagnosing and managing orthopedic conditions.
Manual measurement of alignment on long-leg radiographs (LLRs) is time-consuming and prone to inter-observer variability.

Purpose of the Study:

To develop and validate a deep learning-based method for the automatic quantitative analysis of lower-extremity alignment using LLRs.
To compare the accuracy and efficiency of the automated method against manual measurements by radiologists.

Main Methods:

A U-Net convolutional neural network was trained on 109 LLRs for femur and tibia segmentation.
Anatomic and mechanical axes were identified to quantify alignment via hip-knee-ankle angle (HKAA) and femoral anatomic-mechanical angle (AMA).
The algorithm's measurements were validated against manual measurements from two radiologists on 106 LLRs.

Main Results:

The deep learning model achieved high segmentation accuracy (Sørensen-Dice coefficients: 0.97 for femur, 0.96 for tibia).
Automated measurements of HKAA and AMA showed high agreement with radiologist measurements (interreader correlation coefficients up to 0.995).
The automated analysis was significantly faster (3 seconds) compared to manual measurements (35-36 seconds).

Conclusions:

Fully automated analysis of LLRs using deep learning provides accurate quantitative assessment of lower-extremity alignment.
The method is efficient and has the potential to enhance and accelerate clinical workflows in orthopedic practice.
This AI-driven approach is applicable across diverse clinical and pathological indications.