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Updated: Aug 9, 2025

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

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Published on: July 2, 2021

Key-Point Detection Algorithm of Deep Learning Can Predict Lower Limb Alignment with Simple Knee Radiographs.

Hee Seung Nam¹, Sang Hyun Park¹, Jade Pei Yuik Ho¹

¹Department of Orthopaedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam-si 13620 82, Republic of Korea.

Journal of Clinical Medicine

|February 25, 2023

Summary

A deep learning algorithm accurately predicts the weight-bearing line (WBL) ratio using knee radiographs. This method offers a viable alternative for diagnosing lower limb alignment in osteoarthritis patients.

Keywords:

convolutional neural network knee machine learning prediction weight-bearing line

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Last Updated: Aug 9, 2025

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Area of Science:

Orthopedics
Radiology
Artificial Intelligence

Background:

Predicting the weight-bearing line (WBL) ratio from knee radiographs is crucial for assessing lower limb alignment.
Previous methods relied on direct measurements from whole leg radiographs, which can be cumbersome.

Purpose of the Study:

To quantitatively predict the WBL ratio using a convolutional neural network (CNN) on simple knee anteroposterior (AP) radiographs.
To develop a deep learning (DL) based key-point detection algorithm for lower limb alignment assessment.

Main Methods:

A dataset of 4790 knee AP radiographs from 2410 patients (March 2003-December 2021) was utilized.
A CNN model was trained to detect key points (tibial plateau start and exit points) for WBL ratio calculation.
Model performance was analyzed using pixel units and WBL error values.

Main Results:

Mean accuracy (MA) improved significantly with increased pixel margins, reaching around 0.8 with a 6-pixel margin.
When tibial plateau length was normalized to 100%, MA increased from ~0.1 (1% margin) to ~0.5 (5% margin).
The DL algorithm demonstrated accuracy comparable to direct measurements from whole leg radiographs.

Conclusions:

The DL-based key-point detection algorithm accurately predicts lower limb alignment using simple knee AP radiographs.
This approach provides a valuable tool for diagnosing lower limb alignment in primary care settings, particularly for osteoarthritis patients.