Abstract

Deep learning algorithms can evaluate large and complex sets of data, offering various support for medical imaging analysis. Previous works have explored applications of deep learning to measure leg lengths more efficiently. These previous studies provide evidence to suggest deep-learning algorithms can improve efficiency with high levels of accuracy and speed. In this retrospective study, we utilize deep learning-based convolutional neural networks, programmed with input from a human expert, to identify key points and measure leg length. We collected frontal computed tomography (CT) scout radiographs from preoperative CT scans of patients undergoing evaluation for knee arthroplasty from diverse sources to both train and test the model. We prepared a DenseNet121 model to predict and identify key points, which were then used to develop patch-based models. We applied separable convolutional layers to complete the analysis. The data reflects that 1) separable convolution exhibits lower mean absolute error (MAE) and increased convergence speed as compared to global average pooling layers and 2) optimal learning rates, batch size, and patch size can be achieved to present the least MAE. Our findings provide useful information and an automated tool to assist radiologists to diagnose leg length discrepancy in clinical practice.

Original languageEnglish
Title of host publicationProceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages17-22
Number of pages6
ISBN (Electronic)9781665484879
DOIs
StatePublished - 2022
Event22nd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2022 - Virtual, Online, Taiwan, Province of China
Duration: 7 Nov 20229 Nov 2022

Publication series

NameProceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022

Conference

Conference22nd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2022
Country/TerritoryTaiwan, Province of China
CityVirtual, Online
Period7/11/229/11/22

Keywords

  • deep learning
  • leg length discrepancy
  • radimagenet

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