Basic Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Orthop. Jun 18, 2023; 14(6): 387-398
Published online Jun 18, 2023. doi: 10.5312/wjo.v14.i6.387
Automated patellar height assessment on high-resolution radiographs with a novel deep learning-based approach
Kamil Kwolek, Dariusz Grzelecki, Konrad Kwolek, Dariusz Marczak, Jacek Kowalczewski, Marcin Tyrakowski
Kamil Kwolek, Marcin Tyrakowski, Department of Spine Disorders and Orthopaedics, Centre of Postgraduate Medical Education, Gruca Orthopaedic and Trauma Teaching Hospital, Otwock 05-400, Poland
Dariusz Grzelecki, Dariusz Marczak, Jacek Kowalczewski, Department of Orthopaedics and Rheumoorthopedics, Centre of Postgraduate Medical Education, Gruca Orthopaedic and Trauma Teaching Hospital, Otwock 05-400, Poland
Konrad Kwolek, Department of Orthopaedics and Traumatology, University Hospital, Krakow 30-663, Poland
Author contributions: Kwolek Ka, Grzelecki D, Tyrakowski M designed research; Kwolek Ka, Kwolek Ko performed research; Kwolek Ka, Kwolek Ko elaborated analytic tools, Kwolek Ka, Tyrakowski M, Kowalczewski J, Marczak D analyzed data; Kwolek Ka, Dariusz G, Kwolek Ko, Tyrakowski M wrote the paper.
Institutional review board statement: This study protocol was reviewed and approved by the Bioethics Committee of the authors’ institution (No.133/PB/2020).
Institutional animal care and use committee statement: No animals were used in the study.
Conflict-of-interest statement: The authors have no conflict of interest concerning the materials or methods used in this study or the findings specified in this article.
Data sharing statement: No additional data are available.
ARRIVE guidelines statement: The authors have read the ARRIVE guidelines, and the manuscript was prepared and revised according to the ARRIVE guidelines.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Kamil Kwolek, MD, Academic Research, Doctor, Surgeon, Department of Spine Disorders and Orthopaedics, Centre of Postgraduate Medical Education, Gruca Orthopaedic and Trauma Teaching Hospital, Konarskiego 13, Otwock 05-400, Poland. kwolekamil@gmail.com
Received: February 15, 2023
Peer-review started: February 15, 2023
First decision: March 24, 2023
Revised: April 6, 2023
Accepted: May 6, 2023
Article in press: May 6, 2023
Published online: June 18, 2023
Processing time: 123 Days and 15 Hours
Core Tip

Core Tip: This study presents an accurate method for automatic assessment of patellar height on high-resolution lateral knee radiographs. First, You Only Look Once neural network is used to detect patellar and proximal tibial region. Next, U-Net neural network is utilized to segment bones of the detected region. Then, the Caton-Deschamps and Blackburne-Peel indexes are calculated upon patellar end-points and joint line fitted to proximal tibia joint surface. Experimental results show that our approach has the potential to be used as a pre- and postoperative assessment tool.