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Cited by in CrossRef
For: Kwolek K, Grzelecki D, Kwolek K, Marczak D, Kowalczewski J, Tyrakowski M. Automated patellar height assessment on high-resolution radiographs with a novel deep learning-based approach. World J Orthop 2023; 14(6): 387-398 [PMID: 37377994 DOI: 10.5312/wjo.v14.i6.387]
URL: https://www.wjgnet.com/2218-5836/full/v14/i6/387.htm
Number Citing Articles
1
Zeyu Liu, Jiangjiang Wu, Xu Gao, Zhipeng Qin, Run Tian, Chunsheng Wang. Deep learning-based automatic measurement system for patellar height: a multicenter retrospective studyJournal of Orthopaedic Surgery and Research 2024; 19(1) doi: 10.1186/s13018-024-04809-6
2
Lina Dai, Md Gapar Md Johar, Mohammed Hazim Alkawaz. The diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learningScientific Reports 2024; 14(1) doi: 10.1038/s41598-024-80441-y
3
Umile Giuseppe Longo, Alberto Lalli, Guido Nicodemi, Matteo Giuseppe Pisani, Alessandro De Sire, Pieter D'Hooghe, Ara Nazarian, Jacob F. Oeding, Balint Zsidai, Kristian Samuelsson. Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic reviewJournal of Experimental Orthopaedics 2025; 12(2) doi: 10.1002/jeo2.70259
4
Konrad Kwolek, Artur Gądek, Kamil Kwolek, Radek Kolecki, Henryk Liszka. Automated decision support for Hallux Valgus treatment options using anteroposterior foot radiographsWorld Journal of Orthopedics 2023; 14(11): 800-812 doi: 10.5312/wjo.v14.i11.800