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ORIGINAL RESEARCH article

Front. Bioeng. Biotechnol.
Sec. Biomechanics
Volume 12 - 2024 | doi: 10.3389/fbioe.2024.1471692
This article is part of the Research Topic Computational and Experimental Approaches on Soft Tissues Biomechanics and Mechanobiology View all 12 articles

A Single Sequence MRI-based Deep Learning Radiomics Model in The Diagnosis of Early Osteonecrosis of Femoral Head

Provisionally accepted
Tariq Alkhatatbeh Tariq Alkhatatbeh 1,2Ahmad Alkhatatbeh Ahmad Alkhatatbeh 3Xiaohui Li Xiaohui Li 4Wang Wei Wang Wei 2*
  • 1 Xi'an Jiaotong University, Xi'an, China
  • 2 Comprehensive Orthopedic Surgery Department, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi'an, China
  • 3 Department of Orthopedics, First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong Province, China
  • 4 Department of Radiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi’an, Shaanxi Province, China

The final, formatted version of the article will be published soon.

    Purpose: The objective of this study was to create and assess a Deep Learning-Based Radiomics model that could accurately predict early Femoral Head Osteonecrosis (ONFH). This model has the potential to be highly beneficial in the early stages of diagnosis and treatment planning. Methods: MRI scans from 150 patients in total (80 healthy, 70 necrotic) were used, and split into training and testing sets in a 7:3 ratio. Handcrafted as well as deep learning features were retrieved from Tesla 2 weighted (T2W1) MRI slices. After a rigorous selection process, these features were used to construct three models: a Radiomics-based (Rad-model), a Deep Learning-based (DL-model), and a Deep Learning-based Radiomics (DLR-model). The performance of these models in predicting early ONFH was evaluated by comparing them using the receiver operating characteristic (ROC) and decision curve analysis (DCA).Results: 1197 handcrafted radiomics and 512 DL features were extracted then processed; after the final selection: 15 features were used for the Rad-model, 12 features for the DL-model, and only 9 features were selected for the DLRmodel. The most effective algorithm that was used in all of the models was Logistic regression (LR). The Rad-model depicted good results outperforming the DL-model; AUC=0.944 (95%CI 0.862-1.000) and AUC=0.930 (95%CI 0.838-1.000) respectively. The DLR-model showed superior results to both Rad-model and the DL-model; AUC= 0.968 (95%CI 0.909-1.000); and a sensitivity of 0.95 and specificity of 0.920. The DCA showed that DLR had a greater net clinical benefit in detecting early ONFH.We created and tested a Deep Learning-Based Radiomics model that properly predicted early ONFH. This model has the capacity to significantly aid surgeons in detecting early ONFH and arranging prompt therapy.

    Keywords: Radiomics, deep learning, Osteonecrosis, Femoral head, Magnetic resonance image

    Received: 28 Jul 2024; Accepted: 22 Aug 2024.

    Copyright: © 2024 Alkhatatbeh, Alkhatatbeh, Li and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Wang Wei, Comprehensive Orthopedic Surgery Department, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi'an, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.