Skip to main content

STUDY PROTOCOL article

Front. Med.
Sec. Precision Medicine
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1479187
This article is part of the Research Topic Harnessing Artificial Intelligence for Multimodal Predictive Modeling in Orthopedic Surgery View all 4 articles

Deep learning-based multimodal image analysis predicts bone cement leakage during percutaneous kyphoplasty: Protocol for model development, and validation by prospective and external datasets

Provisionally accepted
Yu Xi Yu Xi 1Ruiyuan Chen Ruiyuan Chen 1Tianyi Wang Tianyi Wang 1*Lei Zang Lei Zang 1*Shuncheng Jiao Shuncheng Jiao 2*Tianlang Xie Tianlang Xie 2*Qichao Wu Qichao Wu 1*Aobo Wang Aobo Wang 1Ning Fan Ning Fan 1Shuo Yuan Shuo Yuan 1Peng Du Peng Du 1*
  • 1 Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
  • 2 Department of Spine surgery, Beijing Shunyi Hospital, Beijing, China

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

    Background: Bone cement leakage (BCL) is one of the most prevalent complications of percutaneous kyphoplasty (PKP) for treating osteoporotic vertebral compression fracture (OVCF), which may result in severe secondary complications and poor outcomes. Previous studies employed several traditional machine learning (ML) models to predict BCL preoperatively, but effective and intelligent methods to bridge the distance between current models and real-life clinical applications remain lacking.We will develop a deep learning (DL)-based prediction model that directly analyzes preoperative computed tomography (CT) and magnetic resonance imaging (MRI) of patients with OVCF to accurately predict BCL occurrence and classification during PKP. This retrospective study includes a retrospective internal dataset for DL model training and validation, a prospective internal dataset, and a cross-center external dataset for model testing. We will evaluate not only model's predictive performance, but also its reliability by calculating its consistency with reference standards and comparing it with that of clinician prediction.The model holds an imperative clinical significance. Clinicians can formulate more targeted treatment strategies to minimize the incidence of BCL, thereby improving clinical outcomes by preoperatively identifying patients at high risk for each BCL subtype. In particular, the model holds great potential to be extended and applied in remote areas where medical resources are relatively scarce so that more patients can benefit from quality perioperative evaluation and management strategies.Moreover, the model will efficiently promote information sharing and decision-making between clinicians and patients, thereby increasing the overall quality of healthcare services.

    Keywords: Osteoporotic vertebral compression fracture, Percutaneous kyphoplasty, Bone cement leakage, artificial intelligence, deep learning

    Received: 11 Aug 2024; Accepted: 09 Sep 2024.

    Copyright: © 2024 Xi, Chen, Wang, Zang, Jiao, Xie, Wu, Wang, Fan, Yuan and Du. 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:
    Tianyi Wang, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
    Lei Zang, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
    Shuncheng Jiao, Department of Spine surgery, Beijing Shunyi Hospital, Beijing, China
    Tianlang Xie, Department of Spine surgery, Beijing Shunyi Hospital, Beijing, China
    Qichao Wu, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
    Peng Du, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 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.