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Current Challenges and Innovations in Spine Deformity: Biomechanics, Imaging and AI-Driven Analysis

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Orthopedics".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 603

Special Issue Editor

Dr. Dong-Ho Kang

E-Mail Website
Guest Editor
Department of Orthopedic Surgery, Spine Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
Interests: spine surgery; spine deformity; minimally invasive surgery; patients-tailored treatment; medical imaging; artificial intelligence; machine learning

Special Issue Information

Dear Colleagues,

Spinal deformities, including scoliosis, kyphosis, sagittal imbalance, and cervical deformities, remain complex challenges in modern spine surgery. Despite advancements in surgical techniques and imaging modalities, optimizing patient outcomes through precise diagnosis, biomechanical understanding, and innovative treatment strategies is an ongoing endeavor. Recent developments in artificial intelligence (AI) and medical imaging offer new opportunities for refining spinal deformity assessment and enhancing surgical planning.

This Special Issue aims to explore the intersection of biomechanics, imaging, and AI-driven analysis in spinal deformity research, with a particular focus on both spinal and cervical deformities. Topics of interest include, but are not limited to, posture-related spinal and cervical alignment changes, the role of imaging modalities in deformity classification, AI-based phenotyping and prediction models, and novel surgical approaches guided by computational analysis. Contributions integrating clinical, radiographic, and computational methodologies are particularly encouraged.

By bringing together multidisciplinary research efforts, this Special Issue seeks to address key challenges in spinal and cervical deformity management, improve diagnostic accuracy, and advance personalized treatment strategies. We welcome original research, systematic reviews, and technical advancements that contribute to the evolving understanding of spinal and cervical deformities and their clinical implications.

Dr. Dong-Ho Kang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • spinal deformity
  • cervical deformity
  • biomechanics
  • postural alignment
  • sagittal imbalance
  • medical imaging
  • radiographic analysis
  • artificial intelligence
  • machine learning
  • deep learning
  • phenotyping
  • computer-assisted diagnosis
  • predictive modeling
  • surgical planning

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Published Papers (2 papers)

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Research

10 pages, 1364 KB  
Article
Automated Detection of Lumbosacral Transitional Vertebrae on Plain Lumbar Radiographs Using a Deep Learning Model
by Donghyuk Kwak, Du Hyun Ro and Dong-Ho Kang
J. Clin. Med. 2025, 14(21), 7671; https://doi.org/10.3390/jcm14217671 (registering DOI) - 29 Oct 2025
Abstract
Background/Objectives: Lumbosacral transitional vertebra (LSTV) is a common anatomical variant, but its identification on plain radiographs is often inconsistent. This inconsistency can lead to clinical complications such as chronic low back pain, misinterpretation of spinal parameters, and an increased risk of wrong-level [...] Read more.
Background/Objectives: Lumbosacral transitional vertebra (LSTV) is a common anatomical variant, but its identification on plain radiographs is often inconsistent. This inconsistency can lead to clinical complications such as chronic low back pain, misinterpretation of spinal parameters, and an increased risk of wrong-level surgery. This study aimed to develop and validate a deep learning-based artificial intelligence (AI) model for the automated detection of LSTV on plain lumbar radiographs. Methods: This retrospective observational study included a total of 3116 standing lumbar lateral radiographs. The presence or absence of lumbosacral transitional vertebra (LSTV) was definitively established using whole-spine imaging, CT, or MRI. Multiple deep learning architectures, including DINOv2, CLIP (ViT-B/32), and ResNet-50, were initially evaluated for binary classification of LSTV. Among these, the ResNet-50 model with partial fine-tuning achieved the best test performance and was subsequently selected for fivefold cross-validation using the training set. Model performance was assessed using accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC), and interpretability was evaluated using gradient-weighted class activation mapping (Grad-CAM). Results: On the independent test set of 313 radiographs, the final model demonstrated robust diagnostic performance. It achieved an accuracy of 76.4%, a sensitivity of 85.1%, a specificity of 61.9%, and an AUC of 0.84. The model correctly identified 166 out of 195 LSTV cases and 73 out of 118 normal cases. Conclusions: This AI-based system offers a highly accurate and reliable method for the automated detection of LSTV on plain radiographs. It shows strong potential as a clinical decision-support tool to reduce diagnostic errors, improve pre-operative planning, and enhance patient safety. Full article
(This article belongs to the Special Issue Current Challenges and Innovations in Spine Deformity: Biomechanics, Imaging and AI-Driven Analysis)
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14 pages, 1804 KB  
Article
Distal Adding-On as a Natural Shoulder Rebalancing Mechanism in Lenke Type 2A AIS with Right Sacral Slanting
by Jae-Hyuk Yang, Jae Min Park, Hyukjune Seong, Chang Ju Hwang and Hyung Rae Lee
J. Clin. Med. 2025, 14(19), 6850; https://doi.org/10.3390/jcm14196850 - 27 Sep 2025
Viewed by 338
Abstract
Background/Objectives: Distal adding-on (DA) is a common postoperative phenomenon in Lenke type 2A adolescent idiopathic scoliosis (AIS). Postoperative shoulder imbalance (PSI) is a clinically significant issue following AIS correction, as it may lead to aesthetic dissatisfaction, functional impairment, and reduced quality of [...] Read more.
Background/Objectives: Distal adding-on (DA) is a common postoperative phenomenon in Lenke type 2A adolescent idiopathic scoliosis (AIS). Postoperative shoulder imbalance (PSI) is a clinically significant issue following AIS correction, as it may lead to aesthetic dissatisfaction, functional impairment, and reduced quality of life. This study investigated radiographic changes in DA and shoulder balance in Lenke type 2A AIS, particularly focusing on distal wedge angle (DWA) and radiologic shoulder height (RSH) in patients with right sacral slanting (RSS). Methods: We retrospectively analyzed 120 patients with Lenke type 2A AIS who underwent posterior spinal fusion. Patients were grouped by sacral slanting: right (RSS), left (LSS), or none (NS). Radiographic parameters including proximal thoracic curve angle, main thoracic curve angle, DWA, RSH were assessed at multiple time points. Univariate and multivariate linear regression analyses were used to identify factors associated with DA. Results: The RSS group consistently showed the highest DWA and the greatest incidence of DA. RSH initially exceeded the PSI threshold in all groups but decreased to approximately 10 mm by final follow-up. In the RSS group, the inverse relationship between increasing DWA and decreasing RSH was most pronounced. Univariate regression identified postoperative RSH and sacral slanting angle as significant predictors of DWA, though not in the final multivariate model. Conclusions: In Lenke type 2A AIS with RSS, an increasing DWA and decreasing RSH over time suggest that DA may serve as a compensatory mechanism for PSI. Sacral slanting and postoperative RSH may be relevant predictors of this dynamic alignment change. Full article
(This article belongs to the Special Issue Current Challenges and Innovations in Spine Deformity: Biomechanics, Imaging and AI-Driven Analysis)
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