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Diagnostics
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Advances in Musculoskeletal Imaging: From Diagnosis to Treatment
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Advances in Musculoskeletal Imaging: From Diagnosis to Treatment

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2339

Special Issue Editors

Dr. Chiara Martini

E-Mail Website
Guest Editor
Department of Radiology, Parma University Hospital, Via Gramsci 14, 43125 Parma, Italy
Interests: cardiac imaging; pulmonary imaging; cardiac computed tomography; cardiac magnetic resonance; post-processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Massimo De Filippo

E-Mail Website
Guest Editor
Department of Medicine and Surgery, Section of Radiology, University of Parma, Maggiore Hospital, Via Gramsci 14, 43126 Parma, Italy
Interests: multiparametric MRI; diagnostic imaging; cardiology; diagnostic & therapeutic ultrasound; lung cancer

Special Issue Information

Dear Colleagues,

Musculoskeletal imaging plays a crucial role in the diagnosis and management of a wide range of conditions, from acute injuries to chronic degenerative diseases. The field has witnessed significant advancements in recent years, with the development and refinement of various imaging modalities. Magnetic resonance imaging (MRI), computed tomography (CT), ultrasonography, and emerging functional and molecular imaging techniques have transformed the way clinicians approach musculoskeletal disorders. These advanced imaging methods provide unprecedented detail and insight into the structure and function of bones, joints, muscles, and surrounding tissues, enabling more accurate diagnoses and tailored treatment plans.

The continuous evolution of musculoskeletal imaging has opened new avenues for research and clinical practice. Artificial intelligence (AI) and machine learning algorithms are being integrated into image analysis, enhancing the speed and accuracy of diagnoses. Additionally, image-guided therapeutic interventions have become increasingly sophisticated, allowing for minimally invasive procedures with improved precision and outcomes. This Special Issue seeks to highlight these cutting-edge developments in musculoskeletal imaging, focusing on novel diagnostic approaches, AI-driven analysis techniques, and innovative image-guided therapies. By showcasing original research and comprehensive reviews in these areas, this Special Issue aims to contribute to the advancement of patient care through the application of state-of-the-art imaging solutions in musculoskeletal medicine.

Dr. Chiara Martini
Prof. Dr. Massimo De Filippo
Guest Editors

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. Diagnostics 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

  • musculoskeletal imaging
  • artificial intelligence
  • bone imaging
  • musculoskeletal ultrasound
  • diagnosis
  • machine learning algorithms

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Published Papers (5 papers)

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Research

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12 pages, 949 KiB  
Article
Diagnostic Value of T2 Mapping in Sacroiliitis Associated with Spondyloarthropathy
by Mustafa Koyun and Kemal Niyazi Arda
Diagnostics 2025, 15(13), 1634; https://doi.org/10.3390/diagnostics15131634 - 26 Jun 2025
Viewed by 216
Abstract
Background/Objectives: T2 mapping is a quantitative magnetic resonance imaging (MRI) technique that provides information about tissue water content and molecular mobility. This study aimed to evaluate the diagnostic utility of T2 mapping in assessing sacroiliitis associated with spondyloarthropathy (SpA). Methods: A prospective study [...] Read more.
Background/Objectives: T2 mapping is a quantitative magnetic resonance imaging (MRI) technique that provides information about tissue water content and molecular mobility. This study aimed to evaluate the diagnostic utility of T2 mapping in assessing sacroiliitis associated with spondyloarthropathy (SpA). Methods: A prospective study examined a total of 56 participants, comprising 31 SpA patients (n = 31) and 25 healthy controls (n = 25), who underwent sacroiliac joint MRI between August 2018 and August 2020. T2 mapping images were generated using multi-echo turbo spin echo (TSE) sequence, and quantitative T2 relaxation times were measured from bone and cartilage regions. Statistical analysis employed appropriate parametric and non-parametric tests with significance set at p < 0.05. Results: The mean T2 relaxation time measured from the areas with osteitis of SpA patients (100.23 ± 7.41 ms; 95% CI: 97.51–102.95) was significantly higher than that of the control group in normal bone (69.44 ± 4.37 ms; 95% CI: 67.64–71.24), and this difference was found to be statistically significant (p < 0.001). No significant difference was observed between cartilage T2 relaxation times in SpA patients and controls (p > 0.05). Conclusions: T2 mapping serves as a valuable quantitative imaging biomarker for diagnosing sacroiliitis associated with SpA, particularly by detecting bone marrow edema. The technique shows promise for objective disease assessment, though larger studies are needed to establish standardized reference values for T2 relaxation times in osteitis to enhance diagnostic accuracy and facilitate treatment monitoring. Full article
(This article belongs to the Special Issue Advances in Musculoskeletal Imaging: From Diagnosis to Treatment)
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12 pages, 2632 KiB  
Article
Comparison of a New Radiographic Technique with MRI Measurements for Tibial Tunnel Evaluation in ACL Reconstruction
by Mücahid Osman Yücel, Raşit Emin Dalaslan, Sönmez Sağlam, Zekeriya Okan Karaduman, Mehmet Arıcan, Bedrettin Akar and Volkan Tural
Diagnostics 2025, 15(10), 1237; https://doi.org/10.3390/diagnostics15101237 - 14 May 2025
Cited by 1 | Viewed by 386
Abstract
Background/Objectives: The correct angular placement of the tibial tunnel is crucial to ensure graft tension, maintain knee stability, and ensure optimal clinical outcomes after anterior cruciate ligament (ACL) reconstruction. While 3D imaging methods such as MRI and CT are the gold standard [...] Read more.
Background/Objectives: The correct angular placement of the tibial tunnel is crucial to ensure graft tension, maintain knee stability, and ensure optimal clinical outcomes after anterior cruciate ligament (ACL) reconstruction. While 3D imaging methods such as MRI and CT are the gold standard for evaluating tunnel positioning, their routine use is limited by cost, availability, and time constraints. In clinical practice, 2D radiographs are more accessible but lack established reliability in accurately estimating tunnel angles. The aim of this study was to convert 2D radiographic angular measurements used in the evaluation of patients undergoing anterior cruciate ligament reconstruction into 3D values with a simple method and to compare these measurements with three-dimensional angles calculated using conventional MRI and CT. Methods: This retrospective study included 38 patients who underwent anatomic anterior cruciate ligament reconstruction. Postoperative radiographs and MR images were analyzed to determine the tibial tunnel angles. The angles calculated from 2D radiographs were statistically analyzed for their correlation with the actual 3D angles measured by MRI. Results: The analysis showed a strong correlation between tibial tunnel angles from radiographs and MRI, with minimal, non-significant differences. This suggests that radiographs can provide a reliable estimate of tibial tunnel angles. Conclusions: These findings suggest that radiographs can predict tibial tunnel angles in ACL reconstruction as accurately as MRI. This method can guide the correct tunnel angle and facilitate postoperative evaluation. Further studies are needed to confirm these results across various populations and techniques. Full article
(This article belongs to the Special Issue Advances in Musculoskeletal Imaging: From Diagnosis to Treatment)
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Review

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25 pages, 418 KiB  
Review
Emerging Diagnostic Approaches for Musculoskeletal Disorders: Advances in Imaging, Biomarkers, and Clinical Assessment
by Rahul Kumar, Kiran Marla, Kyle Sporn, Phani Paladugu, Akshay Khanna, Chirag Gowda, Alex Ngo, Ethan Waisberg, Ram Jagadeesan and Alireza Tavakkoli
Diagnostics 2025, 15(13), 1648; https://doi.org/10.3390/diagnostics15131648 - 27 Jun 2025
Viewed by 282
Abstract
Musculoskeletal (MSK) disorders remain a major global cause of disability, with diagnostic complexity arising from their heterogeneous presentation and multifactorial pathophysiology. Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. This review offers a [...] Read more.
Musculoskeletal (MSK) disorders remain a major global cause of disability, with diagnostic complexity arising from their heterogeneous presentation and multifactorial pathophysiology. Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. This review offers a novel synthesis by unifying recent innovations across multiple diagnostic imaging modalities, such as CT, MRI, and ultrasound, with emerging biochemical, genetic, and digital technologies. While existing reviews typically focus on advances within a single modality or for specific MSK conditions, this paper integrates a broad spectrum of developments to highlight how use of multimodal diagnostic strategies in combination can improve disease detection, stratification, and clinical decision-making in real-world settings. Technological developments in imaging, including photon-counting detector computed tomography, quantitative magnetic resonance imaging, and four-dimensional computed tomography, have enhanced the ability to visualize structural and dynamic musculoskeletal abnormalities with greater precision. Molecular imaging and biochemical markers such as CTX-II (C-terminal cross-linked telopeptides of type II collagen) and PINP (procollagen type I N-propeptide) provide early, objective indicators of tissue degeneration and bone turnover, while genetic and epigenetic profiling can elucidate individual patterns of susceptibility. Point-of-care ultrasound and portable diagnostic devices have expanded real-time imaging and functional assessment capabilities across diverse clinical settings. Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. Wearable sensors and mobile health technologies extend continuous monitoring beyond traditional healthcare environments, generating real-world data critical for dynamic disease management. However, standardization of diagnostic protocols, rigorous validation of novel methodologies, and thoughtful integration of multimodal data remain essential for translating technological advances into improved patient outcomes. Despite these advances, several key limitations constrain widespread clinical adoption. Imaging modalities lack standardized acquisition protocols and reference values, making cross-site comparison and clinical interpretation difficult. AI-driven diagnostic tools often suffer from limited external validation and transparency (“black-box” models), impacting clinicians’ trust and hindering regulatory approval. Molecular markers like CTX-II and PINP, though promising, show variability due to diurnal fluctuations and comorbid conditions, complicating their use in routine monitoring. Integration of multimodal data, especially across imaging, omics, and wearable devices, remains technically and logistically complex, requiring robust data infrastructure and informatics expertise not yet widely available in MSK clinical practice. Furthermore, reimbursement models have not caught up with many of these innovations, limiting access in resource-constrained healthcare settings. As these fields converge, musculoskeletal diagnostics methods are poised to evolve into a more precise, personalized, and patient-centered discipline, driving meaningful improvements in musculoskeletal health worldwide. Full article
(This article belongs to the Special Issue Advances in Musculoskeletal Imaging: From Diagnosis to Treatment)
21 pages, 374 KiB  
Review
Biomarker-Guided Imaging and AI-Augmented Diagnosis of Degenerative Joint Disease
by Rahul Kumar, Kyle Sporn, Aryan Borole, Akshay Khanna, Chirag Gowda, Phani Paladugu, Alex Ngo, Ram Jagadeesan, Nasif Zaman and Alireza Tavakkoli
Diagnostics 2025, 15(11), 1418; https://doi.org/10.3390/diagnostics15111418 - 3 Jun 2025
Viewed by 699
Abstract
Degenerative joint disease remains a leading cause of global disability, with early diagnosis posing a significant clinical challenge due to its gradual onset and symptom overlap with other musculoskeletal disorders. This review focuses on emerging diagnostic strategies by synthesizing evidence specifically from studies [...] Read more.
Degenerative joint disease remains a leading cause of global disability, with early diagnosis posing a significant clinical challenge due to its gradual onset and symptom overlap with other musculoskeletal disorders. This review focuses on emerging diagnostic strategies by synthesizing evidence specifically from studies that integrate biochemical biomarkers, advanced imaging techniques, and machine learning models relevant to osteoarthritis. We evaluate the diagnostic utility of cartilage degradation markers (e.g., CTX-II, COMP), inflammatory cytokines (e.g., IL-1β, TNF-α), and synovial fluid microRNA profiles, and how they correlate with quantitative imaging readouts from T2-mapping MRI, ultrasound elastography, and dual-energy CT. Furthermore, we highlight recent developments in radiomics and AI-driven image interpretation to assess joint space narrowing, osteophyte formation, and subchondral bone changes with high fidelity. The integration of these datasets using multimodal learning approaches offers novel diagnostic phenotypes that stratify patients by disease stage and risk of progression. Finally, we explore the implementation of these tools in point-of-care diagnostics, including portable imaging devices and rapid biomarker assays, particularly in aging and underserved populations. By presenting a unified diagnostic pipeline, this article advances the future of early detection and personalized monitoring in joint degeneration. Full article
(This article belongs to the Special Issue Advances in Musculoskeletal Imaging: From Diagnosis to Treatment)
35 pages, 2649 KiB  
Review
Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care
by Rahul Kumar, Kyle Sporn, Akshay Khanna, Phani Paladugu, Chirag Gowda, Alex Ngo, Ram Jagadeesan, Nasif Zaman and Alireza Tavakkoli
Diagnostics 2025, 15(11), 1377; https://doi.org/10.3390/diagnostics15111377 - 29 May 2025
Cited by 2 | Viewed by 602
Abstract
Musculoskeletal tumors present a diagnostic challenge due to their rarity, histological diversity, and overlapping imaging features. Accurate characterization is essential for effective treatment planning and prognosis, yet current diagnostic workflows rely heavily on invasive biopsy and subjective radiologic interpretation. This review explores the [...] Read more.
Musculoskeletal tumors present a diagnostic challenge due to their rarity, histological diversity, and overlapping imaging features. Accurate characterization is essential for effective treatment planning and prognosis, yet current diagnostic workflows rely heavily on invasive biopsy and subjective radiologic interpretation. This review explores the evolving role of radiogenomics and machine learning in improving diagnostic accuracy for bone and soft tissue tumors. We examine integrating quantitative imaging features from MRI, CT, and PET with genomic and transcriptomic data to enable non-invasive tumor profiling. AI-powered platforms employing convolutional neural networks (CNNs) and radiomic texture analysis show promising results in tumor grading, subtype differentiation (e.g., Osteosarcoma vs. Ewing sarcoma), and predicting mutation signatures (e.g., TP53, RB1). Moreover, we highlight the use of liquid biopsy and circulating tumor DNA (ctDNA) as emerging diagnostic biomarkers, coupled with point-of-care molecular assays, to enable early and accurate detection in low-resource settings. The review concludes by discussing translational barriers, including data harmonization, regulatory challenges, and the need for multi-institutional datasets to validate AI-based diagnostic frameworks. This article synthesizes current advancements and provides a forward-looking view of precision diagnostics in musculoskeletal oncology. Full article
(This article belongs to the Special Issue Advances in Musculoskeletal Imaging: From Diagnosis to Treatment)
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