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Diagnostics
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Artificial Intelligence Applications on Musculoskeletal Imaging
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Artificial Intelligence Applications on Musculoskeletal Imaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 14643

Special Issue Editors

Dr. Michail Klontzas

E-Mail Website
Guest Editor
1. Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece
2. Department of Radiology, School of Medicine, University of Crete, Heraklion, Crete, Greece
3. Advanced Hybrid Imaging Systems, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion, Crete, Greece
Interests: musculoskeletal imaging; artificial intelligence; omics analyses; tissue engineering
Prof. Dr. Apostolos Karantanas

E-Mail Website
Guest Editor
1. Department of Medical Imaging, University Hospital, Voutes, 71110, Heraklion, Crete, Greece
2. Department of Radiology, School of Medicine, University of Crete, Heraklion, Crete, Greece
3. Advanced Hybrid Imaging Systems, Institute of Computer Science-FORTH, Crete, Greece
Interests: musculoskeletal imaging; artificial intelligence; radiomics; biomedical engineering

Special Issue Information

Dear Colleagues,

The development of artificial intelligence (AI) has revolutionized medical imaging over the last few years, allowing for the automation of a series of tasks in routine radiological practice. Musculoskeletal (MSK) imaging plays an important role in the diagnosis and treatment of musculoskeletal disorders, involving a great variety of imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), ultrasound (US) and radiography.

This Special Issue addresses the rapid development of AI in the field of MSK imaging and aims to gather manuscripts covering AI applications for MSK imaging. The issue will include original and review articles on traditional machine learning as well as deep learning applications related to MSK disorders on any relevant imaging modality (MRI, US, CT, X-rays). Contributions considered for this Special Issue may also be related to the field of radiomics as well as the integration of radiomics with other types of biological omics (radiogenomics, radiotranscriptomics etc.) provided that they are applied to MSK disease.

Dr. Michail Klontzas
Prof. Dr. Apostolos Karantanas
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
  • Musculoskeletal disorders
  • Artificial intelligence
  • Machine learning
  • Deep learning
  • Radiomics
  • Radiogenomics
  • Radiotranscriptomics
  • Biomarkers
  • Diagnosis
  • Segmentation
  • MR imaging
  • Computed tomography

Published Papers (5 papers)

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Research

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11 pages, 4267 KiB  
Article
Faster Elbow MRI with Deep Learning Reconstruction—Assessment of Image Quality, Diagnostic Confidence, and Anatomy Visualization Compared to Standard Imaging
by Judith Herrmann, Saif Afat, Sebastian Gassenmaier, Jan-Peter Grunz, Gregor Koerzdoerfer, Andreas Lingg, Haidara Almansour, Dominik Nickel, Theresa Sophie Patzer and Sebastian Werner
Diagnostics 2023, 13(17), 2747; https://doi.org/10.3390/diagnostics13172747 - 24 Aug 2023
Cited by 1 | Viewed by 790
Abstract
Objective: The objective of this study was to evaluate a deep learning (DL) reconstruction for turbo spin echo (TSE) sequences of the elbow regarding image quality and visualization of anatomy. Materials and Methods: Between October 2020 and June 2021, seventeen participants (eight patients, [...] Read more.
Objective: The objective of this study was to evaluate a deep learning (DL) reconstruction for turbo spin echo (TSE) sequences of the elbow regarding image quality and visualization of anatomy. Materials and Methods: Between October 2020 and June 2021, seventeen participants (eight patients, nine healthy subjects; mean age: 43 ± 16 (20–70) years, eight men) were prospectively included in this study. Each patient underwent two examinations: standard MRI, including TSE sequences reconstructed with a generalized autocalibrating partial parallel acquisition reconstruction (TSESTD), and prospectively undersampled TSE sequences reconstructed with a DL reconstruction (TSEDL). Two radiologists evaluated the images concerning image quality, noise, edge sharpness, artifacts, diagnostic confidence, and delineation of anatomical structures using a 5-point Likert scale, and rated the images concerning the detection of common pathologies. Results: Image quality was significantly improved in TSEDL (mean 4.35, IQR 4–5) compared to TSESTD (mean 3.76, IQR 3–4, p = 0.008). Moreover, TSEDL showed decreased noise (mean 4.29, IQR 3.5–5) compared to TSESTD (mean 3.35, IQR 3–4, p = 0.004). Ratings for delineation of anatomical structures, artifacts, edge sharpness, and diagnostic confidence did not differ significantly between TSEDL and TSESTD (p > 0.05). Inter-reader agreement was substantial to almost perfect (κ = 0.628–0.904). No difference was found concerning the detection of pathologies between the readers and between TSEDL and TSESTD. Using DL, the acquisition time could be reduced by more than 35% compared to TSESTD. Conclusion: TSEDL provided improved image quality and decreased noise while receiving equal ratings for edge sharpness, artifacts, delineation of anatomical structures, diagnostic confidence, and detection of pathologies compared to TSESTD. Providing more than a 35% reduction of acquisition time, TSEDL may be clinically relevant for elbow imaging due to increased patient comfort and higher patient throughput. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications on Musculoskeletal Imaging)
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14 pages, 3774 KiB  
Article
Radiomics for the Detection of Active Sacroiliitis Using MR Imaging
by Matthaios Triantafyllou, Michail E. Klontzas, Emmanouil Koltsakis, Vasiliki Papakosta, Konstantinos Spanakis and Apostolos H. Karantanas
Diagnostics 2023, 13(15), 2587; https://doi.org/10.3390/diagnostics13152587 - 03 Aug 2023
Cited by 4 | Viewed by 1237
Abstract
Detecting active inflammatory sacroiliitis at an early stage is vital for prescribing medications that can modulate disease progression and significantly delay or prevent debilitating forms of axial spondyloarthropathy. Conventional radiography and computed tomography offer limited sensitivity in detecting acute inflammatory findings as these [...] Read more.
Detecting active inflammatory sacroiliitis at an early stage is vital for prescribing medications that can modulate disease progression and significantly delay or prevent debilitating forms of axial spondyloarthropathy. Conventional radiography and computed tomography offer limited sensitivity in detecting acute inflammatory findings as these methods primarily identify chronic structural lesions. Conversely, Magnetic Resonance Imaging (MRI) is the preferred technique for detecting bone marrow edema, although it is a complex process requiring extensive expertise. Additionally, ascertaining the origin of lesions can be challenging, even for experienced medical professionals. Machine learning (ML) has showcased its proficiency in various fields by uncovering patterns that are not easily perceived from multi-dimensional datasets derived from medical imaging. The aim of this study is to develop a radiomic signature to aid clinicians in diagnosing active sacroiliitis. A total of 354 sacroiliac joints were segmented from axial fluid-sensitive MRI images, and their radiomic features were extracted. After selecting the most informative features, a number of ML algorithms were utilized to identify the optimal method for detecting active sacroiliitis, leading to the selection of an Extreme Gradient Boosting (XGBoost) model that accomplished an Area Under the Receiver-Operating Characteristic curve (AUC-ROC) of 0.71, thus further showcasing the potential of radiomics in the field. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications on Musculoskeletal Imaging)
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14 pages, 3291 KiB  
Article
Visual Cascaded-Progressive Convolutional Neural Network (C-PCNN) for Diagnosis of Meniscus Injury
by Yingkai Ma, Yong Qin, Chen Liang, Xiang Li, Minglei Li, Ren Wang, Jinping Yu, Xiangning Xu, Songcen Lv, Hao Luo and Yuchen Jiang
Diagnostics 2023, 13(12), 2049; https://doi.org/10.3390/diagnostics13122049 - 13 Jun 2023
Cited by 1 | Viewed by 1287
Abstract
Objective: The objective of this study is to develop a novel automatic convolutional neural network (CNN) that aids in the diagnosis of meniscus injury, while enabling the visualization of lesion characteristics. This will improve the accuracy and reduce diagnosis times. Methods: We presented [...] Read more.
Objective: The objective of this study is to develop a novel automatic convolutional neural network (CNN) that aids in the diagnosis of meniscus injury, while enabling the visualization of lesion characteristics. This will improve the accuracy and reduce diagnosis times. Methods: We presented a cascaded-progressive convolutional neural network (C-PCNN) method for diagnosing meniscus injuries using magnetic resonance imaging (MRI). A total of 1396 images collected in the hospital were used for training and testing. The method used for training and testing was 5-fold cross validation. Using intraoperative arthroscopic diagnosis and MRI diagnosis as criteria, the C-PCNN was evaluated based on accuracy, sensitivity, specificity, receiver operating characteristic (ROC), and evaluation performance. At the same time, the diagnostic accuracy of doctors with the assistance of cascade- progressive convolutional neural networks was evaluated. The diagnostic accuracy of a C-PCNN assistant with an attending doctor and chief doctor was compared to evaluate the clinical significance. Results: C-PCNN showed 85.6% accuracy in diagnosing and identifying anterior horn injury, and 92% accuracy in diagnosing and identifying posterior horn injury. The average accuracy of C-PCNN was 89.8%, AUC = 0.86. The diagnosis accuracy of the attending physician with the aid of the C-PCNN was comparable to that of the chief physician. Conclusion: The C-PCNN-based MRI technique for diagnosing knee meniscus injuries has significant practical value in clinical practice. With a high rate of accuracy, clinical auxiliary physicians can increase the speed and accuracy of diagnosis and decrease the number of incorrect diagnoses. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications on Musculoskeletal Imaging)
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13 pages, 8836 KiB  
Article
Deep Learning for Fully Automated Radiographic Measurements of the Pelvis and Hip
by Christoph Stotter, Thomas Klestil, Christoph Röder, Philippe Reuter, Kenneth Chen, Robert Emprechtinger, Allan Hummer, Christoph Salzlechner, Matthew DiFranco and Stefan Nehrer
Diagnostics 2023, 13(3), 497; https://doi.org/10.3390/diagnostics13030497 - 29 Jan 2023
Cited by 4 | Viewed by 6303
Abstract
The morphometry of the hip and pelvis can be evaluated in native radiographs. Artificial-intelligence-assisted analyses provide objective, accurate, and reproducible results. This study investigates the performance of an artificial intelligence (AI)-based software using deep learning algorithms to measure radiological parameters that identify femoroacetabular [...] Read more.
The morphometry of the hip and pelvis can be evaluated in native radiographs. Artificial-intelligence-assisted analyses provide objective, accurate, and reproducible results. This study investigates the performance of an artificial intelligence (AI)-based software using deep learning algorithms to measure radiological parameters that identify femoroacetabular impingement and hip dysplasia. Sixty-two radiographs (124 hips) were manually evaluated by three observers and fully automated analyses were performed by an AI-driven software (HIPPO™, ImageBiopsy Lab, Vienna, Austria). We compared the performance of the three human readers with the HIPPO™ using a Bayesian mixed model. For this purpose, we used the absolute deviation from the median ratings of all readers and HIPPO™. Our results indicate a high probability that the AI-driven software ranks better than at least one manual reader for the majority of outcome measures. Hence, fully automated analyses could provide reproducible results and facilitate identifying radiographic signs of hip disorders. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications on Musculoskeletal Imaging)
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Review

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14 pages, 2002 KiB  
Review
Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review
by Kenneth Chen, Christoph Stotter, Thomas Klestil and Stefan Nehrer
Diagnostics 2022, 12(9), 2235; https://doi.org/10.3390/diagnostics12092235 - 16 Sep 2022
Cited by 9 | Viewed by 3111
Abstract
Artificial intelligence (AI) in medicine is a rapidly growing field. In orthopedics, the clinical implementations of AI have not yet reached their full potential. Deep learning algorithms have shown promising results in computed radiographs for fracture detection, classification of OA, bone age, as [...] Read more.
Artificial intelligence (AI) in medicine is a rapidly growing field. In orthopedics, the clinical implementations of AI have not yet reached their full potential. Deep learning algorithms have shown promising results in computed radiographs for fracture detection, classification of OA, bone age, as well as automated measurements of the lower extremities. Studies investigating the performance of AI compared to trained human readers often show equal or better results, although human validation is indispensable at the current standards. The objective of this narrative review is to give an overview of AI in medicine and summarize the current applications of AI in orthopedic radiography imaging. Due to the different AI software and study design, it is difficult to find a clear structure in this field. To produce more homogeneous studies, open-source access to AI software codes and a consensus on study design should be aimed for. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications on Musculoskeletal Imaging)
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