Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.2
4.6
Journals
Bioengineering
Special Issues
Radiomics and Artificial Intelligence in the Musculoskeletal System
share announcement

Radiomics and Artificial Intelligence in the Musculoskeletal System

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 3576

Special Issue Editors

Dr. Mingqian Huang

E-Mail Website
Guest Editor
Icahn School of Medicine at Mount Sinai, New York, NY, USA
Interests: medical imaging; artificial intelligence; radiomics; musculoskeletal system; skeletal disorders
Dr. Xueyan Mei

E-Mail Website
Guest Editor
Icahn School of Medicine at Mount Sinai, New York, NY, USA
Interests: deep learning; medical imaging
Dr. Corey K Ho

E-Mail Website
Guest Editor
School of Medicine, University of Colorado Anschutz Medical School, Denver, CO, USA
Interests: musculoskeletal imaging; musculoskeletal intervention; medical imaging; interventional oncology

Special Issue Information

Dear Colleagues,

The musculoskeletal system includes many kinds of tissues such as bones, cartilage, muscles, tendons, and ligaments. Radiomics and artificial intelligence (AI) are two rapidly growing fields that are transforming the way musculoskeletal imaging is being utilized for diagnostic and therapeutic purposes.

In the field of musculoskeletal imaging, radiomics and AI have been used in various applications for detecting bone fractures, diagnosing tumors and cancers, identifying early signs of arthritis and other skeletal disorders, monitoring the progression of joint disease, and predicting treatment outcomes in patients. These technologies are especially useful in complex cases where multiple imaging modalities are used and when traditional clinical assessment methods fall short.

Overall, radiomics and AI are enabling a shift toward personalized medicine by providing clinicians with more accurate and objective tools to diagnose and treat individuals. Additionally, they hold great promise for reducing healthcare costs, improving patient outcomes, and enhancing the quality of care provided to musculoskeletal patients.

This Special Issue will include insights into disciplines including, but not limited to, physiatry, radiology, physical therapy, bioengineering, and movement science. The Guest Editors welcome scholarly contributions regarding screening, diagnosis, measurement, intervention, or analytic approaches (e.g., original research, reviews, etc.) that serve to advance innovative forms of biomedical imaging to address the rehabilitation of musculoskeletal conditions.

Dr. Mingqian Huang
Dr. Xueyan Mei
Dr. Corey K Ho
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • radiomics
  • artificial intelligence
  • musculoskeletal system
  • skeleton disorders

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

18 pages, 7572 KiB  
Communication
Discovery Viewer (DV): Web-Based Medical AI Model Development Platform and Deployment Hub
by Valentin Fauveau, Sean Sun, Zelong Liu, Xueyan Mei, James Grant, Mikey Sullivan, Hayit Greenspan, Li Feng and Zahi A. Fayad
Bioengineering 2023, 10(12), 1396; https://doi.org/10.3390/bioengineering10121396 - 06 Dec 2023
Viewed by 1304
Abstract
The rapid rise of artificial intelligence (AI) in medicine in the last few years highlights the importance of developing bigger and better systems for data and model sharing. However, the presence of Protected Health Information (PHI) in medical data poses a challenge when [...] Read more.
The rapid rise of artificial intelligence (AI) in medicine in the last few years highlights the importance of developing bigger and better systems for data and model sharing. However, the presence of Protected Health Information (PHI) in medical data poses a challenge when it comes to sharing. One potential solution to mitigate the risk of PHI breaches is to exclusively share pre-trained models developed using private datasets. Despite the availability of these pre-trained networks, there remains a need for an adaptable environment to test and fine-tune specific models tailored for clinical tasks. This environment should be open for peer testing, feedback, and continuous model refinement, allowing dynamic model updates that are especially important in the medical field, where diseases and scanning techniques evolve rapidly. In this context, the Discovery Viewer (DV) platform was developed in-house at the Biomedical Engineering and Imaging Institute at Mount Sinai (BMEII) to facilitate the creation and distribution of cutting-edge medical AI models that remain accessible after their development. The all-in-one platform offers a unique environment for non-AI experts to learn, develop, and share their own deep learning (DL) concepts. This paper presents various use cases of the platform, with its primary goal being to demonstrate how DV holds the potential to empower individuals without expertise in AI to create high-performing DL models. We tasked three non-AI experts to develop different musculoskeletal AI projects that encompassed segmentation, regression, and classification tasks. In each project, 80% of the samples were provided with a subset of these samples annotated to aid the volunteers in understanding the expected annotation task. Subsequently, they were responsible for annotating the remaining samples and training their models through the platform’s “Training Module”. The resulting models were then tested on the separate 20% hold-off dataset to assess their performance. The classification model achieved an accuracy of 0.94, a sensitivity of 0.92, and a specificity of 1. The regression model yielded a mean absolute error of 14.27 pixels. And the segmentation model attained a Dice Score of 0.93, with a sensitivity of 0.9 and a specificity of 0.99. This initiative seeks to broaden the community of medical AI model developers and democratize the access of this technology to all stakeholders. The ultimate goal is to facilitate the transition of medical AI models from research to clinical settings. Full article
(This article belongs to the Special Issue Radiomics and Artificial Intelligence in the Musculoskeletal System)
►▼ Show Figures

Figure 1

13 pages, 3011 KiB  
Article
Deep Learning for Automated Measurement of Patellofemoral Anatomic Landmarks
by Zelong Liu, Alexander Zhou, Valentin Fauveau, Justine Lee, Philip Marcadis, Zahi A. Fayad, Jimmy J. Chan, James Gladstone, Xueyan Mei and Mingqian Huang
Bioengineering 2023, 10(7), 815; https://doi.org/10.3390/bioengineering10070815 - 08 Jul 2023
Cited by 2 | Viewed by 1191
Abstract
Background: Patellofemoral anatomy has not been well characterized. Applying deep learning to automatically measure knee anatomy can provide a better understanding of anatomy, which can be a key factor in improving outcomes. Methods: 483 total patients with knee CT imaging (April 2017–May 2022) [...] Read more.
Background: Patellofemoral anatomy has not been well characterized. Applying deep learning to automatically measure knee anatomy can provide a better understanding of anatomy, which can be a key factor in improving outcomes. Methods: 483 total patients with knee CT imaging (April 2017–May 2022) from 6 centers were selected from a cohort scheduled for knee arthroplasty and a cohort with healthy knee anatomy. A total of 7 patellofemoral landmarks were annotated on 14,652 images and approved by a senior musculoskeletal radiologist. A two-stage deep learning model was trained to predict landmark coordinates using a modified ResNet50 architecture initialized with self-supervised learning pretrained weights on RadImageNet. Landmark predictions were evaluated with mean absolute error, and derived patellofemoral measurements were analyzed with Bland–Altman plots. Statistical significance of measurements was assessed by paired t-tests. Results: Mean absolute error between predicted and ground truth landmark coordinates was 0.20/0.26 cm in the healthy/arthroplasty cohort. Four knee parameters were calculated, including transepicondylar axis length, transepicondylar-posterior femur axis angle, trochlear medial asymmetry, and sulcus angle. There were no statistically significant parameter differences (p > 0.05) between predicted and ground truth measurements in both cohorts, except for the healthy cohort sulcus angle. Conclusion: Our model accurately identifies key trochlear landmarks with ~0.20–0.26 cm accuracy and produces human-comparable measurements on both healthy and pathological knees. This work represents the first deep learning regression model for automated patellofemoral annotation trained on both physiologic and pathologic CT imaging at this scale. This novel model can enhance our ability to analyze the anatomy of the patellofemoral compartment at scale. Full article
(This article belongs to the Special Issue Radiomics and Artificial Intelligence in the Musculoskeletal System)
►▼ Show Figures

Figure 1

Review

Jump to: Research

20 pages, 2224 KiB  
Review
The Role of Artificial Intelligence in the Identification and Evaluation of Bone Fractures
by Andrew Tieu, Ezriel Kroen, Yonaton Kadish, Zelong Liu, Nikhil Patel, Alexander Zhou, Alara Yilmaz, Stephanie Lee and Timothy Deyer
Bioengineering 2024, 11(4), 338; https://doi.org/10.3390/bioengineering11040338 - 29 Mar 2024
Viewed by 659
Abstract
Artificial intelligence (AI), particularly deep learning, has made enormous strides in medical imaging analysis. In the field of musculoskeletal radiology, deep-learning models are actively being developed for the identification and evaluation of bone fractures. These methods provide numerous benefits to radiologists such as [...] Read more.
Artificial intelligence (AI), particularly deep learning, has made enormous strides in medical imaging analysis. In the field of musculoskeletal radiology, deep-learning models are actively being developed for the identification and evaluation of bone fractures. These methods provide numerous benefits to radiologists such as increased diagnostic accuracy and efficiency while also achieving standalone performances comparable or superior to clinician readers. Various algorithms are already commercially available for integration into clinical workflows, with the potential to improve healthcare delivery and shape the future practice of radiology. In this systematic review, we explore the performance of current AI methods in the identification and evaluation of fractures, particularly those in the ankle, wrist, hip, and ribs. We also discuss current commercially available products for fracture detection and provide an overview of the current limitations of this technology and future directions of the field. Full article
(This article belongs to the Special Issue Radiomics and Artificial Intelligence in the Musculoskeletal System)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop