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Bioengineering
Special Issues
Artificial Intelligence and Machine Learning in Spine Research
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Artificial Intelligence and Machine Learning in Spine Research

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 2123

Special Issue Editor

Dr. Min Cheol Chang

E-Mail Website
Guest Editor
Department of Physical Medicine & Rehabilitation, College of Medicine, Yeungnam University, Taegu, Republic of Korea
Interests: rehabilitation; image; neurological disorder; muculoskeletal disorder
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Spinal disorders are a prevalent health concern. More than 80% of the general population experience low back pain at least once in their lifetime. As individuals age, degenerative changes occur in the spine and intervertebral discs. These degenerative changes induce pain and cause significant discomfort in daily life. Furthermore, they may result in damage to the spinal nerves and nerve roots, potentially leading to both pain and paralysis. In recent years, the prevalence of spinal disorders among young adults in their 20s and 30s has significantly increased due to a variety of factors, such as the excessive use of smartphones and tablets, poor lifestyle choices, sedentary lifestyle, elevated stress levels, and a lack of exercise due to demanding work or academic commitments. To accurately diagnose spinal disorders and provide more effective treatment, researchers and clinicians have conducted extensive research.

Recent advancements in artificial intelligence (AI) and machine learning (ML) have led to significant changes in research methodologies and environments. AI and ML enable the processing of big data and effective data analysis, even when confronted with numerous confounders. Moreover, these technologies have revolutionized image analysis, a feat that was not previously attainable through traditional statistical methods. AI and ML are also integral to various aspects of spinal research and hold the potential to enhance treatment outcomes by facilitating a more accurate diagnosis of spinal disorders and predictions of prognosis. In this Special Issue, we will delve into the current applications of AI and ML in spinal research. We hope that this Special Issue will advance spinal research further and assist researchers in identifying promising research avenues.

Dr. Min Cheol Chang
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. 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

  • spinal disorder
  • artificial intelligence
  • machine learning

Published Papers (2 papers)

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Research

13 pages, 3996 KiB  
Article
Deep Learning Method for Precise Landmark Identification and Structural Assessment of Whole-Spine Radiographs
by Sung Hyun Noh, Gaeun Lee, Hyun-Jin Bae, Ju Yeon Han, Su Jeong Son, Deok Kim, Jeong Yeon Park, Seung Kyeong Choi, Pyung Goo Cho, Sang Hyun Kim, Woon Tak Yuh, Su Hun Lee, Bumsoo Park, Kwang-Ryeol Kim, Kyoung-Tae Kim and Yoon Ha
Bioengineering 2024, 11(5), 481; https://doi.org/10.3390/bioengineering11050481 - 11 May 2024
Viewed by 599
Abstract
This study measured parameters automatically by marking the point for measuring each parameter on whole-spine radiographs. Between January 2020 and December 2021, 1017 sequential lateral whole-spine radiographs were retrospectively obtained. Of these, 819 and 198 were used for training and testing the performance [...] Read more.
This study measured parameters automatically by marking the point for measuring each parameter on whole-spine radiographs. Between January 2020 and December 2021, 1017 sequential lateral whole-spine radiographs were retrospectively obtained. Of these, 819 and 198 were used for training and testing the performance of the landmark detection model, respectively. To objectively evaluate the program’s performance, 690 whole-spine radiographs from four other institutions were used for external validation. The combined dataset comprised radiographs from 857 female and 850 male patients (average age 42.2 ± 27.3 years; range 20–85 years). The landmark localizer showed the highest accuracy in identifying cervical landmarks (median error 1.5–2.4 mm), followed by lumbosacral landmarks (median error 2.1–3.0 mm). However, thoracic landmarks displayed larger localization errors (median 2.4–4.3 mm), indicating slightly reduced precision compared with the cervical and lumbosacral regions. The agreement between the deep learning model and two experts was good to excellent, with intraclass correlation coefficient values >0.88. The deep learning model also performed well on the external validation set. There were no statistical differences between datasets in all parameters, suggesting that the performance of the artificial intelligence model created was excellent. The proposed automatic alignment analysis system identified anatomical landmarks and positions of the spine with high precision and generated various radiograph imaging parameters that had a good correlation with manual measurements. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Spine Research)
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15 pages, 3151 KiB  
Article
Automatic Osteoporosis Screening System Using Radiomics and Deep Learning from Low-Dose Chest CT Images
by Xiaoyu Tong, Shigeng Wang, Jingyi Zhang, Yong Fan, Yijun Liu and Wei Wei
Bioengineering 2024, 11(1), 50; https://doi.org/10.3390/bioengineering11010050 - 2 Jan 2024
Viewed by 1122
Abstract
Objective: Develop two fully automatic osteoporosis screening systems using deep learning (DL) and radiomics (Rad) techniques based on low-dose chest CT (LDCT) images and evaluate their diagnostic effectiveness. Methods: In total, 434 patients who underwent LDCT and bone mineral density (BMD) examination were [...] Read more.
Objective: Develop two fully automatic osteoporosis screening systems using deep learning (DL) and radiomics (Rad) techniques based on low-dose chest CT (LDCT) images and evaluate their diagnostic effectiveness. Methods: In total, 434 patients who underwent LDCT and bone mineral density (BMD) examination were retrospectively enrolled and divided into the development set (n = 333) and temporal validation set (n = 101). An automatic thoracic vertebra cancellous bone (TVCB) segmentation model was developed. The Dice similarity coefficient (DSC) was used to evaluate the segmentation performance. Furthermore, the three-class Rad and DL models were developed to distinguish osteoporosis, osteopenia, and normal bone mass. The diagnostic performance of these models was evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: The automatic segmentation model achieved excellent segmentation performance, with a mean DSC of 0.96 ± 0.02 in the temporal validation set. The Rad model was used to identify osteoporosis, osteopenia, and normal BMD in the temporal validation set, with respective area under the receiver operating characteristic curve (AUC) values of 0.943, 0.801, and 0.932. The DL model achieved higher AUC values of 0.983, 0.906, and 0.969 for the same categories in the same validation set. The Delong test affirmed that both models performed similarly in BMD assessment. However, the accuracy of the DL model is 81.2%, which is better than the 73.3% accuracy of the Rad model in the temporal validation set. Additionally, DCA indicated that the DL model provided a greater net benefit compared to the Rad model across the majority of the reasonable threshold probabilities Conclusions: The automated segmentation framework we developed can accurately segment cancellous bone on low-dose chest CT images. These predictive models, which are based on deep learning and radiomics, provided comparable diagnostic performance in automatic BMD assessment. Nevertheless, it is important to highlight that the DL model demonstrates higher accuracy and precision than the Rad model. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Spine Research)
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