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Bioengineering
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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 May 2025 | Viewed by 14680

Special Issue Editor

Dr. Min Cheol Chang

E-Mail Website
Guest Editor
Department of Physical Medicine & Rehabilitation, College of Medicine, Yeungnam University, Taegu, 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

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Published Papers (7 papers)

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Editorial

Jump to: Research, Review

4 pages, 183 KiB  
Editorial
Artificial Intelligence and Machine Learning in Spine Research: A New Frontier
by Min Cheol Chang
Bioengineering 2024, 11(9), 915; https://doi.org/10.3390/bioengineering11090915 - 13 Sep 2024
Cited by 2 | Viewed by 1582
Abstract
Artificial Intelligence (AI) refers to the creation of computer systems capable of performing tasks typically requiring human intelligence [...] Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Spine Research)

Research

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13 pages, 2454 KiB  
Article
Deep Learning in Spinal Endoscopy: U-Net Models for Neural Tissue Detection
by Hyung Rae Lee, Wounsuk Rhee, Sam Yeol Chang, Bong-Soon Chang and Hyoungmin Kim
Bioengineering 2024, 11(11), 1082; https://doi.org/10.3390/bioengineering11111082 - 29 Oct 2024
Cited by 1 | Viewed by 1472
Abstract
Biportal endoscopic spine surgery (BESS) is minimally invasive and therefore benefits both surgeons and patients. However, concerning complications include dural tears and neural tissue injuries. In this study, we aimed to develop a deep learning model for neural tissue segmentation to enhance the [...] Read more.
Biportal endoscopic spine surgery (BESS) is minimally invasive and therefore benefits both surgeons and patients. However, concerning complications include dural tears and neural tissue injuries. In this study, we aimed to develop a deep learning model for neural tissue segmentation to enhance the safety and efficacy of endoscopic spinal surgery. We used frames extracted from videos of 28 endoscopic spine surgeries, comprising 2307 images for training and 635 images for validation. A U-Net-like architecture is employed for neural tissue segmentation. Quantitative assessments include the Dice-Sorensen coefficient, Jaccard index, precision, recall, average precision, and image-processing time. Our findings revealed that the best-performing model achieved a Dice-Sorensen coefficient of 0.824 and a Jaccard index of 0.701. The precision and recall values were 0.810 and 0.839, respectively, with an average precision of 0.890. The model processed images at 43 ms per frame, equating to 23.3 frames per second. Qualitative evaluations indicated the effective identification of neural tissue features. Our U-Net-based model robustly performed neural tissue segmentation, indicating its potential to support spine surgeons, especially those with less experience, and improve surgical outcomes in endoscopic procedures. Therefore, further advancements may enhance the clinical applicability of this technique. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Spine Research)
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15 pages, 3855 KiB  
Article
Evaluation of Patients’ Levels of Walking Independence Using Inertial Sensors and Neural Networks in an Acute-Care Hospital
by Tatsuya Sugimoto, Nobuhito Taniguchi, Ryoto Yoshikura, Hiroshi Kawaguchi and Shintaro Izumi
Bioengineering 2024, 11(6), 544; https://doi.org/10.3390/bioengineering11060544 - 26 May 2024
Viewed by 1409
Abstract
This study aimed to evaluate walking independence in acute-care hospital patients using neural networks based on acceleration and angular velocity from two walking tests. Forty patients underwent the 10-m walk test and the Timed Up-and-Go test at normal speed, with or without a [...] Read more.
This study aimed to evaluate walking independence in acute-care hospital patients using neural networks based on acceleration and angular velocity from two walking tests. Forty patients underwent the 10-m walk test and the Timed Up-and-Go test at normal speed, with or without a cane. Physiotherapists divided the patients into two groups: 24 patients who were monitored or independent while walking with a cane or without aids in the ward, and 16 patients who were not. To classify these groups, the Transformer model analyzes the left gait cycle data from eight inertial sensors. The accuracy using all the sensor data was 0.836. When sensor data from the right ankle, right wrist, and left wrist were excluded, the accuracy decreased the most. When analyzing the data from these three sensors alone, the accuracy was 0.795. Further reducing the number of sensors to only the right ankle and wrist resulted in an accuracy of 0.736. This study demonstrates the potential of a neural network-based analysis of inertial sensor data for clinically assessing a patient’s level of walking independence. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Spine Research)
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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
Cited by 2 | Viewed by 2449
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
Cited by 3 | Viewed by 2106
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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Review

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13 pages, 534 KiB  
Review
Scoping Review of Machine Learning and Patient-Reported Outcomes in Spine Surgery
by Christian Quinones, Deepak Kumbhare, Bharat Guthikonda and Stanley Hoang
Bioengineering 2025, 12(2), 125; https://doi.org/10.3390/bioengineering12020125 - 29 Jan 2025
Viewed by 934
Abstract
Machine learning is an evolving branch of artificial intelligence that is being applied in neurosurgical research. In spine surgery, machine learning has been used for radiographic characterization of cranial and spinal pathology and in predicting postoperative outcomes such as complications, functional recovery, and [...] Read more.
Machine learning is an evolving branch of artificial intelligence that is being applied in neurosurgical research. In spine surgery, machine learning has been used for radiographic characterization of cranial and spinal pathology and in predicting postoperative outcomes such as complications, functional recovery, and pain relief. A relevant application is the investigation of patient-reported outcome measures (PROMs) after spine surgery. Although a multitude of PROMs have been described and validated, there is currently no consensus regarding which questionnaires should be utilized. Additionally, studies have reported varying degrees of accuracy in predicting patient outcomes based on questionnaire responses. PROMs currently lack standardization, which renders them difficult to compare across studies. The purpose of this manuscript is to identify applications of machine learning to predict PROMs after spine surgery. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Spine Research)
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30 pages, 1331 KiB  
Review
Applications of Artificial Intelligence and Machine Learning in Spine MRI
by Aric Lee, Wilson Ong, Andrew Makmur, Yong Han Ting, Wei Chuan Tan, Shi Wei Desmond Lim, Xi Zhen Low, Jonathan Jiong Hao Tan, Naresh Kumar and James T. P. D. Hallinan
Bioengineering 2024, 11(9), 894; https://doi.org/10.3390/bioengineering11090894 - 5 Sep 2024
Cited by 2 | Viewed by 3269
Abstract
Diagnostic imaging, particularly MRI, plays a key role in the evaluation of many spine pathologies. Recent progress in artificial intelligence and its subset, machine learning, has led to many applications within spine MRI, which we sought to examine in this review. A literature [...] Read more.
Diagnostic imaging, particularly MRI, plays a key role in the evaluation of many spine pathologies. Recent progress in artificial intelligence and its subset, machine learning, has led to many applications within spine MRI, which we sought to examine in this review. A literature search of the major databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search yielded 1226 results, of which 50 studies were selected for inclusion. Key data from these studies were extracted. Studies were categorized thematically into the following: Image Acquisition and Processing, Segmentation, Diagnosis and Treatment Planning, and Patient Selection and Prognostication. Gaps in the literature and the proposed areas of future research are discussed. Current research demonstrates the ability of artificial intelligence to improve various aspects of this field, from image acquisition to analysis and clinical care. We also acknowledge the limitations of current technology. Future work will require collaborative efforts in order to fully exploit new technologies while addressing the practical challenges of generalizability and implementation. In particular, the use of foundation models and large-language models in spine MRI is a promising area, warranting further research. Studies assessing model performance in real-world clinical settings will also help uncover unintended consequences and maximize the benefits for patient care. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Spine Research)
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