Image-Guided Tethering Spine Surgery With Outcome Prediction Using Spatio-Temporal Dynamic Networks

Mandel, William J.; Oulbacha, Reda; Roy‐Beaudry, Marjolaine; Parent, Stefan; Kadoury, Samuel

doi:10.1109/tmi.2020.3030741

Cited by 9 publications

(8 citation statements)

References 40 publications

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“…The Ghosh et al [53] paper is excluded from Figure 5b because it uses FE-based mechanoregulatory as input and outputs a prediction of major tissue growth on a macrotexture-optimized implant design after 120 days of healing time. Mandel et al [32] use 695 samples from a dataset with 3D spine models to predict growth and therefore exclude them from Figure 5b.…”

Section: Discussionmentioning

confidence: 99%

“…Due in part to the recent development of open software in machine learning libraries, the use of ADAM optimizer was used in 7 cases [29][30][31][32][33][34][35], Pytorch in 5 cases [29,33,[35][36][37], Numpy in 2 cases [38,39], TensorFlow in 2 cases [30,40], and OpenCV in 2 cases [41,42]. (By considering the mean square and mean of the gradient as first and second-order moments, weights can be updated on an appropriate scale for each parameter.)…”

Section: Softwarementioning

confidence: 99%

“…This is because our study does not focus on specific machine learning techniques, making the comparison of loss functions less effective. The most commonly used loss functions in the papers reviewed in our study are L1 loss in 8 papers [31,[34][35][36][37]47,59,78], MSE in 5 papers [29,39,40,53,66], and Dice loss in 4 papers [32,33,37,74]. We also reviewed learning algorithms that do not use loss functions, such as PLS and PLSR [68,69], as well as those where the loss function is not mathematically defined but only verbally explained, as in Y-H Chang et al [42].…”

Section: Comparison With Previous Studies and Limitationsmentioning

confidence: 99%

“…To improve the accuracy of plant growth prediction, Kim et al [35] proposed a novel loss function for combining STN loss with RGB and shape loss. Meanwhile, other studies have introduced loss functions that minimize the geodesic distance between the sample and the regressed curve and metrics produced from simple distance spaces [32]. As complex geometry data become more prevalent, the adaptation of machine learning models to handle such data and the derivation of complex losses by combining existing ones will continue to evolve.…”

Section: Implications For Future Researchmentioning

confidence: 99%

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Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

2023

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Growth prediction technology is not only a practical application but also a crucial approach that strengthens the safety of image processing techniques. By supplementing the growth images obtained from the original images, especially in insufficient data sets, we can increase the robustness of machine learning. Therefore, predicting the growth of living organisms is an important technology that increases the safety of existing applications that target living organisms and can extend to areas not yet realized. This paper is a systematic literature review (SLR) investigating biological growth prediction based on the PRISMA 2020 guidelines. We systematically survey existing studies from 2017 to 2022 to provide other researchers with current trends. We searched four digital libraries—IEEE Xplore, ACM Digital Library, Science Direct, and Web of Science—and finally analyzed 47 articles. We summarize the methods used, year, features, accuracy, and dataset of each paper. In particular, we explained LSTM, GAN, and STN, the most frequently used methods among the 20 papers related to machine learning (40% of all papers).

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Section: Discussionmentioning

confidence: 99%

Section: Softwarementioning

confidence: 99%

Section: Comparison With Previous Studies and Limitationsmentioning

confidence: 99%

Section: Implications For Future Researchmentioning

confidence: 99%

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Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

2023

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“…Furthermore, the surgical procedure can be long and tedious, causing mental and physical exhaustion in the orthopaedic surgeon. As a result of these factors, spinal surgery may be an ideal candidate for a combination of navigation and robotic-assisted surgery [53,54].…”

Section: The Clinical Application Of Robotic Technology In Spinal Sur...mentioning

confidence: 99%

A Bibliometric Analysis: The Application of Robotic Technology in Spine Surgery in the Past 30 Years

Zhou,

Ma,

Wei

et al. 2024

J Biomed Res Environ Sci

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Purpose: The aim of this study is to comprehensively analyze the application of robotic technology in spine surgery and display the current research status in this field. Methods: To investigate the distribution and characteristics of robotics in spinal surgery, the publications in PubMed and web of science was examined and analyzed. We utilized CiteSpace and VOSviewer to review and visualize academic literature from the previous 30 years on the use of robotic technology in spine surgery. Results: According to the findings of the research, it indicates that robotics is becoming more widespread in spine surgery, with robotics playing an important role in pedicle screw placement, intraoperative navigation, imaging guidance, and minimizing radiation exposure for surgeons and surgical teams. how to establish a set of standards, how to reduce personnel radiation exposure in the operating room, postoperative follow-up between traditional and robotic surgery, and the prognosis and incidence of complications such as spondylosis, and so on are all topics that will be discussed. Conclusion: The study looked into the recovery time following robotic surgery, the expense of hospitalization, the robotic technology in terms of time during surgical procedures, blood loss, and implant placement accuracy, which are all topics that will be investigated more in the future. While there are some discrepancies in government, physician, and patient views, expectations, and clinical data for robotic surgery, robots will increasingly be used in areas that demand great precision. The application of robotic technology in spine surgery will be extensive.

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Intra-operative Forecasting of Standing Spine Shape with Articulated Neural Kernel Fields

Thibeault,

Parent,

Kadoury

2023

Lecture Notes in Computer Science

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Image-Guided Tethering Spine Surgery With Outcome Prediction Using Spatio-Temporal Dynamic Networks

Cited by 9 publications

References 40 publications

Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

A Bibliometric Analysis: The Application of Robotic Technology in Spine Surgery in the Past 30 Years

Intra-operative Forecasting of Standing Spine Shape with Articulated Neural Kernel Fields

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