| 1. |
- Oeding, J. F., et al.
(author)
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A practical guide to the development and deployment of deep learning models for the Orthopedic surgeon: part I
- 2023
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In: Knee Surgery Sports Traumatology Arthroscopy. - : Springer Science and Business Media LLC. - 0942-2056 .- 1433-7347. ; 31:2, s. 382-389
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Research review (peer-reviewed)abstract
- Deep learning has a profound impact on daily life. As Orthopedics makes use of this rapid escalation in technology, Orthopedic surgeons will need to take leadership roles on deep learning projects. Moreover, surgeons must possess an understanding of what is necessary to design and implement deep learning-based project pipelines. This review provides a practical guide for the Orthopedic surgeon to understand the steps needed to design, develop, and deploy a deep learning pipeline for clinical applications. A detailed description of the processes involved in defining the problem, building the team, acquiring and curating the data, labeling the data, establishing the ground truth, pre-processing and augmenting the data, and selecting the required hardware is provided. In addition, an overview of unique considerations involved in the training and evaluation of deep learning models is provided. This review strives to provide surgeons with the groundwork needed to identify gaps in the clinical landscape that deep learning models may be able to fill and equips them with the knowledge needed to lead an interdisciplinary team through the process of creating novel deep-learning-based solutions to fill those gaps.
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| 2. |
- Pruneski, J. A., et al.
(author)
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Natural language processing: using artificial intelligence to understand human language in orthopedics
- 2023
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In: Knee Surgery Sports Traumatology Arthroscopy. - : Springer Science and Business Media LLC. - 0942-2056 .- 1433-7347. ; 31:4, s. 1203-1211
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Journal article (peer-reviewed)abstract
- Natural language processing (NLP) describes the broad field of artificial intelligence by which computers are trained to understand and generate human language. Within healthcare research, NLP is commonly used for variable extraction and classification/cohort identification tasks. While these tools are becoming increasingly popular and available as both open-source and commercial products, there is a paucity of the literature within the orthopedic space describing the key tasks within these powerful pipelines. Curation and navigation of the electronic medical record are becoming increasingly onerous, and it is important for physicians and other healthcare professionals to understand potential methods of harnessing this large data resource. The purpose of this study is to provide an overview of the tasks required to develop an NLP pipeline for orthopedic research and present recent examples of successful implementations.
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| 3. |
- Eckhardt, C. M., et al.
(author)
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Unsupervised machine learning methods and emerging applications in healthcare
- 2023
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In: Knee Surgery Sports Traumatology Arthroscopy. - : Springer Science and Business Media LLC. - 0942-2056 .- 1433-7347. ; 31:2, s. 376-381
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Research review (peer-reviewed)abstract
- Unsupervised machine learning methods are important analytical tools that can facilitate the analysis and interpretation of high-dimensional data. Unsupervised machine learning methods identify latent patterns and hidden structures in high-dimensional data and can help simplify complex datasets. This article provides an overview of key unsupervised machine learning techniques including K-means clustering, hierarchical clustering, principal component analysis, and factor analysis. With a deeper understanding of these analytical tools, unsupervised machine learning methods can be incorporated into health sciences research to identify novel risk factors, improve prevention strategies, and facilitate delivery of personalized therapies and targeted patient care.
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| 4. |
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| 5. |
- Khorana, A., et al.
(author)
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Choosing the appropriate measure of central tendency: mean, median, or mode?
- 2023
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In: Knee Surgery Sports Traumatology Arthroscopy. - : Springer Science and Business Media LLC. - 0942-2056 .- 1433-7347. ; 31:1, s. 12-15
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Journal article (peer-reviewed)abstract
- Mean, median, and mode are among the most basic and consistently used measures of central tendency in statistical analysis and are crucial for simplifying data sets to a single value. However, there is a lack of understanding of when to use each metric and how various factors can impact these values. The aim of this article is to clarify some of the confusion related to each measure and explain how to select the appropriate metric for a given data set. The authors present this work as an educational resource, ensuring that these common statistical concepts are better understood throughout the Orthopedic research community.
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| 6. |
- Kunze, K. N., et al.
(author)
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A guide to appropriately planning and conducting meta-analyses: part 2-effect size estimation, heterogeneity and analytic approaches
- 2023
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In: Knee Surgery Sports Traumatology Arthroscopy. - : Springer Science and Business Media LLC. - 0942-2056 .- 1433-7347. ; 31:5, s. 1629-1634
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Research review (peer-reviewed)abstract
- Meta-analyses by definition are a subtype of systematic review intended to quantitatively assess the strength of evidence present on an intervention or treatment. Such analyses may use individual-level data or aggregate data to produce a point estimate of an effect, also known as the combined effect, and measure precision of the calculated estimate. The current article will review several important considerations during the analytic phase of a meta-analysis, including selection of effect estimators, heterogeneity and various sub-types of meta-analytic approaches.
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| 7. |
- Pruneski, J. A., et al.
(author)
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Supervised machine learning and associated algorithms: applications in orthopedic surgery
- 2023
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In: Knee Surgery Sports Traumatology Arthroscopy. - : Springer Science and Business Media LLC. - 0942-2056 .- 1433-7347. ; 31:4, s. 1196-1202
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Journal article (peer-reviewed)abstract
- Supervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or negative cases with a known ground truth. Supervised learning describes a spectrum of techniques, ranging from traditional regression modeling to more complex tree boosting, which are becoming increasingly prevalent as the focus on "big data" develops. While these tools are becoming increasingly popular and powerful, there is a paucity of literature available that describe the strengths and limitations of these different modeling techniques. Typically, there is no formal training for health care professionals in the use of machine learning models. As machine learning applications throughout medicine increase, it is important that physicians and other health care professionals better understand the processes underlying application of these techniques. The purpose of this study is to provide an overview of commonly used supervised learning techniques with recent case examples within the orthopedic literature. An additional goal is to address disparities in the understanding of these methods to improve communication within and between research teams.
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| 8. |
- Varady, N. H., et al.
(author)
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Multivariable regression: understanding one of medicine's most fundamental statistical tools
- 2023
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In: Knee Surgery Sports Traumatology Arthroscopy. - : Springer Science and Business Media LLC. - 0942-2056 .- 1433-7347. ; 31:1, s. 7-11
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Journal article (peer-reviewed)abstract
- Multivariable regression is a fundamental tool that drives observational research in orthopaedic surgery. However, regression analyses are not always implemented correctly. This study presents a basic overview of regression analyses and reviews frequent points of confusion. Topics include linear, logistic, and time-to-event regressions, causal inference, confounders, overfitting, missing data, multicollinearity, interactions, and key differences between multivariable versus multivariate regression. The goal is to provide clarity regarding the use and interpretation of multivariable analyses for those attempting to increase their statistical literacy in orthopaedic research.
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