Artificial intelligence for automated identification of total shoulder arthroplasty implants

Kunze, Kyle N.; Jang, Seong Jun; Li, T.; Pareek, Ayoosh; Finocchiaro, Anthony; Fu, Michael C.; Taylor, Samuel A.; Dines, Joshua S.; Dines, David M.; Warren, Russell F.; Gulotta, Lawrence V.

doi:10.1016/j.jse.2023.03.028

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…Although DICOM tags are usually used to record imaging information, they can be subject to manual recording errors and may not include information that can be directly useful for other purposes. A number of different AI-based classification algorithms have been created in recent years to recognize different important features of orthopaedic imaging data, such as imaging view, implant existence and implant type [21,27,41].…”

Section: Unetmentioning

confidence: 99%

“…Recent studies have demonstrated that deep learning classification models can be trained to predict the manufacturer of surgical implants in postoperative radiographs. For example, a study created a deep learning model to identify 22 unique TSA implants from 8 manufacturers [21]. This model achieved a high accuracy of 97.1% with a high inference speed of 0.079 s per image.…”

Section: Concordance Index (C-index)mentioning

confidence: 99%

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A practical guide to the development and deployment of deep learning models for the orthopaedic surgeon: Part III, focus on registry creation, diagnosis, and data privacy

Oeding,

Yang,

Sanchez‐Sotelo

et al. 2024

Knee surg. sports traumatol. arthrosc.

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Deep learning is a subset of artificial intelligence (AI) with enormous potential to transform orthopaedic surgery. As has already become evident with the deployment of Large Language Models (LLMs) like ChatGPT (OpenAI Inc.), deep learning can rapidly enter clinical and surgical practices. As such, it is imperative that orthopaedic surgeons acquire a deeper understanding of the technical terminology, capabilities and limitations associated with deep learning models. The focus of this series thus far has been providing surgeons with an overview of the steps needed to implement a deep learning‐based pipeline, emphasizing some of the important technical details for surgeons to understand as they encounter, evaluate or lead deep learning projects. However, this series would be remiss without providing practical examples of how deep learning models have begun to be deployed and highlighting the areas where the authors feel deep learning may have the most profound potential. While computer vision applications of deep learning were the focus of Parts I and II, due to the enormous impact that natural language processing (NLP) has had in recent months, NLP‐based deep learning models are also discussed in this final part of the series. In this review, three applications that the authors believe can be impacted the most by deep learning but with which many surgeons may not be familiar are discussed: (1) registry construction, (2) diagnostic AI and (3) data privacy. Deep learning‐based registry construction will be essential for the development of more impactful clinical applications, with diagnostic AI being one of those applications likely to augment clinical decision‐making in the near future. As the applications of deep learning continue to grow, the protection of patient information will become increasingly essential; as such, applications of deep learning to enhance data privacy are likely to become more important than ever before.Level of Evidence: Level IV.

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Section: Unetmentioning

confidence: 99%

Section: Concordance Index (C-index)mentioning

confidence: 99%

A practical guide to the development and deployment of deep learning models for the orthopaedic surgeon: Part III, focus on registry creation, diagnosis, and data privacy

Oeding,

Yang,

Sanchez‐Sotelo

et al. 2024

Knee surg. sports traumatol. arthrosc.

Self Cite

View full text Add to dashboard Cite

show abstract

“…A few contemporary examples of these uses are highlighted below. Kunze et al [37] created a hybrid database of 3060 images combined from the Hospital for Special Surgery and the University of Washington public shoulder arthroplasty implant repository. They subsequently developed novel deep-learning models to automate shoulder arthroplasty implant identification.…”

Section: Contemporary Uses Of Registriesmentioning

confidence: 99%

Artificial intelligence (AI) and large data registries: Understanding the advantages and limitations of contemporary data sets for use in AI research

Kunze,

Williams,

Ranawat

et al. 2024

Knee surg. sports traumatol. arthrosc.

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“…The application of artificial intelligence (AI) and machine learning (ML) models in rehabilitation has become increasingly important for different purposes, such as classification, prediction, and the development of personalized treatment plans, as well as the enhancement of diagnostic accuracy [44][45][46][47][48]. These not only enhance treatment effectiveness but also facilitate more efficient and cost-effective care.…”

Section: Artificial Intelligencementioning

confidence: 99%

Advanced Home-Based Shoulder Rehabilitation: A Systematic Review of Remote Monitoring Devices and Their Therapeutic Efficacy

Sassi,

Villa Corta,

Pisani

et al. 2024

Sensors

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Shoulder pain represents the most frequently reported musculoskeletal disorder, often leading to significant functional impairment and pain, impacting quality of life. Home-based rehabilitation programs offer a more accessible and convenient solution for an effective shoulder disorder treatment, addressing logistical and financial constraints associated with traditional physiotherapy. The aim of this systematic review is to report the monitoring devices currently proposed and tested for shoulder rehabilitation in home settings. The research question was formulated using the PICO approach, and the PRISMA guidelines were applied to ensure a transparent methodology for the systematic review process. A comprehensive search of PubMed and Scopus was conducted, and the results were included from 2014 up to 2023. Three different tools (i.e., the Rob 2 version of the Cochrane risk-of-bias tool, the Joanna Briggs Institute (JBI) Critical Appraisal tool, and the ROBINS-I tool) were used to assess the risk of bias. Fifteen studies were included as they fulfilled the inclusion criteria. The results showed that wearable systems represent a promising solution as remote monitoring technologies, offering quantitative and clinically meaningful insights into the progress of individuals within a rehabilitation pathway. Recent trends indicate a growing use of low-cost, non-intrusive visual tracking devices, such as camera-based monitoring systems, within the domain of tele-rehabilitation. The integration of home-based monitoring devices alongside traditional rehabilitation methods is acquiring significant attention, offering broader access to high-quality care, and potentially reducing healthcare costs associated with in-person therapy.

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Artificial intelligence for automated identification of total shoulder arthroplasty implants

Cited by 10 publications

References 22 publications

A practical guide to the development and deployment of deep learning models for the orthopaedic surgeon: Part III, focus on registry creation, diagnosis, and data privacy

A practical guide to the development and deployment of deep learning models for the orthopaedic surgeon: Part III, focus on registry creation, diagnosis, and data privacy

Artificial intelligence (AI) and large data registries: Understanding the advantages and limitations of contemporary data sets for use in AI research

Advanced Home-Based Shoulder Rehabilitation: A Systematic Review of Remote Monitoring Devices and Their Therapeutic Efficacy

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