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- Oeding, Jacob F., et al.
(författare)
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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
- 2024
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Ingår i: KNEE SURGERY SPORTS TRAUMATOLOGY ARTHROSCOPY. - 0942-2056 .- 1433-7347. ; 32:3, s. 518-528
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Forskningsöversikt (refereegranskat)abstract
- 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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| 2. |
- Svantesson, Eleonor, et al.
(författare)
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Clinical Outcomes After Anterior Cruciate Ligament Injury: Panther Symposium ACL Injury Clinical Outcomes Consensus Group.
- 2020
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Ingår i: Orthopaedic journal of sports medicine. - 2325-9671. ; 8:7
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Tidskriftsartikel (refereegranskat)abstract
- A stringent outcome assessment is a key aspect of establishing evidence-based clinical guidelines for anterior cruciate ligament (ACL) injury treatment. To establish a standardized assessment of clinical outcome after ACL treatment, a consensus meeting including a multidisciplinary group of ACL experts was held at the ACL Consensus Meeting Panther Symposium, Pittsburgh, Pennsylvania, USA, in June 2019. The aim was to establish a consensus on what data should be reported when conducting an ACL outcome study, what specific outcome measurements should be used, and at what follow-up time those outcomes should be assessed. The group reached consensus on 9 statements by using a modified Delphi method. In general, outcomes after ACL treatment can be divided into 4 robust categories: early adverse events, patient-reported outcomes (PROs), ACL graft failure/recurrent ligament disruption, and clinical measures of knee function and structure. A comprehensive assessment after ACL treatment should aim to provide a complete overview of the treatment result, optimally including the various aspects of outcome categories. For most research questions, a minimum follow-up of 2 years with an optimal follow-up rate of 80% is necessary to achieve a comprehensive assessment. This should include clinical examination, any sustained reinjuries, validated knee-specific PROs, and health-related quality of life questionnaires. In the midterm to long-term follow-up, the presence of osteoarthritis should be evaluated. This consensus paper provides practical guidelines for how the aforementioned entities of outcomes should be reported and suggests the preferred tools for a reliable and valid assessment of outcome after ACL treatment.
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