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Staats K, Kayani B, Haddad FS. The impact of the European Union's Medical Device Regulation on orthopaedic implants, technology, and future innovation. Bone Joint J 2024; 106-B:303-306. [PMID: 38555944 DOI: 10.1302/0301-620x.106b4.bjj-2023-1228.r1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Affiliation(s)
- Kevin Staats
- Department of Trauma and Orthopaedics, University College Hospital, London, UK
| | - Babar Kayani
- Department of Trauma and Orthopaedics, University College Hospital, London, UK
| | - Fares S Haddad
- Department of Trauma and Orthopaedics, University College London NHS Hospitals, London, UK
- Princess Grace Hospital, London, UK
- The NIHR Biomedical Research Centre, UCLH, London, UK
- The Bone & Joint Journal , London, UK
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Mitterer JA, Huber S, Schwarz GM, Simon S, Pallamar M, Kissler F, Frank BJH, Hofstaetter JG. Fully automated assessment of the knee alignment on long leg radiographs following corrective knee osteotomies in patients with valgus or varus deformities. Arch Orthop Trauma Surg 2024; 144:1029-1038. [PMID: 38091069 DOI: 10.1007/s00402-023-05151-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 11/20/2023] [Indexed: 02/28/2024]
Abstract
INTRODUCTION The assessment of the knee alignment on long leg radiographs (LLR) postoperative to corrective knee osteotomies (CKOs) is highly dependent on the reader's expertise. Artificial Intelligence (AI) algorithms may help automate and standardise this process. The study aimed to analyse the reliability of an AI-algorithm for the evaluation of LLRs following CKOs. MATERIALS AND METHODS In this study, we analysed a validation cohort of 110 postoperative LLRs from 102 patients. All patients underwent CKO, including distal femoral (DFO), high tibial (HTO) and bilevel osteotomies. The agreement between manual measurements and the AI-algorithm was assessed for the mechanical axis deviation (MAD), hip knee ankle angle (HKA), anatomical-mechanical-axis-angle (AMA), joint line convergence angle (JLCA), mechanical lateral proximal femur angle (mLPFA), mechanical lateral distal femoral angle (mLDFA), mechanical medial proximal tibia angle (mMPTA) and mechanical lateral distal tibia angle (mLDTA), using the intra-class-correlation (ICC) coefficient between the readers, each reader and the AI and the mean of the manual reads and the AI-algorithm and Bland-Altman Plots between the manual reads and the AI software for the MAD, HKA, mLDFA and mMPTA. RESULTS In the validation cohort, the AI software showed excellent agreement with the manual reads (ICC: 0.81-0.99). The agreement between the readers (Inter-rater) showed excellent correlations (ICC: 0.95-0. The mean difference in the DFO group for the MAD, HKA, mLDFA and mMPTA were 0.50 mm, - 0.12°, 0.55° and 0.15°. In the HTO group the mean difference for the MAD, HKA, mLDFA and mMPTA were 0.36 mm, - 0.17°, 0.57° and 0.08°, respectively. Reliable outputs were generated in 95.4% of the validation cohort. CONCLUSION he application of AI-algorithms for the assessment of lower limb alignment on LLRs following CKOs shows reliable and accurate results. LEVEL OF EVIDENCE Diagnostic Level III.
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Affiliation(s)
- Jennyfer A Mitterer
- Michael Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
| | - Stephanie Huber
- Michael Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
- Center for Anatomy and Cell Biology, Medical University Vienna Speising, Währinger Straße 13, 1090, Vienna, Austria
| | - Gilbert M Schwarz
- Michael Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
- Center for Anatomy and Cell Biology, Medical University Vienna Speising, Währinger Straße 13, 1090, Vienna, Austria
- Department of Orthopaedic and Trauma-Surgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Sebastian Simon
- Michael Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
- 2nd Department, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
| | - Matthias Pallamar
- Department of Pediatric Orthopaedics, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
| | - Florian Kissler
- 1st Department, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
| | - Bernhard J H Frank
- Michael Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
- 2nd Department, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria
| | - Jochen G Hofstaetter
- Michael Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria.
- 2nd Department, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria.
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Powling AS, Lisacek-Kiosoglous AB, Fontalis A, Mazomenos E, Haddad FS. Unveiling the potential of artificial intelligence in orthopaedic surgery. Br J Hosp Med (Lond) 2023; 84:1-5. [PMID: 38153019 DOI: 10.12968/hmed.2023.0258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
Artificial intelligence is paving the way in contemporary medical advances, with the potential to revolutionise orthopaedic surgical care. By harnessing the power of complex algorithms, artificial intelligence yields outputs that have diverse applications including, but not limited to, identifying implants, diagnostic imaging for fracture and tumour recognition, prognostic tools through the use of electronic medical records, assessing arthroplasty outcomes, length of hospital stay and economic costs, monitoring the progress of functional rehabilitation, and innovative surgical training via simulation. However, amid the promising potential and enthusiasm surrounding artificial intelligence, clinicians should understand its limitations, and caution is needed before artificial intelligence-driven tools are introduced to clinical practice.
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Affiliation(s)
- Amber S Powling
- Barts and The London School of Medicine and Dentistry, School of Medicine London, London, UK
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Anthony B Lisacek-Kiosoglous
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Andreas Fontalis
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
- Division of Surgery and Interventional Science, University College London, London, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Evangelos Mazomenos
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Fares S Haddad
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
- Division of Surgery and Interventional Science, University College London, London, UK
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Haddad FS. With gratitude to The Bone & Joint Journal family. Bone Joint J 2023; 105-B:1233-1234. [PMID: 38035594 DOI: 10.1302/0301-620x.105b12.bjj-2023-1123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Affiliation(s)
- Fares S Haddad
- University College London Hospitals, London, UK
- The Princess Grace Hospital, London, UK
- The NIHR Biomedical Research Centre at UCLH, London, UK
- The Bone & Joint Journal , London, UK
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Vera-Garcia DV, Nugen F, Padash S, Khosravi B, Mickley JP, Erickson BJ, Wyles CC, Taunton MJ. Educational Overview of the Concept and Application of Computer Vision in Arthroplasty. J Arthroplasty 2023; 38:1954-1958. [PMID: 37633507 PMCID: PMC10616773 DOI: 10.1016/j.arth.2023.08.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 08/10/2023] [Accepted: 08/11/2023] [Indexed: 08/28/2023] Open
Abstract
Image data has grown exponentially as systems have increased their ability to collect and store it. Unfortunately, there are limits to human resources both in time and knowledge to fully interpret and manage that data. Computer Vision (CV) has grown in popularity as a discipline for better understanding visual data. Computer Vision has become a powerful tool for imaging analytics in orthopedic surgery, allowing computers to evaluate large volumes of image data with greater nuance than previously possible. Nevertheless, even with the growing number of uses in medicine, literature on the fundamentals of CV and its implementation is mainly oriented toward computer scientists rather than clinicians, rendering CV unapproachable for most orthopedic surgeons as a tool for clinical practice and research. The purpose of this article is to summarize and review the fundamental concepts of CV application for the orthopedic surgeon and musculoskeletal researcher.
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Affiliation(s)
- Diana Victoria Vera-Garcia
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Mayo Clinic, Rochester, MN
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN
| | - Fred Nugen
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Mayo Clinic, Rochester, MN
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN
| | - Sirwa Padash
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Mayo Clinic, Rochester, MN
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN
| | - Bardia Khosravi
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Mayo Clinic, Rochester, MN
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN
| | - John P. Mickley
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Mayo Clinic, Rochester, MN
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN
| | - Bradley J. Erickson
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN
| | - Cody C. Wyles
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Mayo Clinic, Rochester, MN
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN
| | - Michael J. Taunton
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Mayo Clinic, Rochester, MN
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN
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Lisacek-Kiosoglous AB, Powling AS, Fontalis A, Gabr A, Mazomenos E, Haddad FS. Artificial intelligence in orthopaedic surgery. Bone Joint Res 2023; 12:447-454. [PMID: 37423607 DOI: 10.1302/2046-3758.127.bjr-2023-0111.r1] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/11/2023] Open
Abstract
The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as 'big data', AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI's limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction.
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Affiliation(s)
- Anthony B Lisacek-Kiosoglous
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Amber S Powling
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
- Barts and The London School of Medicine and Dentistry, School of Medicine London, London, UK
| | - Andreas Fontalis
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
- Division of Surgery and Interventional Science, University College London, London, UK
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Ayman Gabr
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Evangelos Mazomenos
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Fares S Haddad
- Department of Trauma and Orthopaedic Surgery, University College London Hospitals NHS Foundation Trust, London, UK
- Division of Surgery and Interventional Science, University College London, London, UK
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Wilton T, Skinner JA, Haddad FS. Camouflage uncovered: what should happen next? Bone Joint J 2023; 105-B:221-226. [PMID: 36854320 DOI: 10.1302/0301-620x.105b3.bjj-2023-0145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
Recent publications have drawn attention to the fact that some brands of joint replacement may contain variants which perform significantly worse (or better) than their 'siblings'. As a result, the National Joint Registry has performed much more detailed analysis on the larger families of knee arthroplasties in order to identify exactly where these differences may be present and may hitherto have remained hidden. The analysis of the Nexgen knee arthroplasty brand identified that some posterior-stabilized combinations have particularly high revision rates for aseptic loosening of the tibia, and consequently a medical device recall has been issued for the Nexgen 'option' tibial component which was implicated. More elaborate signal detection is required in order to identify such variation in results in a routine fashion if patients are to be protected from such variation in outcomes between closely related implant types.
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Affiliation(s)
| | - John A Skinner
- Institute of Orthopaedics, Royal National Orthopaedic Hospital, London, UK
| | - Fares S Haddad
- University College London Hospitals NHS Foundation Trust, London, UK.,The Bone & Joint Journal , London, UK
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Haddad FS. Looking back over the past year. Bone Joint J 2022; 104-B:1279-1280. [DOI: 10.1302/0301-620x.104b12.bjj-2022-1161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Affiliation(s)
- Fares S. Haddad
- University College London Hospitals, The Princess Grace Hospital, and The NIHR Biomedical Research Centre at UCLH, London, UK
- The Bone & Joint Journal, London, UK
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