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Prosthesis
Special Issues
Joint Prostheses: Innovations in Shoulder, Hip, and Knee Replacement
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Joint Prostheses: Innovations in Shoulder, Hip, and Knee Replacement

A Special Issue of Prosthesis (ISSN 2673-1592).

Deadline for manuscript submissions: 30 November 2026 | Viewed by 2164

Editor

Dr. Mihai Lazar Mioc

E-Mail Website
Guest Editor
Department XV Orthopedics and Traumatology, Victor Babes University of Medicine and Pharmacy, 2 Eftimie Murgu Square, 300041 Timisoara, Romania
Interests: knee arthroplasty; hip arthroplasty; perioperative optimization

Special Issue Information

Dear Colleagues,

Joint replacement procedures have evolved significantly in recent years, with advances in implant design, surgical techniques, navigation, and biomaterials contributing to improved outcomes and patient satisfaction. As the demand for shoulder, hip, and knee arthroplasty grows globally, continuous innovation is essential to address challenges related to longevity, function, and revision surgery.

This Special Issue aims to highlight recent innovations in joint prostheses for the shoulder, hip, and knee, focusing on technologies, materials, surgical strategies, and clinical outcomes. The goal is to bring together contributions that advance our understanding of prosthetic reconstruction and align with the interdisciplinary scope of Prosthesis.

Suggest themes.

Implant design and fixation in joint replacement

Prosthetic innovations in shoulder, hip, and knee arthroplasty

Robotic and computer-assisted joint replacement

Patient-specific and 3D-printed prostheses

Personalized approaches and data-driven surgical planning

I look forward to receiving your contributions.

Dr. Mihai Lazar Mioc
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Prosthesis is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • joint arthroplasty
  • implant design
  • revision arthroplasty
  • robotic-assisted arthroplasty
  • patient-specific implants
  • outcomes in joint replacement

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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22 pages, 53305 KB  
Article
Superior In Vitro Osteo-Supportive Properties of Trabecular Titanium vs. Chromium–Cobalt Scaffolds
by Andrea Massimiliano Nebuloni, Roberta Lauro, Michela Maria Taiana, Gaetano Sorano, Piero Costa, Enrico Ragni and Laura de Girolamo
Prosthesis 2026, 8(7), 70; https://doi.org/10.3390/prosthesis8070070 - 1 Jul 2026
Viewed by 576
Abstract
Background: Degenerative joint diseases are a major cause of disability and drive the increasing demand for joint arthroplasty. Long-term prosthesis success depends on rapid and stable bone–implant integration, which is influenced by the osteo-inductive and osteo-conductive properties of implant materials. Chromium–cobalt (CrCo) and [...] Read more.
Background: Degenerative joint diseases are a major cause of disability and drive the increasing demand for joint arthroplasty. Long-term prosthesis success depends on rapid and stable bone–implant integration, which is influenced by the osteo-inductive and osteo-conductive properties of implant materials. Chromium–cobalt (CrCo) and titanium (Ti) alloys are widely used in reconstructive orthopedics, but direct comparative data on their biological performance, particularly for trabecular titanium (T-Ti), remain limited. This study aimed to directly compare the biocompatibility and osteogenic potential of CrCo and T-Ti using human mesenchymal stromal cells (MSCs). Methods: Human MSCs were characterized by immunophenotyping and cultured on CrCo and T-Ti scaffolds under control and osteogenic conditions for up to 28 days. Cell adhesion and morphology were assessed by scanning electron microscopy. Proliferation and viability were quantified, and osteogenic differentiation was evaluated using alkaline phosphatase activity, calcium deposition assays, and gene expression profiling of osteogenic markers. Results: Both materials supported MSC adhesion and proliferation, confirming cytocompatibility. Under control conditions, T-Ti significantly increased alkaline phosphatase activity and osteogenic gene expression. Under osteogenic stimulation, T-Ti accelerated differentiation and mineralized matrix deposition. CrCo exhibited limited stimulation of the osteogenic-supportive microenvironment and delayed differentiation responses. Conclusions: Trabecular titanium, in terms of morphology and topology, provides a biologically active scaffold that both induces and conducts osteogenic differentiation of human MSCs, whereas CrCo acts primarily as a mechanically optimized but biologically passive material. These findings support the use of trabecular titanium at bone-contact interfaces in joint prostheses to enhance osteointegration and potentially improve long-term implant stability. Full article
(This article belongs to the Special Issue Joint Prostheses: Innovations in Shoulder, Hip, and Knee Replacement)
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Review

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11 pages, 723 KB  
Review
Challenges and Limitations of Machine Learning in Total Joint Arthroplasty: Insights from Recent Studies
by Sara Ghasemi Rad Abiyaneh, Reza Hashemi, Corinne Archer and Khashayar Ghadirinejad
Prosthesis 2026, 8(4), 35; https://doi.org/10.3390/prosthesis8040035 - 31 Mar 2026
Viewed by 929
Abstract
Background: Total joint arthroplasty (TJA) is one of the most successful surgical procedures for patients to improve the quality of life. In recent years, the use of machine learning (ML) in the setting of arthroplasty decision-making has grown. Methods: This article [...] Read more.
Background: Total joint arthroplasty (TJA) is one of the most successful surgical procedures for patients to improve the quality of life. In recent years, the use of machine learning (ML) in the setting of arthroplasty decision-making has grown. Methods: This article reviewed studies published between 2020 and 2025 that applied ML to TJA, with a focus on the limitations reported by these studies. A search in ScienceDirect identified 220 articles. After screening and full-text assessment, 17 studies met the inclusion criteria, excluding imaging-based research, to focus on predictive models trained on non-image clinical data. Results: The reviewed studies revealed several common limitations, categorised into four groups, including observations and follow-up (30.3% of the studies), dataset quality and design (27.3%), model transferability and generalisation (27.3%), and outcome measurement and interpretation (15.2%). These limitations impact the reliability and real-world relevance of ML models in the context of arthroplasty. This article also provides suggestions to help researchers address these limitations in future studies. Conclusions: This review provides an overview of the potential limitations associated with the development of ML models within the TJA community in order to identify the gaps and challenges to improve the quality of research and possibly decision-making support systems using joint arthroplasty clinical datasets. Full article
(This article belongs to the Special Issue Joint Prostheses: Innovations in Shoulder, Hip, and Knee Replacement)
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