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Precision Orthopedics: Evolving Shoulder, Hip, and Knee Surgery Through Intelligent...
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Precision Orthopedics: Evolving Shoulder, Hip, and Knee Surgery Through Intelligent Technology and Personalized Clinical Care

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 892

Special Issue Editor

Dr. Alessandra Berton

E-Mail Website
Guest Editor
Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
Interests: shoulder instability; rotator cuff disease; patellofemoral pathologies; shoulder, knee and hip arthroplasty; orthopedic biomechanics; computational models; finite elements models

Special Issue Information

Dear Colleagues,

We are delighted to present the Special Issue titled “Precision Orthopedics: Evolving Shoulder, Hip, and Knee Surgery Through Intelligent Technology and Personalized Clinical Care”. Procedures involving the shoulder, hip, and knee represent some of the most common orthopedic surgeries performed globally, affecting a significant number of patients each year. The increasing prevalence of joint conditions, such as arthritis, traumatic injuries, tendon and ligament issues, and joint instability, underscores the critical need for tailored surgical approaches that address the specific requirements of each patient.

This Special Issue welcomes original research, in-depth literature reviews, and practical clinical perspectives highlighting recent developments in orthopedic surgery. Key areas explored include advanced surgical strategies combined with innovative technologies like robotic surgery to enhance precision, 3D printing for improved surgical planning, artificial intelligence for refined clinical decision-making, and wearable smart sensors to optimize rehabilitation and patient monitoring.

We sincerely thank all contributing authors and reviewers for their invaluable efforts. We hope that this Special Issue will foster further collaboration and progress, ultimately leading to improved patient care and surgical outcomes in orthopedics worldwide.

Dr. Alessandra Berton
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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine 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 2600 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

  • precision orthopedics
  • shoulder surgery
  • hip surgery
  • knee surgery
  • personalized medicine
  • artificial intelligence
  • robotic surgery
  • 3D printing
  • wearable sensors
  • patient outcomes

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Published Papers (2 papers)

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Research

12 pages, 1467 KB  
Article
Identifying Risk Groups in 73,000 Patients with Diabetes Receiving Total Hip Replacement: A Machine Learning Clustering Analysis
by Alishah Ahmadi, Anthony J. Kaywood, Alejandra Chavarria, Oserekpamen Favour Omobhude, Adam Kiss, Mateusz Faltyn and Jason S. Hoellwarth
J. Pers. Med. 2025, 15(11), 537; https://doi.org/10.3390/jpm15110537 - 5 Nov 2025
Abstract
Background/Objective: Diabetes mellitus (DM) is a highly prevalent condition that contributes to adverse outcomes in patients undergoing total hip arthroplasty (THA). This study applied machine learning clustering algorithms to identify comorbidity profiles among diabetic THA patients and evaluate their association with postoperative [...] Read more.
Background/Objective: Diabetes mellitus (DM) is a highly prevalent condition that contributes to adverse outcomes in patients undergoing total hip arthroplasty (THA). This study applied machine learning clustering algorithms to identify comorbidity profiles among diabetic THA patients and evaluate their association with postoperative outcomes. Methods: The 2015–2021 National Inpatient Sample was queried using ICD-10 CM/PCS codes to identify DM patients undergoing THA. Forty-nine comorbidities, complications, and clinical covariates were incorporated into clustering analysis. The Davies–Bouldin and Calinski–Harabasz indices determined the optimal number of clusters. Multivariate logistic regression assessed risk of non-routine discharge (NRD), and Kruskal–Wallis H testing evaluated length-of-stay (LOS) differences. Results: A total of 73,606 patients were included. Six clusters were identified, ranging from 107 to 61,505 patients. Cluster 6, enriched for urinary tract infection and sepsis, had the highest risk of NRD (OR 7.83, p < 0.001) and the longest median LOS (9.0 days). Clusters 1–4 had shorter recoveries with median LOS of 2.0 days and narrow variability, while Cluster 5 showed intermediate outcomes. Kruskal–Wallis and post hoc testing confirmed significant differences across clusters (p < 0.001). Conclusions: Machine learning clustering of diabetic THA patients revealed six distinct groups with varied comorbidity profiles. Infection-driven clusters carried the highest risk for non-routine discharge and prolonged hospitalization. This approach provides a novel framework for risk stratification and may inform targeted perioperative management strategies. Full article
(This article belongs to the Special Issue Precision Orthopedics: Evolving Shoulder, Hip, and Knee Surgery Through Intelligent Technology and Personalized Clinical Care)
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12 pages, 1520 KB  
Article
Real-World Outcomes of Robotic Total Knee Arthroplasty: Five Years’ Experience in a Non-Academic Center
by Joost Burger, Wei Fan, Sandy Gansiniec, Casper Reinders, Scarlette Kienzle, Clemens Gwinner, Adrianus den Hertog and Arne Kienzle
J. Pers. Med. 2025, 15(10), 482; https://doi.org/10.3390/jpm15100482 - 9 Oct 2025
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Abstract
Background: Robotic-assisted systems have transformed total knee arthroplasty (TKA), promising improved accuracy and intraoperative consistency, yet real-world data from non-academic centers remain limited. Objective: This study evaluates five-year clinical integration of a semi-autonomous, CT-based, robotic-arm-assisted TKA at a tertiary non-teaching hospital in Germany, [...] Read more.
Background: Robotic-assisted systems have transformed total knee arthroplasty (TKA), promising improved accuracy and intraoperative consistency, yet real-world data from non-academic centers remain limited. Objective: This study evaluates five-year clinical integration of a semi-autonomous, CT-based, robotic-arm-assisted TKA at a tertiary non-teaching hospital in Germany, focusing on planning accuracy, gap balancing, and intraoperative outcomes. Methods: We retrospectively analyzed all patients (n = 457) who underwent MAKO-assisted TKA from 2020 to 2025, performed by three orthopedic surgeons using a standardized subvastus approach. We assessed preoperative deformities, intraoperative alignment, implant sizing, and gap balancing. Surgical plans were adapted intraoperatively when indicated. Pre- vs. post-implantation values were compared using slopes to evaluate execution consistency. Results: Median patient age was 67.0 years (IQR: 60.0–75.0), with varus in 84.1% (7.0°, IQR: 4.0°–10.0°), valgus in 13.2% (3.0°, IQR: 1.5°–5.8°), and neutral alignment in 2.7%. Flexion contracture occurred in 80.4% (6.0°, IQR: 3.0–10.0%), hyperextension in 12.7% (2.0°, IQR: 1.5°–5.0°). Planning-to-execution consistency was high, even with plan adaptations. Slope values for alignment parameters were: tibial rotation in degrees (slope value: 1.0), femoral sagittal angle in degrees (0.8), tibial sagittal angle in degrees (0.9), coronal posterior condylar angle in degrees (0.9), femoral component size (1.0), tibial component size (1.0). Over 95% of cases showed ≤3.0° deviation between planned and final values. Bone resection concordance showed moderate agreement, with slopes from 0.8 (posterior medial femoral cut in mm) to 0.5 (lateral tibial cut in mm). Gap balancing improved at all stages, with reduced variability in medial/lateral extension and flexion gaps (all p < 0.05). Functional reconstruction showed significant improvements in extension, flexion, and deformities (all p < 0.001). Conclusions: Semi-autonomous, CT-based, robotic-arm-assisted TKA was successfully implemented in this non-academic setting, demonstrating acceptable intraoperative and functional reconstruction outcomes, supporting the feasibility of robotic-assisted surgery outside academic centers. Full article
(This article belongs to the Special Issue Precision Orthopedics: Evolving Shoulder, Hip, and Knee Surgery Through Intelligent Technology and Personalized Clinical Care)
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