Submit to Diagnostics Review for Diagnostics Propose a Special Issue

Journal Browser

Knee Osteoarthritis: Current Challenges in Diagnosis and Management

Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 10346

Share This Special Issue

Special Issue Editors

Dr. Andrea Bernetti

E-Mail Website
Guest Editor

Department of Biological and Environmental Science and Technologies (Di.S.Te.B.A.), University of Salento, 73100 Lecce, Italy
Interests: rehabilitation medicine; physiotherapy; COVID-19; osteoarthritis
Special Issues, Collections and Topics in MDPI journals

Dr. Massimiliano Mangone

E-Mail Website
Co-Guest Editor

Department of Anatomy, Histology, Forensic Medicine and Locomotor Sciences, School of Pharmacy and Medicine, Sapienza University of Rome, 00185 Rome, Italy
Interests: rehabilitation; bioengineering; biomechanics of movement and function
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I am pleased to announce the launch of a new Special Issue of Diagnostics (ISSN 2075-4418) titled “Knee Osteoarthritis: Current Challenges in Diagnosis and Management”. In this Special Issue, the central themes will be the diagnosis and management of knee osteoarthritis. In particular, in addition to instrumental diagnostic techniques, space will also be given to biomechanics and instrumental evaluation of movement. Specifically, gait analysis methods and biomechanical evaluation will be emphasized, also through inertial and wearable sensors. Further space will be dedicated to minimally invasive and interventional procedures capable of supporting diagnostic reasoning through an ex adjuvantibus evaluation. Finally, the management of the patient with knee osteoarthritis will also be emphasized through telemedicine.

We hope to have your fundamental contribution to such an important issue.

Dr. Andrea Bernetti
Prof. Massimiliano Mangone
Guest Editors

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. Diagnostics is an international peer-reviewed open access semimonthly 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

Osteoarthritis
Physiatry
Musculoskeletal ultrasound
Musculoskeletal imaging
Movement analysis
Interventional physiatry
Telemedicine
Algology
Hyaluronic acid

Published Papers (3 papers)

Download All Papers

Research

Jump to: Review

8 pages, 588 KiB

Open AccessArticle

Biomechanical Changes in Gait Patterns of Patients with Grade II Medial Gonarthritis

by Dmitry Skvortsov, Sergey Kaurkin, Alexey Prizov, Aljona Altukhova, Alexander Troitskiy and Fedor Lazko

Diagnostics 2021, 11(7), 1242; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11071242 - 12 Jul 2021

Cited by 5 | Viewed by 1456

Abstract

Deforming osteoarthritis (OA) of the knee is a progressive disease associated with pain and compromised function of the joint. Typical biomechanical modifications in the gait of subjects with medial knee OA are characterized by decreased gait speed and overload on the affected limb. The borderline stage for conservative versus surgical management is Grade II OA. The aim of this research was to study preoperatively the specific features of gait, knee, and hip function in patients with Grade II medial OA. We examined 26 patients with Grade II unilateral gonarthritis with varus deformity and 20 healthy adults. Biomechanical parameters of gait were recorded using an inertial sensor system. The gait cycle (GC) slightly increased both for the affected and for the intact limb. The hip joint movements showed significant symmetrical reduction in the first flexion amplitude, as well as a symmetrical delay in full hip extension at the end of the stance phase. In the knee, the first flexion amplitude was significantly reduced on the affected side compared to healthy control. The extension amplitude in the single support phase was significantly increased in both the affected and the intact lower limbs. The swing amplitude was significantly reduced on the affected side. On the affected side, the changes were more pronounced, both in incidence and in severity. The affected knee showed a syndrome of three reduced amplitudes. In patients, walking is characterized by several groups of symptoms: those of unloading of the affected limb, those of limiting the load on the affected joint and the musculoskeletal system as a whole, and those of gait harmonization. The symptoms of unloading the affected side and those of harmonization are the common symptoms of adaptation, typical for several pathological conditions with a relatively preserved function. The intensity of the observed symptoms can help assess changes in the subject’s functional condition over time and during the treatment. Full article

(This article belongs to the Special Issue Knee Osteoarthritis: Current Challenges in Diagnosis and Management)

► Show Figures

Figure 1

Review

Jump to: Research

17 pages, 1343 KiB

Open AccessReview

Advanced MR Imaging for Knee Osteoarthritis: A Review on Local and Brain Effects

by Carlo A. Mallio, Caterina Bernetti, Francesco Agostini, Massimiliano Mangone, Marco Paoloni, Gabriele Santilli, Francesca Maria Martina, Carlo C. Quattrocchi, Bruno Beomonte Zobel and Andrea Bernetti

Diagnostics 2023, 13(1), 54; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics13010054 - 24 Dec 2022

Cited by 3 | Viewed by 3556

Abstract

Knee osteoarthritis is one of the leading causes of chronic disability worldwide and is a significant social and economic burden on healthcare systems; hence it has become essential to develop methods to identify patients at risk for developing knee osteoarthritis at an early stage. Standard morphological MRI sequences are focused mostly on alterations seen in advanced stages of osteoarthritis. However, they possess low sensitivity for early, subtle, and potentially reversible changes of the degenerative process. In this review, we have summarized the state of the art with regard to innovative quantitative MRI techniques that exploit objective and quantifiable biomarkers to identify subtle alterations that occur in early stages of osteoarthritis in knee cartilage before any morphological alteration occurs and to capture potential effects on the brain. These novel MRI imaging tools are believed to have great potential for improving the current standard of care, but further research is needed to address limitations before these compositional techniques can be robustly applied in research and clinical settings. Full article

(This article belongs to the Special Issue Knee Osteoarthritis: Current Challenges in Diagnosis and Management)

► Show Figures

Figure 1

26 pages, 4956 KiB

Open AccessReview

A Comprehensive Survey on Bone Segmentation Techniques in Knee Osteoarthritis Research: From Conventional Methods to Deep Learning

by Sozan Mohammed Ahmed and Ramadhan J. Mstafa

Diagnostics 2022, 12(3), 611; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12030611 - 01 Mar 2022

Cited by 19 | Viewed by 4018

Abstract

Knee osteoarthritis (KOA) is a degenerative joint disease, which significantly affects middle-aged and elderly people. The majority of KOA is primarily based on hyaline cartilage change, according to medical images. However, technical bottlenecks such as noise, artifacts, and modality pose enormous challenges for an objective and efficient early diagnosis. Therefore, the correct prediction of arthritis is an essential step for effective diagnosis and the prevention of acute arthritis, where early diagnosis and treatment can assist to reduce the progression of KOA. However, predicting the development of KOA is a difficult and urgent problem that, if addressed, could accelerate the development of disease-modifying drugs, in turn helping to avoid millions of total joint replacement procedures each year. In knee joint research and clinical practice there are segmentation approaches that play a significant role in KOA diagnosis and categorization. In this paper, we seek to give an in-depth understanding of a wide range of the most recent methodologies for knee articular bone segmentation; segmentation methods allow the estimation of articular cartilage loss rate, which is utilized in clinical practice for assessing the disease progression and morphological change, ranging from traditional techniques to deep learning (DL)-based techniques. Moreover, the purpose of this work is to give researchers a general review of the currently available methodologies in the area. Therefore, it will help researchers who want to conduct research in the field of KOA, as well as highlight deficiencies and potential considerations in application in clinical practice. Finally, we highlight the diagnostic value of deep learning for future computer-aided diagnostic applications to complete this review. Full article

(This article belongs to the Special Issue Knee Osteoarthritis: Current Challenges in Diagnosis and Management)

► Show Figures