MODERN CONCEPTS OF DIAGNOSTICS OF POSTEROLATERAL CORNER KNEE INJURIES
DOI:
https://doi.org/10.15674/0030-59872025463-71Keywords:
Knee, ligaments, posterolateral corner, injuries, instability, diagnosticsAbstract
Injuries to the posterolateral corner (PLC) of the knee are usually not initially apparent, and diagnosis and treatment require a full understanding of the functional interactions of their structures, as well as a specific history and complete physical examination. Objective. To summarize current concepts regarding the anatomy, biomechanics, and diagnosis of PLC injuries of the knee and to outline directions for improving the diagnostic algorithm. Materials and methods. A narrative review of publications indexed in PubMed, Scopus, and Google Scholar was conducted, focusing on anatomical and biomechanical characteristics, clinical manifestations, imaging modalities, and classification systems for PLC injuries. Results. The lateral collateral ligament, popliteofibular ligament, popliteus tendon, posterolateral capsule, and associated musculotendinous complexes were identified as the key static and dynamic stabilizers resisting varus stress and external rotation of the tibia. PLC injuries are rarely isolated; more commonly, they occur in combination with anterior or posterior cruciate ligament tears and, if not diagnosed in a timely manner, lead to chronic instability and increased load on the medial compartment of the knee. Clinical stress tests and varus stress radiography provide an approximate assessment of instability; however, existing classification systems do not fully capture the variety of injury patterns and their combinations, while the sensitivity of conventional MRI, particularly in chronic cases, remains limited. Arthroscopy may serve as an additional method for intra-articular evaluation. Conclusions. Accurate diagnosis of PLC injuries requires a standardized, multimodal approach with precise identification of the injured structures. The development of an integrated, differentiated diagnostic algorithm supported by machine-learning – based artificial intelligence tools appears to be a promising strategy for improving early detection and optimizing treatment planning.
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2025 Maxim Golovakha, Yevhen Bilykh, Andrii Bezverkhiy
This work is licensed under a Creative Commons Attribution 4.0 International License.
The authors retain the right of authorship of their manuscript and pass the journal the right of the first publication of this article, which automatically become available from the date of publication under the terms of Creative Commons Attribution License, which allows others to freely distribute the published manuscript with mandatory linking to authors of the original research and the first publication of this one in this journal.
Authors have the right to enter into a separate supplemental agreement on the additional non-exclusive distribution of manuscript in the form in which it was published by the journal (i.e. to put work in electronic storage of an institution or publish as a part of the book) while maintaining the reference to the first publication of the manuscript in this journal.
The editorial policy of the journal allows authors and encourages manuscript accommodation online (i.e. in storage of an institution or on the personal websites) as before submission of the manuscript to the editorial office, and during its editorial processing because it contributes to productive scientific discussion and positively affects the efficiency and dynamics of the published manuscript citation (see The Effect of Open Access).
