Morphometric parameters of the knee joint: completeness and quality of assessment according to radiography
Автор: Astapenko E.V., Vladzimirskiy A.V.
Журнал: Вестник медицинского института "РЕАВИЗ": реабилитация, врач и здоровье @vestnik-reaviz
Рубрика: Медицинская визуализация
Статья в выпуске: 4 т.15, 2025 года.
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Background. Knee joint radiography remains the primary diagnostic method for gonarthrosis at the outpatient stage. Correct disease staging requires accurate measurement of joint space width, however, the quality of study result descriptions has not been fully investigated. Objective: To evaluate the completeness of joint space width descriptions and the correctness of gonarthrosis staging in knee joint radiography protocols. Materials and methods. 1000 randomly selected knee joint radiography protocols from the UMIAS database of Moscow for the period 2023-2024 were analyzed. The presence of objective data on joint space width measurements and the correctness of staging according to N.S. Kosinskaya classification were assessed. Results. Objective joint space measurement data was contained in only 22.0% of protocols (220 out of 1000). In 78.0% of cases, subjective formulations such as "moderately narrowed", "unevenly narrowed" were used without specific measurements. Nevertheless, specific stages of gonarthrosis were indicated in conclusions: stage I – in 54.1% of defective protocols, stage II – in 31.8%, stage III – in 8.5%. In 5.6% of cases, disease stage was not determined. Conclusion. Unsatisfactory completeness of morphometric parameter descriptions in knee joint radiography was revealed. Implementation of automated joint space width measurement tools is necessary for objectification of gonarthrosis staging and improvement of diagnostic quality.
Knee joint radiography [D011859], morphometric parameters [D009056], gonarthrosis [D020370], deforming osteoarthritis [D010003], joint space [нет точного MeSH-кода], quality of description [D011787], study protocol [D011795], staging [D009367], Kosinskaya classification [нет MeSH-кода], quality control [D011786], artificial intelligence [D001185], automation [D001331], лучевая диагностика / radiation diagnostics [D003952], medical documentation [D008499], protocol defects [нет точного MeSH-кода]
Короткий адрес: https://sciup.org/143185018
IDR: 143185018 | УДК: 616.728.3-073.75-073.916:004.93 | DOI: 10.20340/vmi-rvz.2025.4.MIM.1