CONCORDANCE AND DISCORDANCE BETWEEN RADIOLOGY RESIDENTS AND CONSULTANT RADIOLOGIST IN INTERPRETATION OF KNEE RADIOGRAPHS IN PATEINTS WITH KNEE PAIN
DOI:
https://doi.org/10.37762/jgmds.13-1.741Keywords:
Knee radiographs, Diagnostic Concordance, Resident, Consultant., Knee Radiographs, Diagnostic Concordance, Radiology Resident, Consultant Radiologist, Osteoarthritis, Fracture, Musculoskeletal ImagingAbstract
Objectives: The aim of the study was to evaluate the level of agreement and disagreement between resident and consultant radiologists' interpretations of knee radiographs, highlighting the importance of accurate image analysis for informed clinical decisions.
Methodology: This prospective study was conducted in the Radiology Department at Rehman Medical Institute over two years (November 2022 – November 2024). Four residents interpreted knee radiographs of 203 patients. Independently, four blinded consultant radiologists reviewed the same images. Agreement between the two groups was defined as concordance. Data was recorded using Microsoft Excel and analyzed with SPSS version 22. Cohen’s Kappa coefficient was used to measure the level of agreement.
Results: The study found a substantial agreement between residents and consultants, with a Cohen’s Kappa value of 0.629 (p < 0.05), indicating statistically significant concordance. The overall concordance rate was 79.3%, suggesting that in nearly 8 out of 10 cases, resident interpretations matched those of consultants. Discordance was observed in 20.7% of cases, with osteoarthritis being the most frequently misinterpreted condition, often over-reported by residents.
Conclusion: This study highlights a significant concordance (79.3%) between residents and consultant radiologists in knee radiograph interpretation, with a Cohen’s Kappa of 0.629. However, the 20.7% discordance underscores the need for ongoing training to enhance diagnostic accuracy, particularly in conditions like osteoarthritis. Strengthening radiology education can further improve clinical decision-making and patient care.
Keywords: Knee radiographs, Diagnostic Concordance, Resident, Consultant.
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Brejnebøl MW, Lenskjold A, Ziegeler K, Ruitenbeek H, Müller FC, Nybing JU, et al. Interobserver agreement and performance of concurrent AI assistance for radiographic evaluation of knee osteoarthritis. Radiology. 2024;312(1):e233341. doi:10.1148/radiol.233341 DOI: https://doi.org/10.1148/radiol.233341
Huhtanen JT, Nyman M, Sequeiros RB, Koskinen SK, Pudas TK, Kajander S, et al. Discrepancies between radiology specialists and residents in fracture detection from musculoskeletal radiographs. Diagnostics (Basel). 2023;13(20):3207. doi:10.3390/diagnostics13203207 DOI: https://doi.org/10.3390/diagnostics13203207
Cain G, Pittock LJ, Piper K, Venumbaka MR, Bodoceanu M. Agreement in the reporting of general practitioner requested musculoskeletal radiographs: reporting radiographers and consultant radiologists compared with an index radiologist. Radiography (Lond). 2022;28(2):288–95. doi:10.1016/j.radi.2021.12.004 DOI: https://doi.org/10.1016/j.radi.2021.12.004
Seow D, Yasui Y, Chan L, et al. Inconsistent radiographic diagnostic criteria for Lisfranc injuries: a systematic review. BMC Musculoskelet Disord. 2023;24:915. doi:10.1186/s12891-023-07043-z DOI: https://doi.org/10.1186/s12891-023-07043-z
Shahzad M, Zulfiqar T, Ali A. Radiographic evaluation of knee joint in patients with knee pain and its correlation with osteoarthritis and gender. Adv Life Sci. 2022;9(3):309–12 DOI: https://doi.org/10.62940/als.v9i3.1404
Brejnebøl MW, Hansen P, Nybing JU, Bachmann R, Ratjen U, Hansen IV, et al. External validation of an artificial intelligence tool for radiographic knee osteoarthritis severity classification. Eur J Radiol. 2022;150:110249. doi:10.1016/j.ejrad.2022.110249 DOI: https://doi.org/10.1016/j.ejrad.2022.110249
Pham HH, Nguyen HQ, Nguyen HT, Le LT, Lam K. Evaluating the impact of an explainable machine learning system on the interobserver agreement in chest radiograph interpretation. arXiv [Preprint]. 2023 Apr 1. arXiv:2304.01220
Zhang J, Santos C, Park C, Mazurowski MA, Colglazier R. Improving image classification of knee radiographs: an automated image labeling approach. J Digit Imaging. 2023;36(6):2402–10. doi:10.1007/s10278-023-00897-1 DOI: https://doi.org/10.1007/s10278-023-00894-x
Guria A, Chandra V, Bej B, Vimal K, Barnwal RK, Singh US, et al. Clinical and radiological evaluation of osteoarthritis in knee pain patients and its association with inflammatory markers at MGM Medical College Hospital, Jamshedpur. J Family Med Prim Care. 2025;14(6):2242–50. doi:10.4103/jfmpc.jfmpc_365_25 DOI: https://doi.org/10.4103/jfmpc.jfmpc_1452_24
Parsons C, Fuggle N, Edwards MH, et al. Concordance between clinical and radiographic evaluations of knee osteoarthritis. Aging Clin Exp Res. 2017;30(1):17–25. doi:10.1007/s40520-017-0845-0 DOI: https://doi.org/10.1007/s40520-017-0847-z
Haas R, Gorelik A, O'Connor DA, Pearce C, Mazza D, Buchbinder R. Patterns of imaging requests by general practitioners for people with musculoskeletal complaints: an analysis from a primary care database. Arthritis Care Res (Hoboken). 2025;77(3):402–11. doi:10.1002/acr.25302 DOI: https://doi.org/10.1002/acr.25189
Eckersley T, Faulkner J, Al-Dadah O. Inter- and intra-observer reliability of radiological grading systems for knee osteoarthritis. Skeletal Radiol. 2021;50(10):2069–78. doi:10.1007/s00256-021-03749-3 DOI: https://doi.org/10.1007/s00256-021-03767-y
Smolle MA, Goetz C, Maurer D, Vielgut I, Novak M, Zier G, et al. Artificial intelligence-based computer-aided system for knee osteoarthritis assessment increases experienced orthopaedic surgeons' agreement rate and accuracy. Knee Surg Sports Traumatol Arthrosc. 2023;31(3):1053–62. doi:10.1007/s00167-022-07313-1 DOI: https://doi.org/10.1007/s00167-022-07220-y
Yasa Y, Çoban D, Özbey F, Tuna T, Yılmaz BN, Erzurumlu ZÜ, Sadık E. Evaluation of common diagnostic errors in panoramic radiographs and interobserver agreement in error identification. Med Records. 2025;7(1):114–9. doi:10.5455/medrec.2025.114 DOI: https://doi.org/10.37990/medr.1562670
Borotikar B, Lempereur M, Lelievre M, Burdin V, Ben Salem D, Brochard S. Dynamic MRI to quantify musculoskeletal motion: a systematic review of concurrent validity and reliability, and perspectives for evaluation of musculoskeletal disorders. PLoS One. 2017;12(12):e0189587. doi:10.1371/journal.pone.0189587 DOI: https://doi.org/10.1371/journal.pone.0189587
Harkey P, Duszak R Jr, Gyftopoulos S, Rosenkrantz AB. Who refers musculoskeletal extremity imaging examinations to radiologists? Am J Roentgenol. 2018;210(4):834–41. doi:10.2214/AJR.17.19079 DOI: https://doi.org/10.2214/AJR.17.18591
Meredith S, McBurnie A, Tavete I, Edwards C. Radiographer-clinician agreement on imaging selection for extremity injuries in the emergency department: a pilot study. Radiography (Lond). 2025;31(6):103166. doi:10.1016/j.radi.2025.103166 DOI: https://doi.org/10.1016/j.radi.2025.103166
Parsons C, Clynes M, Syddall H, Jagannath D, Litwic A, van der Pas S, et al. How well do radiographic, clinical and self-reported diagnoses of knee osteoarthritis agree? Findings from the Hertfordshire cohort study. SpringerPlus. 2015;4:177. doi:10.1186/s40064-015-0962-0 DOI: https://doi.org/10.1186/s40064-015-0949-z
Benchoufi M, Matzner-Lober E, Molinari N, Jannot AS, Soyer P. Interobserver agreement issues in radiology. Diagn Interv Imaging. 2020;101(10):639–41. doi:10.1016/j.diii.2020.07.005 DOI: https://doi.org/10.1016/j.diii.2020.09.001
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