Introduction: The aim of this study was to determine the reliability and accuracy of the tape measurement method (TMM) with a nearest reading of 5 mm in assessing leg length discrepancy (LLD).
Methods: This was a cross-sectional study conducted on 35 patients with LLD and 13 patients without LLD. Two blinded surgeons measured the lower limbs from the anterior superior iliac spine to the medial malleolus using TMM with a nearest reading of 5 mm. Computed tomography (CT) scanograms of the lower limbs of 22 patients were conducted by two blinded radiologists. Intraclass correlation coefficient (ICC) with 95 percent confidence interval was calculated to assess the interobserver reliability of TMM. The accuracy of TMM was assessed by comparison with CT as the gold standard.
Results: The interobserver reliability of LLD measurement using both TMM and CT scanogram was high, with ICCs of 0.924 and 0.971, respectively. No significant mean difference on paired sample t-test was observed for both TMM and CT scanogram. Compared to CT scanogram, TMM had good accuracy, with an ICC of 0.805. When the mean TMM readings by two observers were compared to those derived from CT scanogram, the ICC was found to be 0.847, with a mean difference of 1.95 (range -3.17 to 7.07) mm.
Conclusion: There was excellent agreement in the LLD measurements between the two surgeons using TMM, between the two radiologists using CT sonogram, and between the TMM and CT measurements. This study showed that one TMM with the nearest reading of 5 mm was reliable and accurate in measuring LLD.
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Sensors (Basel)
December 2024
Intelligent Manufacturing Laboratory, Production Engineering Institute, Faculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia.
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December 2024
2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece.
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School of Mathematical & Statistical Sciences, University of Texas Rio Grande Valley, One West University Boulevard, Brownsville, TX, 78520, USA.
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Department of Laboratory Medicine, Chungnam National University School of Medicine, Daejeon, Korea.
Background: Urinalysis, an essential diagnostic tool, faces challenges in terms of standardization and accuracy. The use of artificial intelligence (AI) with mobile technology can potentially solve these challenges. Therefore, we investigated the effectiveness and accuracy of an AI-based program in automatically interpreting urine test strips using mobile phone cameras, an approach that may revolutionize point-of-care testing.
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December 2024
Department of Ophthalmology, Centre Hospitalier Universitaire de Toulouse, Toulouse, France.
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