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This study developed and validated a deep learning-based method for automatic measurement of femoral and tibial torsion on MRI. Axial T2-weighted sequences acquired of the hips, knees, and ankles of 93 patients (mean age, 13\u2009\u00b1\u20095\u00a0years; 52 males) were included and allocated to training (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200960), validation (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u20099), and test sets (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200924). A U-net convolutional neural network was trained to segment both femur and tibia, identify osseous anatomic landmarks, define pertinent reference lines, and quantify femoral and tibial torsion. Manual measurements by two radiologists provided the reference standard. Inter-reader comparisons were performed using repeated-measures ANOVA, Pearson\u2019s\n                    <jats:italic>r,<\/jats:italic>\n                    and the intraclass correlation coefficient (ICC). Mean S\u00f8rensen-Dice coefficients for segmentation accuracy ranged between 0.89 and 0.93 and erroneous segmentations were scarce. Ranges of torsion as measured by both readers and the algorithm on the same axial image were 15.8\u00b0\u201318.0\u00b0 (femur) and 33.9\u00b0\u201335.2\u00b0 (tibia). Correlation coefficients (ranges, .968\u2009\u2264\u2009r\u2009\u2264\u2009.984 [femur]; .867\u2009\u2264\u2009r\u2009\u2264\u2009.904 [tibia]) and ICCs (ranges, .963\u2009\u2264\u2009ICC\u2009\u2264\u2009.974 [femur]; .867\u2009\u2264\u2009ICC\u2009\u2264\u2009.894 [tibia]) indicated excellent inter-reader agreement. Algorithm-based analysis was faster than manual analysis (7 vs 207 vs 230\u00a0s,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u2009.001). In conclusion, fully automatic measurement of torsional alignment is accurate, reliable, and sufficiently fast for clinical workflows.\n                  <\/jats:p>","DOI":"10.1038\/s41598-021-02708-y","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T06:04:42Z","timestamp":1638338682000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Artificial intelligence-based automatic assessment of lower limb torsion on MRI"],"prefix":"10.1038","volume":"11","author":[{"given":"Justus","family":"Schock","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Truhn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Darius","family":"N\u00fcrnberger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Conrad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc Sebastian","family":"Huppertz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Keil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christiane","family":"Kuhl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dorit","family":"Merhof","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sven","family":"Nebelung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,12,1]]},"reference":[{"key":"2708_CR1","doi-asserted-by":"publisher","DOI":"10.2106\/JBJS.RVW.18.00016","volume":"7","author":"JA Gruskay","year":"2019","unstructured":"Gruskay, J. 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