Gurney B. Leg length discrepancy. Gait Posture. 2002;15(2):195–206.

Article  PubMed  Google Scholar 

Raczkowski JW, Daniszewska B, Zolynski K. Clinical research Functional scoliosis caused by leg length discrepancy. Arch Med Sci. 2010;3:393–8.

Article  Google Scholar 

Gordon JE, Davis LE. Leg length discrepancy: the natural history (and what do we really know). J Pediatr Orthop. 2019;39(Issue 6, Supplement 1 Suppl 1):S10-s13.

Article  PubMed  Google Scholar 

Vogt B, et al. Leg length discrepancy- treatment indications and strategies. Dtsch Arztebl Int. 2020;117(24):405–11.

PubMed  PubMed Central  Google Scholar 

Sharma L, et al. Varus and valgus alignment and incident and progressive knee osteoarthritis. Ann Rheum Dis. 2010;69(11):1940–5.

Article  PubMed  Google Scholar 

Thienpont E, et al. Bone morphotypes of the varus and valgus knee. Arch Orthop Trauma Surg. 2017;137(3):393–400.

Article  CAS  PubMed  Google Scholar 

Fang DM, Ritter MA, Davis KE. Coronal alignment in total knee arthroplasty: just how important is it? J Arthroplasty. 2009;24(6 Suppl):39–43.

Article  PubMed  Google Scholar 

Ritter MA, Faris PM, Keating EM, Meding JB. Postoperative alignment of total knee replacement. Its effect on survival. Clin Orthop Relat Res. 1994;(299):153–6.

Parratte S, et al. Effect of postoperative mechanical axis alignment on the fifteen-year survival of modern, cemented total knee replacements. J Bone Joint Surg Am. 2010;92(12):2143–9.

Article  PubMed  Google Scholar 

Archer H, et al. Artificial intelligence-generated hip radiological measurements are fast and adequate for reliable assessment of hip dysplasia. Bone Joint Open. 2022;3(11):877–84.

Article  PubMed  PubMed Central  Google Scholar 

Kurmis AP, Ianunzio JR. Artificial intelligence in orthopedic surgery: evolution, current state and future directions. Arthroplasty. 2022;4(1):9. https://doi.org/10.1186/s42836-022-00112-z.

Rouzrokh P, et al. Deep learning artificial intelligence model for assessment of hip dislocation risk following primary total hip arthroplasty from postoperative radiographs. J Arthroplasty. 2021;36(6):2197-2203.e3.

Article  PubMed  PubMed Central  Google Scholar 

Ramkumar PN, et al. Deep learning preoperatively predicts value metrics for primary total knee arthroplasty: development and validation of an artificial neural network model. J Arthroplasty. 2019;34(10):2220-2227.e1.

Article  PubMed  Google Scholar 

Krogue JD, et al. Automatic hip fracture identification and functional subclassification with deep learning. Radiol: Artif Intell. 2020;2(2):e190023.

PubMed  Google Scholar 

Schock J, et al. Automated analysis of alignment in long-leg radiographs by using a fully automated support system based on artificial intelligence. Radiol Artif Intell. 2021;3(2):e200198.

Article  PubMed  Google Scholar 

Meng X, Wang Z, Ma X, Liu X, Ji H, Cheng JZ, Dong P. Fully automated measurement on coronal alignment of lower limbs using deep convolutional neural networks on radiographic images. BMC Musculoskelet Disord. 2022;23(1):869. https://doi.org/10.1186/s12891-022-05818-4.

Erne F, et al. Automated artificial intelligence-based assessment of lower limb alignment validated on weight-bearing pre- and postoperative full-leg radiographs. Diagnostics. 2022;12(11):2679.

Article  PubMed  PubMed Central  Google Scholar 

Larson N, et al. Artificial intelligence system for automatic quantitative analysis and radiology reporting of leg length radiographs. J Digit Imaging. 2022;35(6):1494–505.

Article  PubMed  PubMed Central  Google Scholar 

Jo C, et al. Deep learning-based landmark recognition and angle measurement of full-leg plain radiographs can be adopted to assess lower extremity alignment. Knee Surg Sports Traumatol Arthrosc. 2023;31(4):1388–97.

Article  PubMed  Google Scholar 

Simon S, et al. Fully automated deep learning for knee alignment assessment in lower extremity radiographs: a cross-sectional diagnostic study. Skeletal Radiol. 2022;51(6):1249–59.

Article  PubMed  Google Scholar 

Archer H, et al. Artificial intelligence in musculoskeletal radiographs: scoliosis, hip, limb length, and lower extremity alignment measurements. Semin Roentgenol. 2024;59(4):510–7.

Article  PubMed  Google Scholar 

Archer H, Reine S, Xia S, Vazquez LC, Ashikyan O, Pezeshk P, Kohli A, Xi Y, Wells JE, Hummer A, Difranco M, Chhabra A. Deep learning generated lower extremity radiographic measurements are adequate for quick assessment of knee angular alignment and leg length determination. Skeletal Radiol. 2024;53(5):923–933. https://doi.org/10.1007/s00256-023-04502-5.

Kallogjeri D, Spitznagel EL Jr, Piccirillo JF. Importance of defining and interpreting a clinically meaningful difference in clinical research. JAMA Otolaryngol-Head Neck Surg. 2020;146(2):101–2.

Article  PubMed  Google Scholar 

Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1(8476):307–10.

Article  CAS  PubMed  Google Scholar 

Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess. 1994;6:284–90.

Article  Google Scholar 

Lu MJ, Zhong WH, Liu YX, Miao HZ, Li YC, Ji MH. Sample size for assessing agreement between two methods of measurement by Bland-Altman method. Int J Biostat. 2016;12(2):/j/ijb.2016.12.issue-2/ijb-2015-0039/ijb-2015-0039.xml. https://doi.org/10.1515/ijb-2015-0039.

Schwarz GM, et al. Artificial intelligence enables reliable and standardized measurements of implant alignment in long leg radiographs with total knee arthroplasties. Knee Surg Sports Traumatol Arthrosc. 2022;30(8):2538–47.

Article  PubMed  Google Scholar