{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T12:14:47Z","timestamp":1773317687511,"version":"3.50.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T00:00:00Z","timestamp":1707523200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T00:00:00Z","timestamp":1707523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["PD\/BD\/142797\/2018"],"award-info":[{"award-number":["PD\/BD\/142797\/2018"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/04436\/2020"],"award-info":[{"award-number":["UIDB\/04436\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDP\/04436\/2020"],"award-info":[{"award-number":["UIDP\/04436\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008814","name":"Universidade do Minho","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100008814","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objectives<\/jats:title>\n                <jats:p>To develop and validate a deep learning\u2013based approach to automatically measure the patellofemoral instability (PFI) indices related to patellar height and trochlear dysplasia in knee magnetic resonance imaging (MRI) scans.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>A total of 763 knee MRI slices from 95 patients were included in the study, and 3393 anatomical landmarks were annotated for measuring sulcus angle (SA), trochlear facet asymmetry (TFA), trochlear groove depth (TGD) and lateral trochlear inclination (LTI) to assess trochlear dysplasia, and Insall-Salvati index (ISI), modified Insall-Salvati index (MISI), Caton Deschamps index (CDI) and patellotrochlear index (PTI) to assess patellar height. A U-Net based network was implemented to predict the landmarks\u2019 locations. The successful detection rate (SDR) and the mean absolute error (MAE) evaluation metrics were used to evaluate the performance of the network. The intraclass correlation coefficient (ICC) was also used to evaluate the reliability of the proposed framework to measure the mentioned PFI indices.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The developed models achieved good accuracy in predicting the landmarks\u2019 locations, with a maximum value for the MAE of 1.38\u2009\u00b1\u20090.76\u00a0mm. The results show that LTI, TGD, ISI, CDI and PTI can be measured with excellent reliability (ICC\u2009&gt;\u20090.9), and SA, TFA and MISI can be measured with good reliability (ICC\u2009&gt;\u20090.75), with the proposed framework.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>This study proposes a reliable approach with promising applicability for automatic patellar height and trochlear dysplasia assessment, assisting the radiologists in their clinical practice.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Clinical relevance statement<\/jats:title>\n                <jats:p>The objective knee landmarks detection on MRI images provided by artificial intelligence may improve the reproducibility and reliability of the imaging evaluation of trochlear anatomy and patellar height, assisting radiologists in their clinical practice in the patellofemoral instability assessment.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key Points<\/jats:title>\n                <jats:p><jats:italic>\u2022 Imaging evaluation of patellofemoral instability is subjective and vulnerable to substantial intra and interobserver variability.<\/jats:italic><\/jats:p>\n                <jats:p>\u2022 <jats:italic>Patellar height and trochlear dysplasia are reliably assessed in MRI by means of artificial intelligence (AI).<\/jats:italic><\/jats:p>\n                <jats:p>\u2022 <jats:italic>The developed AI framework provides an objective evaluation of patellar height and trochlear dysplasia enhancing the clinical practice of the radiologists.<\/jats:italic><\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00330-024-10596-9","type":"journal-article","created":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T03:02:22Z","timestamp":1707534142000},"page":"5736-5747","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Knee landmarks detection via deep learning for automatic imaging evaluation of trochlear dysplasia and patellar height"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3835-2389","authenticated-orcid":false,"given":"Roberto M.","family":"Barbosa","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7267-3106","authenticated-orcid":false,"given":"Lu\u00eds","family":"Serrador","sequence":"additional","affiliation":[]},{"given":"Manuel Vieira","family":"da Silva","sequence":"additional","affiliation":[]},{"given":"Carlos Sampaio","family":"Macedo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0023-7203","authenticated-orcid":false,"given":"Cristina P.","family":"Santos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,10]]},"reference":[{"key":"10596_CR1","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/978-3-642-54965-6_6","volume-title":"The Patellofemoral Joint: State of the Art in Evaluation and Management","author":"I Cardona-Mu\u00f1oz","year":"2014","unstructured":"Cardona-Mu\u00f1oz I, Cardona-Medina JI, de la Rosa A (2014) Imaging of patellofemoral joint. In: Gobbi A, Espregueira-Mendes J, Nakamura N (eds) The Patellofemoral Joint: State of the Art in Evaluation and Management. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 29\u201335"},{"key":"10596_CR2","doi-asserted-by":"publisher","first-page":"5251","DOI":"10.3390\/app12105251","volume":"12","author":"EG Pandini","year":"2022","unstructured":"Pandini EG, Pironti P, Maggioni DM et al (2022) Is Caton-Deschamps Index reliable and reproducible in preoperative assessment of patellar height for patellar instability surgery? Appl Sci 12:5251. https:\/\/doi.org\/10.3390\/app12105251","journal-title":"Appl Sci"},{"key":"10596_CR3","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/BF01552649","volume":"2","author":"H Dejour","year":"1994","unstructured":"Dejour H, Walch G, Nove-Josserand L, Guier C (1994) Factors of patellar instability: an anatomic radiographic study. Knee Surg Sports Traumatol Arthrosc 2:19\u201326. https:\/\/doi.org\/10.1007\/BF01552649","journal-title":"Knee Surg Sports Traumatol Arthrosc"},{"key":"10596_CR4","doi-asserted-by":"publisher","first-page":"8","DOI":"10.5435\/00124635-201101000-00002","volume":"19","author":"M Bollier","year":"2011","unstructured":"Bollier M, Fulkerson JP (2011) The role of trochlear dysplasia in patellofemoral instability. J Am Acad Orthop Surg 19:8\u201316. https:\/\/doi.org\/10.5435\/00124635-201101000-00002","journal-title":"J Am Acad Orthop Surg"},{"key":"10596_CR5","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1148\/rg.304095755","volume":"30","author":"G Diederichs","year":"2010","unstructured":"Diederichs G, Issever AS, Scheffler S (2010) MR imaging of patellar instability: Injury patterns and assessment of risk factors. Radiographics 30:961\u2013981. https:\/\/doi.org\/10.1148\/rg.304095755","journal-title":"Radiographics"},{"key":"10596_CR6","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/j.ejrad.2010.06.042","volume":"79","author":"P Balcarek","year":"2011","unstructured":"Balcarek P, Walde TA, Frosch S et al (2011) Patellar dislocations in children, adolescents and adults: a comparative MRI study of medial patellofemoral ligament injury patterns and trochlear groove anatomy. Eur J Radiol 79:415\u2013420. https:\/\/doi.org\/10.1016\/j.ejrad.2010.06.042","journal-title":"Eur J Radiol"},{"key":"10596_CR7","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1148\/radiology.216.3.r00se38858","volume":"216","author":"CWA Pfirrmann","year":"2000","unstructured":"Pfirrmann CWA, Zanetti M, Romero J, Hodler J (2000) Femoral trochlear dysplasia: MR findings. Radiology 216:858\u2013864. https:\/\/doi.org\/10.1148\/radiology.216.3.r00se38858","journal-title":"Radiology"},{"key":"10596_CR8","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1177\/0363546516663498","volume":"45","author":"M Askenberger","year":"2017","unstructured":"Askenberger M, Janarv PM, Finnbogason T, Arendt EA (2017) Morphology and anatomic patellar instability risk factors in first-time traumatic lateral patellar dislocations. Am J Sports Med 45:50\u201358. https:\/\/doi.org\/10.1177\/0363546516663498","journal-title":"Am J Sports Med"},{"key":"10596_CR9","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1148\/radiology.216.2.r00au07582","volume":"216","author":"Y Carrillon","year":"2000","unstructured":"Carrillon Y, Abidi H, Dejour D et al (2000) Patellar instability: assessment on MR images by measuring the lateral trochlear inclination - initial experience. Radiology 216:582\u2013585. https:\/\/doi.org\/10.1148\/radiology.216.2.r00au07582","journal-title":"Radiology"},{"key":"10596_CR10","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1148\/101.1.101","volume":"101","author":"J Insall","year":"1971","unstructured":"Insall J, Salvati E (1971) Patella position in the normal knee joint. Radiology 101:101\u2013104. https:\/\/doi.org\/10.1148\/101.1.101","journal-title":"Radiology"},{"key":"10596_CR11","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1097\/00003086-199209000-00022","volume":"282","author":"R Grelsamer","year":"1992","unstructured":"Grelsamer R, Meadows S (1992) The modified Insall-Salvati ratio for assessment of patellar height. Clin Orthop Relat Res 282:170\u2013176","journal-title":"Clin Orthop Relat Res"},{"key":"10596_CR12","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1097\/JSA.0000000000000148","volume":"25","author":"A Haj-Mirzaian","year":"2017","unstructured":"Haj-Mirzaian A, Thawait GK, Tanaka MJ, Demehri S (2017) Diagnosis and characterization of patellofemoral instability: review of available imaging modalities. Sports Med Arthrosc Rev 25:64\u201371. https:\/\/doi.org\/10.1097\/JSA.0000000000000148","journal-title":"Sports Med Arthrosc Rev"},{"key":"10596_CR13","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1007\/s00167-005-0015-4","volume":"14","author":"RM Biedert","year":"2006","unstructured":"Biedert RM, Albrecht S (2006) The patellotrochlear index: a new index for assessing patellar height. Knee Surg Sports Traumatol Arthrosc 14:707\u2013712. https:\/\/doi.org\/10.1007\/s00167-005-0015-4","journal-title":"Knee Surg Sports Traumatol Arthrosc"},{"key":"10596_CR14","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1007\/s00167-019-05531-1","volume":"28","author":"F Verhulst","year":"2020","unstructured":"Verhulst F, v., van Sambeeck JDP, Olthuis GS, et al (2020) Patellar height measurements: Insall-Salvati ratio is most reliable method. Knee Surg Sports Traumatol Arthrosc 28:869\u2013875. https:\/\/doi.org\/10.1007\/s00167-019-05531-1","journal-title":"Knee Surg Sports Traumatol Arthrosc"},{"key":"10596_CR15","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1002\/ima.22612","volume":"32","author":"KM Ridhma","year":"2022","unstructured":"Ridhma KM, Sofat S et al (2022) Automated measurement of sulcus angle on axial knee magnetic resonance images. Int J Imaging Syst Technol 32:251\u2013265. https:\/\/doi.org\/10.1002\/ima.22612","journal-title":"Int J Imaging Syst Technol"},{"key":"10596_CR16","doi-asserted-by":"publisher","first-page":"6785","DOI":"10.1088\/0031-9155\/55\/22\/012","volume":"55","author":"HC Chen","year":"2010","unstructured":"Chen HC, Lin CJ, Wu CH et al (2010) Automatic Insall-Salvati ratio measurement on lateral knee x-ray images using model-guided landmark localization. Phys Med Biol 55:6785\u20136800. https:\/\/doi.org\/10.1088\/0031-9155\/55\/22\/012","journal-title":"Phys Med Biol"},{"key":"10596_CR17","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.medengphy.2020.01.012","volume":"78","author":"H Chen","year":"2020","unstructured":"Chen H, Kluijtmans L, Bakker M et al (2020) A robust and semi-automatic quantitative measurement of patellofemoral instability based on four dimensional computed tomography. Med Eng Phys 78:29\u201338. https:\/\/doi.org\/10.1016\/j.medengphy.2020.01.012","journal-title":"Med Eng Phys"},{"key":"10596_CR18","doi-asserted-by":"publisher","unstructured":"Sun L, Kong Q, Huang Y et al (2020) Automatic segmentation and measurement on knee computerized tomography images for patellar dislocation diagnosis. Comput Math Methods Med 2020. https:\/\/doi.org\/10.1155\/2020\/1782531","DOI":"10.1155\/2020\/1782531"},{"key":"10596_CR19","doi-asserted-by":"publisher","first-page":"4974","DOI":"10.1007\/s00330-020-06856-z","volume":"30","author":"Q Ye","year":"2020","unstructured":"Ye Q, Shen Q, Yang W et al (2020) Development of automatic measurement for patellar height based on deep learning and knee radiographs. Eur Radiol 30:4974\u20134984. https:\/\/doi.org\/10.1007\/s00330-020-06856-z","journal-title":"Eur Radiol"},{"key":"10596_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-022-08967-1","author":"E T, Nai R, Liu X,","year":"2022","unstructured":"E T, Nai R, Liu X, et al (2022) Automatic measurement of the patellofemoral joint parameters in the Laurin view: a deep learning\u2013based approach. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-022-08967-1","journal-title":"Eur Radiol"},{"key":"10596_CR21","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1016\/j.ejrnm.2016.09.020","volume":"47","author":"NM Osman","year":"2016","unstructured":"Osman NM, Ebrahim SMB (2016) Patellofemoral instability: quantitative evaluation of predisposing factors by MRI. Egypt J Radiol Nucl Med 47:1529\u20131538. https:\/\/doi.org\/10.1016\/j.ejrnm.2016.09.020","journal-title":"Egypt J Radiol Nucl Med"},{"key":"10596_CR22","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1136\/jisakos-2015-000015","volume":"1","author":"TJ Ridley","year":"2016","unstructured":"Ridley TJ, Bremer Hinckel B, Kruckeberg BM et al (2016) Anatomical patella instability risk factors on MRI show sensitivity without specificity in patients with patellofemoral instability: a systematic review. J ISAKOS 1:141\u2013152. https:\/\/doi.org\/10.1136\/jisakos-2015-000015","journal-title":"J ISAKOS"},{"key":"10596_CR23","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1177\/0363546514565768","volume":"43","author":"N Skelley","year":"2015","unstructured":"Skelley N, Friedman M, McGinnis M et al (2015) Inter- and intraobserver reliability in the MRI measurement of the tibial tubercle-trochlear groove distance and trochlea dysplasia. Am J Sports Med 43:873\u2013878. https:\/\/doi.org\/10.1177\/0363546514565768","journal-title":"Am J Sports Med"},{"key":"10596_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12891-019-2697-7","volume":"20","author":"Q Ye","year":"2019","unstructured":"Ye Q, Yu T, Wu Y et al (2019) Patellar instability: the reliability of magnetic resonance imaging measurement parameters. BMC Musculoskelet Disord 20:1\u20139. https:\/\/doi.org\/10.1186\/s12891-019-2697-7","journal-title":"BMC Musculoskelet Disord"},{"key":"10596_CR25","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/978-3-030-00937-3_18","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"A Tuysuzoglu","year":"2018","unstructured":"Tuysuzoglu A, Tan J, Eissa K et al (2018) Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging. In: Frangi AF, Schnabel JA, Davatzikos C et al (eds) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018. Springer International Publishing, Cham, pp 151\u2013158"},{"key":"10596_CR26","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1007\/978-3-030-32226-7_60","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings","author":"Z Zhong","year":"2019","unstructured":"Zhong Z, Li J, Zhang Z et al (2019) An attention-guided deep regression model for landmark detection in cephalograms. In: Part VI (ed) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings. Springer-Verlag, Berlin, Heidelberg, pp 540\u2013548"},{"key":"10596_CR27","doi-asserted-by":"crossref","unstructured":"Goutham END, Vasamsetti S, Kishore PV v, Sardana HK (2019) Automatic localization of landmarks in cephalometric images via modified U-Net. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp 1\u20136","DOI":"10.1109\/ICCCNT45670.2019.8944411"},{"key":"10596_CR28","doi-asserted-by":"publisher","first-page":"112633","DOI":"10.1109\/ACCESS.2020.3002939","volume":"8","author":"J Qian","year":"2020","unstructured":"Qian J, Luo W, Cheng M et al (2020) CephaNN: a multi-head attention network for cephalometric landmark detection. IEEE Access 8:112633\u2013112641. https:\/\/doi.org\/10.1109\/ACCESS.2020.3002939","journal-title":"IEEE Access"},{"key":"10596_CR29","doi-asserted-by":"crossref","unstructured":"Tan Z, Feng J, Zhou J (2021) Multi-task learning network for landmark detection in anatomical tree structures. In: Proceedings - International Symposium on Biomedical Imaging. IEEE Computer Society, pp 1975\u20131979","DOI":"10.1109\/ISBI48211.2021.9434017"},{"key":"10596_CR30","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.media.2019.03.007","volume":"54","author":"C Payer","year":"2019","unstructured":"Payer C, \u0160tern D, Bischof H, Urschler M (2019) Integrating spatial configuration into heatmap regression based CNNs for landmark localization. Med Image Anal 54:207\u2013219. https:\/\/doi.org\/10.1016\/j.media.2019.03.007","journal-title":"Med Image Anal"},{"key":"10596_CR31","doi-asserted-by":"publisher","unstructured":"Xue H, Artico J, Fontana M et al (2021) Landmark detection in cardiac MRI by using a convolutional neural network. Radiol Artif Intell 3. https:\/\/doi.org\/10.1148\/RYAI.2021200197","DOI":"10.1148\/RYAI.2021200197"},{"key":"10596_CR32","first-page":"234","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015. Springer International Publishing, Cham, pp 234\u2013241"},{"key":"10596_CR33","doi-asserted-by":"publisher","unstructured":"Harrison D, de Leo FC, Gallin WJ et al (2021) Machine learning applications of convolutional neural networks and unet architecture to predict and classify demosponge behavior. Water 13. https:\/\/doi.org\/10.3390\/w13182512","DOI":"10.3390\/w13182512"},{"key":"10596_CR34","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/978-1-4842-4470-8_7","volume-title":"Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners","author":"E Bisong","year":"2019","unstructured":"Bisong E (2019) Google Colaboratory. Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners. Apress, Berkeley, CA, pp 59\u201364"},{"key":"10596_CR35","doi-asserted-by":"publisher","DOI":"10.1117\/1.jmi.4.1.014501","volume":"4","author":"S\u00d6 Arik","year":"2017","unstructured":"Arik S\u00d6, Ibragimov B, Xing L (2017) Fully automated quantitative cephalometry using convolutional neural networks. J Med Imaging 4:014501. https:\/\/doi.org\/10.1117\/1.jmi.4.1.014501","journal-title":"J Med Imaging"},{"key":"10596_CR36","doi-asserted-by":"publisher","unstructured":"Song Y, Qiao X, Iwamoto Y, Chen YW (2020) Automatic cephalometric landmark detection on X-ray images using a deep-learning method. Applied Sciences (Switzerland) 10. https:\/\/doi.org\/10.3390\/app10072547","DOI":"10.3390\/app10072547"},{"key":"10596_CR37","doi-asserted-by":"crossref","unstructured":"Tiulpin A, Melekhov I, Saarakkala S (2019) KNEEL: Knee anatomical landmark localization using hourglass networks. In: Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019. Institute of Electrical and Electronics Engineers Inc., pp 352\u2013361","DOI":"10.1109\/ICCVW.2019.00046"},{"key":"10596_CR38","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","volume":"15","author":"TK Koo","year":"2016","unstructured":"Koo TK, Li MY (2016) A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med 15:155\u2013163. https:\/\/doi.org\/10.1016\/j.jcm.2016.02.012","journal-title":"J Chiropr Med"},{"key":"10596_CR39","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/s00247-015-3459-9","volume":"46","author":"A Mundy","year":"2016","unstructured":"Mundy A, Ravindra A, Yang J et al (2016) Standardization of patellofemoral morphology in the pediatric knee. Pediatr Radiol 46:255\u2013262. https:\/\/doi.org\/10.1007\/s00247-015-3459-9","journal-title":"Pediatr Radiol"},{"key":"10596_CR40","doi-asserted-by":"publisher","unstructured":"Joseph SM, Cheng C, Solomito MJ, Pace JL (2020) Lateral trochlear inclination angle: measurement via a 2-image technique to reliably characterize and quantify trochlear dysplasia. Orthop J Sports Med 8. https:\/\/doi.org\/10.1177\/2325967120958415","DOI":"10.1177\/2325967120958415"},{"key":"10596_CR41","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1007\/s00256-010-0961-x","volume":"40","author":"TO Smith","year":"2011","unstructured":"Smith TO, Davies L, Toms AP et al (2011) The reliability and validity of radiological assessment for patellar instability. A systematic review and meta-analysis. Skeletal Radiol 40:399\u2013414. https:\/\/doi.org\/10.1007\/s00256-010-0961-x","journal-title":"Skeletal Radiol"},{"key":"10596_CR42","doi-asserted-by":"publisher","first-page":"3021","DOI":"10.1007\/s00167-015-3614-8","volume":"24","author":"RS Thakkar","year":"2016","unstructured":"Thakkar RS, del Grande F, Wadhwa V et al (2016) Patellar instability: CT and MRI measurements and their correlation with internal derangement findings. Knee Surg Sports Traumatol Arthrosc 24:3021\u20133028. https:\/\/doi.org\/10.1007\/s00167-015-3614-8","journal-title":"Knee Surg Sports Traumatol Arthrosc"},{"key":"10596_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knee.2021.11.006","volume":"34","author":"N Degen","year":"2022","unstructured":"Degen N, Daniel T, Sass J et al (2022) A new 3D software for analysis and planning of lower limb and patellofemoral alignment: reliability and accuracy. Knee 34:1\u20138. https:\/\/doi.org\/10.1016\/j.knee.2021.11.006","journal-title":"Knee"},{"key":"10596_CR44","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s002640050002","volume":"24","author":"AD Delgado-Mart\u00ednez","year":"2000","unstructured":"Delgado-Mart\u00ednez AD, Rodr\u00edguez-Merch\u00e1n EC, Ballesteros R, Luna JD (2000) Reproducibility of patellofemoral CT scan measurements. Int Orthop 24:5\u20138. https:\/\/doi.org\/10.1007\/s002640050002","journal-title":"Int Orthop"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-024-10596-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-024-10596-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-024-10596-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T12:08:24Z","timestamp":1725019704000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-024-10596-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,10]]},"references-count":44,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["10596"],"URL":"https:\/\/doi.org\/10.1007\/s00330-024-10596-9","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,10]]},"assertion":[{"value":"15 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The scientific guarantor of this publication is Professor Cristina P. Santos.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The study was approved by the Ethics Committee of Trofa Sa\u00fade.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"None.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"value":"\u2022 retrospective\u2022 diagnostic study\u2022 multicentre study","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}