{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T19:54:45Z","timestamp":1776110085776,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-025-01624-z","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:24:04Z","timestamp":1748305444000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Multicentre evaluation of deep learning CT autosegmentation of the head and neck region for radiotherapy"],"prefix":"10.1038","volume":"8","author":[{"given":"Eric Pei Ping","family":"Pang","sequence":"first","affiliation":[]},{"given":"Hong Qi","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Fuqiang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jarkko","family":"Niemel\u00e4","sequence":"additional","affiliation":[]},{"given":"Gregory","family":"Bolard","sequence":"additional","affiliation":[]},{"given":"Susan","family":"Ramadan","sequence":"additional","affiliation":[]},{"given":"Timo","family":"Kiljunen","sequence":"additional","affiliation":[]},{"given":"Marta","family":"Capala","sequence":"additional","affiliation":[]},{"given":"Steven","family":"Petit","sequence":"additional","affiliation":[]},{"given":"Jan","family":"Sepp\u00e4l\u00e4","sequence":"additional","affiliation":[]},{"given":"Kristiina","family":"Vuolukka","sequence":"additional","affiliation":[]},{"given":"Ingrid","family":"Kiitam","sequence":"additional","affiliation":[]},{"given":"Danil","family":"Zolotuhhin","sequence":"additional","affiliation":[]},{"given":"Eduard","family":"Gershkevitsh","sequence":"additional","affiliation":[]},{"given":"Kaisa","family":"Lehti\u00f6","sequence":"additional","affiliation":[]},{"given":"Juha","family":"Nikkinen","sequence":"additional","affiliation":[]},{"given":"Jani","family":"Keyril\u00e4inen","sequence":"additional","affiliation":[]},{"given":"Miia","family":"Mokka","sequence":"additional","affiliation":[]},{"given":"Melvin Lee Kiang","family":"Chua","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,27]]},"reference":[{"key":"1624_CR1","first-page":"e54467","volume":"16","author":"S Mukherjee","year":"2024","unstructured":"Mukherjee, S., Vagha, S. & Gadkari, P. Navigating the Future: a comprehensive review of artificial intelligence applications in gastrointestinal cancer. Cureus 16, e54467 (2024).","journal-title":"Cureus"},{"key":"1624_CR2","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1158\/2159-8290.CD-23-1199","volume":"14","author":"W Lotter","year":"2024","unstructured":"Lotter, W. et al. Artificial intelligence in oncology: current landscape, challenges, and future directions. Cancer Discov. 14, 711\u2013726 (2024).","journal-title":"Cancer Discov."},{"key":"1624_CR3","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1038\/s41571-020-0417-8","volume":"17","author":"E Huynh","year":"2020","unstructured":"Huynh, E. et al. Artificial intelligence in radiation oncology. Nat. Rev. Clin. Oncol. 17, 771\u2013781 (2020).","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"1624_CR4","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.ijrobp.2023.10.033","volume":"119","author":"Y Rong","year":"2024","unstructured":"Rong, Y. et al. NRG oncology assessment of artificial intelligence deep learning-based auto-segmentation for radiation therapy: current developments, clinical considerations, and future directions. Int. J. Radiat. Oncol. Biol. Phys. 119, 261\u2013280 (2024).","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"1624_CR5","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.clon.2021.12.003","volume":"34","author":"K Harrison","year":"2022","unstructured":"Harrison, K. et al. Machine learning for auto-segmentation in radiotherapy planning. Clin. Oncol. (R. Coll. Radio.) 34, 74\u201388 (2022).","journal-title":"Clin. Oncol. (R. Coll. Radio.)"},{"key":"1624_CR6","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.semradonc.2019.02.001","volume":"29","author":"CE Cardenas","year":"2019","unstructured":"Cardenas, C. E., Yang, J., Anderson, B. M., Court, L. E. & Brock, K. B. Advances in auto-segmentation. Semin Radiat. Oncol. 29, 185\u2013197 (2019).","journal-title":"Semin Radiat. Oncol."},{"key":"1624_CR7","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1213068","volume":"13","author":"PJ Doolan","year":"2023","unstructured":"Doolan, P. J. et al. A clinical evaluation of the performance of five commercial artificial intelligence contouring systems for radiotherapy. Front Oncol. 13, 1213068 (2023).","journal-title":"Front Oncol."},{"key":"1624_CR8","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-33178-z","volume":"13","author":"X Ye","year":"2022","unstructured":"Ye, X. et al. Comprehensive and clinically accurate head and neck cancer organs-at-risk delineation on a multi-institutional study. Nat. Commun. 13, 6137 (2022).","journal-title":"Nat. Commun."},{"issue":"Suppl 2","key":"1624_CR9","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1002\/jmrs.618","volume":"70","author":"E Gibbons","year":"2023","unstructured":"Gibbons, E. et al. Clinical evaluation of deep learning and atlas-based auto-segmentation for critical organs at risk in radiation therapy. J. Med Radiat. Sci. 70(Suppl 2), 15\u201325 (2023).","journal-title":"J. Med Radiat. Sci."},{"key":"1624_CR10","doi-asserted-by":"publisher","first-page":"134","DOI":"10.3857\/roj.2019.00038","volume":"37","author":"A Ayyalusamy","year":"2019","unstructured":"Ayyalusamy, A. et al. Auto-segmentation of head and neck organs at risk in radiotherapy and its dependence on anatomic similarity. Radiat. Oncol. J. 37, 134\u2013142 (2019).","journal-title":"Radiat. Oncol. J."},{"key":"1624_CR11","doi-asserted-by":"publisher","unstructured":"Anand, A. et al. Study Design: Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planning. medRxiv, 2021.2012.2007.21266421, https:\/\/doi.org\/10.1101\/2021.12.07.21266421 (2021).","DOI":"10.1101\/2021.12.07.21266421"},{"key":"1624_CR12","doi-asserted-by":"publisher","first-page":"950","DOI":"10.1016\/j.ijrobp.2009.09.062","volume":"77","author":"PM Harari","year":"2010","unstructured":"Harari, P. M., Song, S. & Tom\u00e9, W. A. Emphasizing conformal avoidance versus target definition for IMRT planning in head-and-neck cancer. Int. J. Radiat. Oncol., Biol., Phys. 77, 950\u2013958 (2010).","journal-title":"Int. J. Radiat. Oncol., Biol., Phys."},{"key":"1624_CR13","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-020-01677-2","volume":"16","author":"J van der Veen","year":"2021","unstructured":"van der Veen, J., Gulyban, A., Willems, S., Maes, F. & Nuyts, S. Interobserver variability in organ at risk delineation in head and neck cancer. Radiat. Oncol. 16, 120 (2021).","journal-title":"Radiat. Oncol."},{"key":"1624_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.adro.2022.100925","volume":"8","author":"A Caissie","year":"2023","unstructured":"Caissie, A. et al. Head and neck radiation therapy patterns of practice variability identified as a challenge to real-world big data: results from the learning from analysis of multicentre big data aggregation (LAMBDA) consortium. Adv. Radiat. Oncol. 8, 100925 (2023).","journal-title":"Adv. Radiat. Oncol."},{"key":"1624_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2024.110337","volume":"197","author":"CP Nielsen","year":"2024","unstructured":"Nielsen, C. P. et al. Interobserver variation in organs at risk contouring in head and neck cancer according to the DAHANCA guidelines. Radiother. Oncol. 197, 110337 (2024).","journal-title":"Radiother. Oncol."},{"key":"1624_CR16","doi-asserted-by":"publisher","unstructured":"Lucido, J. J. et al. Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planning. Front. Oncol https:\/\/doi.org\/10.3389\/fonc.2023.1137803 (2023).","DOI":"10.3389\/fonc.2023.1137803"},{"key":"1624_CR17","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1016\/j.ijrobp.2009.09.023","volume":"77","author":"LJ Stapleford","year":"2010","unstructured":"Stapleford, L. J. et al. Evaluation of automatic atlas-based lymph node segmentation for head-and-neck cancer. Int J. Radiat. Oncol. Biol. Phys. 77, 959\u2013966 (2010).","journal-title":"Int J. Radiat. Oncol. Biol. Phys."},{"key":"1624_CR18","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.radonc.2022.05.018","volume":"173","author":"SS Almberg","year":"2022","unstructured":"Almberg, S. S. et al. Training, validation, and clinical implementation of a deep-learning segmentation model for radiotherapy of loco-regional breast cancer. Radiother. Oncol. 173, 62\u201368 (2022).","journal-title":"Radiother. Oncol."},{"key":"1624_CR19","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1016\/j.radi.2024.02.001","volume":"30","author":"S Singh","year":"2024","unstructured":"Singh, S., Singh, B. K. & Kumar, A. Multi-organ segmentation of organ-at-risk (OAR\u2019s) of head and neck site using ensemble learning technique. Radiogr. (Lond.) 30, 673\u2013680 (2024).","journal-title":"Radiogr. (Lond.)"},{"key":"1624_CR20","doi-asserted-by":"publisher","first-page":"5501","DOI":"10.3390\/cancers14225501","volume":"14","author":"VIJ Strijbis","year":"2022","unstructured":"Strijbis, V. I. J. et al. Deep learning for automated elective lymph node level segmentation for head and neck cancer radiotherapy. Cancers 14, 5501 (2022).","journal-title":"Cancers"},{"key":"1624_CR21","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1111\/1754-9485.13286","volume":"65","author":"G Samarasinghe","year":"2021","unstructured":"Samarasinghe, G. et al. Deep learning for segmentation in radiation therapy planning: a review. J. Med. Imaging Radiat. Oncol. 65, 578\u2013595 (2021).","journal-title":"J. Med. Imaging Radiat. Oncol."},{"key":"1624_CR22","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.14248","volume":"25","author":"S Luan","year":"2024","unstructured":"Luan, S. et al. Accurate and robust auto-segmentation of head and neck organ-at-risks based on a novel CNN fine-tuning workflow. J. Appl Clin. Med Phys. 25, e14248 (2024).","journal-title":"J. Appl Clin. Med Phys."},{"key":"1624_CR23","doi-asserted-by":"publisher","unstructured":"Kiljunen, T. et al. A deep learning-based automated CT segmentation of prostate cancer anatomy for radiation therapy planning-a retrospective multicenter study. Diagnostics (Basel) https:\/\/doi.org\/10.3390\/diagnostics10110959 (2020).","DOI":"10.3390\/diagnostics10110959"},{"key":"1624_CR24","doi-asserted-by":"publisher","first-page":"7118","DOI":"10.1002\/mp.15854","volume":"49","author":"JK Udupa","year":"2022","unstructured":"Udupa, J. K. et al. Combining natural and artificial intelligence for robust automatic anatomy segmentation: Application in neck and thorax auto-contouring. Med Phys. 49, 7118\u20137149 (2022).","journal-title":"Med Phys."},{"key":"1624_CR25","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.radonc.2019.03.004","volume":"135","author":"M Kosmin","year":"2019","unstructured":"Kosmin, M. et al. Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer. Radiother. Oncol. 135, 130\u2013140 (2019).","journal-title":"Radiother. Oncol."},{"key":"1624_CR26","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.meddos.2022.11.001","volume":"48","author":"JS Ginn","year":"2023","unstructured":"Ginn, J. S. et al. A clinical and time savings evaluation of a deep learning automatic contouring algorithm. Med Dosim. 48, 55\u201360 (2023).","journal-title":"Med Dosim."},{"key":"1624_CR27","doi-asserted-by":"publisher","first-page":"e43","DOI":"10.1002\/mp.12256","volume":"44","author":"KK Brock","year":"2017","unstructured":"Brock, K. K., Mutic, S., McNutt, T. R., Li, H. & Kessler, M. L. Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132. Med Phys. 44, e43\u2013e76 (2017).","journal-title":"Med Phys."},{"key":"1624_CR28","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.radonc.2020.05.038","volume":"150","author":"R Mir","year":"2020","unstructured":"Mir, R. et al. Organ at risk delineation for radiation therapy clinical trials: Global Harmonization Group consensus guidelines. Radiother. Oncol. 150, 30\u201339 (2020).","journal-title":"Radiother. Oncol."},{"key":"1624_CR29","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.radonc.2015.07.041","volume":"117","author":"CL Brouwer","year":"2015","unstructured":"Brouwer, C. L. et al. CT-based delineation of organs at risk in the head and neck region: DAHANCA, EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology and TROG consensus guidelines. Radiother. Oncol. 117, 83\u201390 (2015).","journal-title":"Radiother. Oncol."},{"key":"1624_CR30","volume":"47","author":"L Melerowitz","year":"2024","unstructured":"Melerowitz, L. et al. Design and evaluation of a deep learning-based automatic segmentation of maxillary and mandibular substructures using a 3D U-Net. Clin. Transl. Radiat. Oncol. 47, 100780 (2024).","journal-title":"Clin. Transl. Radiat. Oncol."},{"key":"1624_CR31","doi-asserted-by":"publisher","first-page":"1442","DOI":"10.1016\/j.ijrobp.2010.07.1977","volume":"81","author":"FM Kong","year":"2011","unstructured":"Kong, F. M. et al. Consideration of dose limits for organs at risk of thoracic radiotherapy: atlas for lung, proximal bronchial tree, esophagus, spinal cord, ribs, and brachial plexus. Int. J. Radiat. Oncol. Biol. Phys. 81, 1442\u20131457 (2011).","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"1624_CR32","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.radonc.2015.01.016","volume":"114","author":"S Scoccianti","year":"2015","unstructured":"Scoccianti, S. et al. Organs at risk in the brain and their dose-constraints in adults and in children: a radiation oncologist\u2019s guide for delineation in everyday practice. Radiother. Oncol. 114, 230\u2013238 (2015).","journal-title":"Radiother. Oncol."},{"key":"1624_CR33","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.radonc.2013.10.010","volume":"110","author":"V Gregoire","year":"2014","unstructured":"Gregoire, V. et al. Delineation of the neck node levels for head and neck tumors: a 2013 update. DAHANCA, EORTC, HKNPCSG, NCIC CTG, NCRI, RTOG, TROG consensus guidelines. Radiother. Oncol. 110, 172\u2013181 (2014).","journal-title":"Radiother. Oncol."},{"key":"1624_CR34","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1089807","volume":"13","author":"S Strolin","year":"2023","unstructured":"Strolin, S. et al. How smart is artificial intelligence in organs delineation? Testing a CE and FDA-approved Deep-Learning tool using multiple expert contours delineated on planning CT images. Front Oncol. 13, 1089807 (2023).","journal-title":"Front Oncol."},{"key":"1624_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.radonc.2019.01.018","volume":"134","author":"J Biau","year":"2019","unstructured":"Biau, J. et al. Selection of lymph node target volumes for definitive head and neck radiation therapy: a 2019 Update. Radiother. Oncol. 134, 1\u20139 (2019).","journal-title":"Radiother. Oncol."},{"key":"1624_CR36","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-024-03890-0","volume":"11","author":"X Luo","year":"2024","unstructured":"Luo, X. et al. A multicenter dataset for lymph node clinical target volume delineation of nasopharyngeal carcinoma. Sci. Data 11, 1085 (2024).","journal-title":"Sci. Data"},{"key":"1624_CR37","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.radonc.2021.05.003","volume":"160","author":"MV Sherer","year":"2021","unstructured":"Sherer, M. V. et al. Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review. Radiother. Oncol. 160, 185\u2013191 (2021).","journal-title":"Radiother. Oncol."},{"key":"1624_CR38","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-015-0068-x","volume":"15","author":"AA Taha","year":"2015","unstructured":"Taha, A. A. & Hanbury, A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15, 29 (2015).","journal-title":"BMC Med Imaging"},{"key":"1624_CR39","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1109\/83.748897","volume":"8","author":"DG Sim","year":"1999","unstructured":"Sim, D. G., Kwon, O. K. & Park, R. H. Object matching algorithms using robust Hausdorff distance measures. IEEE Trans. Image Process 8, 425\u2013429 (1999).","journal-title":"IEEE Trans. Image Process"},{"key":"1624_CR40","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.radonc.2017.04.019","volume":"123","author":"M Christiaens","year":"2017","unstructured":"Christiaens, M. et al. Quality assurance of radiotherapy in the ongoing EORTC 1219-DAHANCA-29 trial for HPV\/p16 negative squamous cell carcinoma of the head and neck: Results of the benchmark case procedure. Radiother. Oncol. 123, 424\u2013430 (2017).","journal-title":"Radiother. Oncol."},{"key":"1624_CR41","doi-asserted-by":"publisher","first-page":"050902","DOI":"10.1118\/1.4871620","volume":"41","author":"G Sharp","year":"2014","unstructured":"Sharp, G. et al. Vision 20\/20: perspectives on automated image segmentation for radiotherapy. Med Phys. 41, 050902 (2014).","journal-title":"Med Phys."},{"key":"1624_CR42","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1016\/j.clon.2010.05.006","volume":"22","author":"GG Hanna","year":"2010","unstructured":"Hanna, G. G., Hounsell, A. R. & O\u2019Sullivan, J. M. Geometrical analysis of radiotherapy target volume delineation: a systematic review of reported comparison methods. Clin. Oncol. 22, 515\u2013525 (2010).","journal-title":"Clin. Oncol."},{"key":"1624_CR43","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.1002\/mp.12752","volume":"45","author":"EA AlBadawy","year":"2018","unstructured":"AlBadawy, E. A., Saha, A. & Mazurowski, M. A. Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing. Med Phys. 45, 1150\u20131158 (2018).","journal-title":"Med Phys."},{"key":"1624_CR44","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/s41592-023-02151-z","volume":"21","author":"L Maier-Hein","year":"2024","unstructured":"Maier-Hein, L. et al. Metrics reloaded: recommendations for image analysis validation. Nat. Methods 21, 195\u2013212 (2024).","journal-title":"Nat. Methods"},{"key":"1624_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.phro.2019.12.001","volume":"13","author":"F Vaassen","year":"2020","unstructured":"Vaassen, F. et al. Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy. Phys. Imaging Radiat. Oncol. 13, 1\u20136 (2020).","journal-title":"Phys. Imaging Radiat. Oncol."},{"key":"1624_CR46","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1109\/TMI.2004.828354","volume":"23","author":"SK Warfield","year":"2004","unstructured":"Warfield, S. K., Zou, K. H. & Wells, W. M. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23, 903\u2013921 (2004).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1624_CR47","doi-asserted-by":"publisher","DOI":"10.1186\/1748-717X-6-110","volume":"6","author":"J Hwee","year":"2011","unstructured":"Hwee, J. et al. Technology assessment of automated atlas based segmentation in prostate bed contouring. Radiat. Oncol. 6, 110 (2011).","journal-title":"Radiat. Oncol."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01624-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01624-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01624-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T13:02:22Z","timestamp":1748350942000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01624-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,27]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1624"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-01624-z","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,27]]},"assertion":[{"value":"26 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that J. Niemel\u00e4 is one of the founders and shareholders of MVision AI Oy. All other authorsdeclare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"312"}}