{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T05:53:07Z","timestamp":1791352387843,"version":"4.3.1"},"reference-count":83,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Liaoning Provincial Education Department Surface Project","award":["No. LJKZ0127"],"award-info":[{"award-number":["No. LJKZ0127"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No.62101357"],"award-info":[{"award-number":["No.62101357"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BioMed Eng OnLine"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>The contouring of organs at risk (OARs) in head and neck cancer radiation treatment planning is a crucial, yet repetitive and time-consuming process. Recent studies have applied deep learning (DL) algorithms to automatically contour head and neck OARs. This study aims to conduct a systematic review and meta-analysis to summarize and analyze the performance of DL algorithms in contouring head and neck OARs. The objective is to assess the advantages and limitations of DL algorithms in contour planning of head and neck OARs.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>This study conducted a literature search of Pubmed, Embase and Cochrane Library databases, to include studies related to DL contouring head and neck OARs, and the dice similarity coefficient (DSC) of four categories of OARs from the results of each study are selected as effect sizes for meta-analysis. Furthermore, this study conducted a subgroup analysis of OARs characterized by image modality and image type.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>149 articles were retrieved, and 22 studies were included in the meta-analysis after excluding duplicate literature, primary screening, and re-screening. The combined effect sizes of DSC for brainstem, spinal cord, mandible, left eye, right eye, left optic nerve, right optic nerve, optic chiasm, left parotid, right parotid, left submandibular, and right submandibular are 0.87, 0.83, 0.92, 0.90, 0.90, 0.71, 0.74, 0.62, 0.85, 0.85, 0.82, and 0.82, respectively. For subgroup analysis, the combined effect sizes for segmentation of the brainstem, mandible, left optic nerve, and left parotid gland using CT and MRI images are 0.86\/0.92, 0.92\/0.90, 0.71\/0.73, and 0.84\/0.87, respectively. Pooled effect sizes using 2D and 3D images of the brainstem, mandible, left optic nerve, and left parotid gland for contouring are 0.88\/0.87, 0.92\/0.92, 0.75\/0.71 and 0.87\/0.85.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The use of automated contouring technology based on DL algorithms is an essential tool for contouring head and neck OARs, achieving high accuracy, reducing the workload of clinical radiation oncologists, and providing individualized, standardized, and refined treatment plans for implementing \"precision radiotherapy\". Improving DL performance requires the construction of high-quality data sets and enhancing algorithm optimization and innovation.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12938-023-01159-y","type":"journal-article","created":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T11:49:00Z","timestamp":1698839340000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis"],"prefix":"10.1186","volume":"22","author":[{"given":"Peiru","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinzhuo","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,1]]},"reference":[{"issue":"4","key":"1159_CR1","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/j.coms.2018.06.001","volume":"30","author":"N Cohen","year":"2018","unstructured":"Cohen N, Fedewa S, Chen AY. Epidemiology and demographics of the head and neck cancer population. Oral Maxillofac Surg Clin North Am. 2018;30(4):381\u201395.","journal-title":"Oral Maxillofac Surg Clin North Am"},{"issue":"1","key":"1159_CR2","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1002\/hed.27209","volume":"45","author":"AL Mazul","year":"2023","unstructured":"Mazul AL, Chidambaram S, Zevallos JP, Massa ST. Disparities in head and neck cancer incidence and trends by race\/ethnicity and sex. Head Neck. 2023;45(1):75\u201384. https:\/\/doi.org\/10.1002\/hed.27209. (Epub 20221006).","journal-title":"Head Neck"},{"issue":"3","key":"1159_CR3","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1007\/s13187-014-0753-4","volume":"30","author":"P Daraei","year":"2015","unstructured":"Daraei P, Moore CE. Racial disparity among the head and neck cancer population. J Cancer Educ. 2015;30(3):546\u201351. https:\/\/doi.org\/10.1007\/s13187-014-0753-4.","journal-title":"J Cancer Educ"},{"issue":"3","key":"1159_CR4","doi-asserted-by":"publisher","first-page":"32657","DOI":"10.1371\/journal.pone.0032657","volume":"7","author":"L Cole","year":"2012","unstructured":"Cole L, Polfus L, Peters ES. Examining the incidence of human papillomavirus-associated head and neck cancers by race and ethnicity in the U.S,, 1995\u20132005. PLoS ONE. 2012;7(3):32657. https:\/\/doi.org\/10.1371\/journal.pone.0032657. (Epub 20120320).","journal-title":"PLoS ONE"},{"key":"1159_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2022.104154","volume":"82","author":"SC Larsson","year":"2022","unstructured":"Larsson SC, Burgess S. Appraising the causal role of smoking in multiple diseases: a systematic review and meta-analysis of mendelian randomization studies. EBioMedicine. 2022;82: 104154. https:\/\/doi.org\/10.1016\/j.ebiom.2022.104154. (Epub 20220708).","journal-title":"EBioMedicine"},{"issue":"9","key":"1159_CR6","doi-asserted-by":"publisher","first-page":"1456","DOI":"10.1038\/s41416-020-01031-z","volume":"123","author":"G Di Credico","year":"2020","unstructured":"Di Credico G, Polesel J, Dal Maso L, Pauli F, Torelli N, Luce D, et al. Alcohol drinking and head and neck cancer risk: the joint effect of intensity and duration. Br J Cancer. 2020;123(9):1456\u201363. https:\/\/doi.org\/10.1038\/s41416-020-01031-z. (Epub 20200824).","journal-title":"Br J Cancer"},{"issue":"23","key":"1159_CR7","doi-asserted-by":"publisher","first-page":"1801","DOI":"10.1093\/jnci\/djm233","volume":"99","author":"KM Applebaum","year":"2007","unstructured":"Applebaum KM, Furniss CS, Zeka A, Posner MR, Smith JF, Bryan J, et al. Lack of association of alcohol and tobacco with Hpv16-associated head and neck cancer. J Natl Cancer Inst. 2007;99(23):1801\u201310. https:\/\/doi.org\/10.1093\/jnci\/djm233. (Epub 20071127).","journal-title":"J Natl Cancer Inst"},{"issue":"2","key":"1159_CR8","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1634\/theoncologist.11-2-146","volume":"11","author":"M Merlano","year":"2006","unstructured":"Merlano M. alternating chemotherapy and radiotherapy in locally advanced head and neck cancer: an alternative? Oncologist. 2006;11(2):146\u201351. https:\/\/doi.org\/10.1634\/theoncologist.11-2-146.","journal-title":"Oncologist"},{"key":"1159_CR9","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.oraloncology.2018.09.011","volume":"86","author":"DM Gujral","year":"2018","unstructured":"Gujral DM, Nutting CM. Patterns of failure, treatment outcomes and late toxicities of head and neck cancer in the current era of imrt. Oral Oncol. 2018;86:225\u201333. https:\/\/doi.org\/10.1016\/j.oraloncology.2018.09.011. (Epub 20181004).","journal-title":"Oral Oncol"},{"issue":"10","key":"1159_CR10","doi-asserted-by":"publisher","first-page":"3594","DOI":"10.1002\/hed.25880","volume":"41","author":"M Baudelet","year":"2019","unstructured":"Baudelet M, Van den Steen L, Tomassen P, Bonte K, Deron P, Huvenne W, et al. Very late xerostomia, dysphagia, and neck fibrosis after head and neck radiotherapy. Head Neck. 2019;41(10):3594\u2013603. https:\/\/doi.org\/10.1002\/hed.25880. (Epub 20190722).","journal-title":"Head Neck"},{"issue":"4","key":"1159_CR11","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1007\/s11764-018-0687-7","volume":"12","author":"SL Crowder","year":"2018","unstructured":"Crowder SL, Douglas KG, Yanina Pepino M, Sarma KP, Arthur AE. Nutrition impact symptoms and associated outcomes in post-chemoradiotherapy head and neck cancer survivors: a systematic review. J Cancer Surviv. 2018;12(4):479\u201394. https:\/\/doi.org\/10.1007\/s11764-018-0687-7. (Epub 20180320).","journal-title":"J Cancer Surviv"},{"issue":"5","key":"1159_CR12","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1016\/s1808-8694","volume":"72","author":"BC Jham","year":"2006","unstructured":"Jham BC, da Silva Freire AR. Oral Complications of Radiotherapy in the Head and Neck. Braz J Otorhinolaryngol. 2006;72(5):704\u20138. https:\/\/doi.org\/10.1016\/s1808-8694.","journal-title":"Braz J Otorhinolaryngol"},{"issue":"1","key":"1159_CR13","doi-asserted-by":"publisher","first-page":"120","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. 2021;16(1):120. https:\/\/doi.org\/10.1186\/s13014-020-01677-2.","journal-title":"Radiat Oncol"},{"issue":"1","key":"1159_CR14","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.radonc.2005.04.010","volume":"77","author":"X Geets","year":"2005","unstructured":"Geets X, Daisne JF, Arcangeli S, Coche E, De Poel M, Duprez T, Nardella G, Gr\u00e9goire V. Inter-observer variability in the delineation of pharyngo-laryngeal tumor, parotid glands and cervical spinal cord: comparison between CT-scan and MRI. Radiother Oncol. 2005;77(1):25\u201331. https:\/\/doi.org\/10.1016\/j.radonc.2005.04.010.","journal-title":"Radiother Oncol"},{"key":"1159_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.oraloncology.2018.04.025","volume":"82","author":"YL Peng","year":"2018","unstructured":"Peng YL, Chen L, Shen GZ, Li YN, Yao JJ, Xiao WW, Yang L, Zhou S, Li JX, Cheng WQ, et al. Interobserver variations in the delineation of target volumes and organs at risk and their impact on dose distribution in intensity-modulated radiation therapy for nasopharyngeal carcinoma. Oral Oncol. 2018;82:1\u20137.","journal-title":"Oral Oncol"},{"key":"1159_CR16","first-page":"121","volume":"36","author":"R Zukauskaite","year":"2022","unstructured":"Zukauskaite R, Rumley CN, Hansen CR, Jameson MG, Trada Y, Johansen J, Gyldenkerne N, Eriksen JG, Aly F, Christensen RL, et al. Delineation uncertainties of tumour volumes on MRI of head and neck cancer patients. Clin Transl Radiat Oncol. 2022;36:121\u20136.","journal-title":"Clin Transl Radiat Oncol"},{"issue":"11","key":"1159_CR17","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2020.27426","volume":"3","author":"O Oktay","year":"2020","unstructured":"Oktay O, Nanavati J, Schwaighofer A, Carter D, Bristow M, Tanno R, Jena R, Barnett G, Noble D, Rimmer Y, et al. Evaluation of deep learning to augment image-guided radiotherapy for head and neck and prostate cancers. JAMA Netw Open. 2020;3(11): e2027426.","journal-title":"JAMA Netw Open"},{"issue":"1","key":"1159_CR18","doi-asserted-by":"publisher","first-page":"6137","DOI":"10.1038\/s41467-022-33178-z","volume":"13","author":"X Ye","year":"2022","unstructured":"Ye X, Guo D, Ge J, Yan S, Xin Y, Song Y, et al. Comprehensive and clinically accurate head and neck cancer organs-at-risk delineation on a multi-institutional study. Nat Commun. 2022;13(1):6137. https:\/\/doi.org\/10.1038\/s41467-022-33178-z. (Epub 20221017).","journal-title":"Nat Commun"},{"issue":"5","key":"1159_CR19","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/s00066-014-0634-0","volume":"190","author":"H Vorwerk","year":"2014","unstructured":"Vorwerk H, Zink K, Schiller R, Budach V, B\u00f6hmer D, Kampfer S, Popp W, Sack H, Engenhart-Cabillic R. Protection of quality and innovation in radiation oncology: the prospective multicenter trial the German Society of Radiation Oncology (DEGRO-QUIRO study). Evaluation of time, attendance of medical staff, and resources during radiotherapy with IMRT. Strahlenther Onkol. 2014;190(5):433\u201343.","journal-title":"Strahlenther Onkol"},{"key":"1159_CR20","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1186\/1748-717X-7-160","volume":"7","author":"M La Macchia","year":"2012","unstructured":"La Macchia M, Fellin F, Amichetti M, Cianchetti M, Gianolini S, Paola V, Lomax AJ, Widesott L. Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer. Radiat Oncol. 2012;7:160.","journal-title":"Radiat Oncol"},{"issue":"1","key":"1159_CR21","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s13246-023-01229-4","volume":"46","author":"M Douglass","year":"2023","unstructured":"Douglass M, Gorayski P, Patel S, Santos A. Synthetic cranial MRI from 3D optical surface scans using deep learning for radiation therapy treatment planning. Phys Eng Sci Med. 2023;46(1):367\u201375.","journal-title":"Phys Eng Sci Med"},{"key":"1159_CR22","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.radonc.2021.04.019","volume":"160","author":"X Chen","year":"2021","unstructured":"Chen X, Sun S, Bai N, Han K, Liu Q, Yao S, Tang H, Zhang C, Lu Z, Huang Q, et al. A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy. Radiother Oncol. 2021;160:175\u201384.","journal-title":"Radiother Oncol"},{"key":"1159_CR23","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.b2700","volume":"339","author":"A Liberati","year":"2009","unstructured":"Liberati A, Altman DG, Tetzlaff J, Mulrow C, G\u00f8tzsche PC, Ioannidis JP, Clarke M, Devereaux PJ, Kleijnen J, Moher D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339: b2700.","journal-title":"BMJ"},{"issue":"2","key":"1159_CR24","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020200029","volume":"2","author":"J Mongan","year":"2020","unstructured":"Mongan J, Moy L, Kahn CE Jr. Checklist for artificial intelligence in medical imaging (claim): a guide for authors and reviewers. Radiol Artif Intell. 2020;2(2): 200029. https:\/\/doi.org\/10.1148\/ryai.2020200029. (Epub 20200325).","journal-title":"Radiol Artif Intell."},{"issue":"1","key":"1159_CR25","doi-asserted-by":"publisher","first-page":"51","DOI":"10.7326\/m18-1376","volume":"170","author":"RF Wolff","year":"2019","unstructured":"Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al. Probast: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. 2019;170(1):51\u20138. https:\/\/doi.org\/10.7326\/m18-1376.","journal-title":"Ann Intern Med"},{"key":"1159_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.arr.2022.101614","volume":"77","author":"TO Frizzell","year":"2022","unstructured":"Frizzell TO, Glashutter M, Liu CC, Zeng A, Pan D, Hajra SG, et al. Artificial intelligence in brain mri analysis of alzheimer\u2019s disease over the past 12 years: a systematic review. Ageing Res Rev. 2022;77: 101614. https:\/\/doi.org\/10.1016\/j.arr.2022.101614. (Epub 20220328).","journal-title":"Ageing Res Rev"},{"issue":"10","key":"1159_CR27","doi-asserted-by":"publisher","first-page":"5862","DOI":"10.1002\/mp.15146","volume":"48","author":"X Dai","year":"2021","unstructured":"Dai X, Lei Y, Wang T, Zhou J, Roper J, McDonald M, et al. Automated delineation of head and neck organs at risk using synthetic mri-aided mask scoring regional convolutional neural network. Med Phys. 2021;48(10):5862\u201373. https:\/\/doi.org\/10.1002\/mp.15146. (Epub 20210818).","journal-title":"Med Phys"},{"key":"1159_CR28","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13825","author":"T Zhong","year":"2019","unstructured":"Zhong T, Huang X, Tang F, Liang S, Deng X, Zhang Y. Boosting-based cascaded convolutional neural networks for the segmentation of ct organs-at-risk in nasopharyngeal carcinoma. Med Phys. 2019. https:\/\/doi.org\/10.1002\/mp.13825. (Epub 20190916).","journal-title":"Med Phys"},{"issue":"12","key":"1159_CR29","doi-asserted-by":"publisher","first-page":"7757","DOI":"10.1002\/mp.15290","volume":"48","author":"JC Korte","year":"2021","unstructured":"Korte JC, Hardcastle N, Ng SP, Clark B, Kron T, Jackson P. Cascaded deep learning-based auto-segmentation for head and neck cancer patients: organs at risk on T2-weighted magnetic resonance imaging. Med Phys. 2021;48(12):7757\u201372. https:\/\/doi.org\/10.1002\/mp.15290. (Epub 20211101).","journal-title":"Med Phys"},{"issue":"5","key":"1159_CR30","doi-asserted-by":"publisher","first-page":"2204","DOI":"10.1002\/mp.13495","volume":"46","author":"JW Chan","year":"2019","unstructured":"Chan JW, Kearney V, Haaf S, Wu S, Bogdanov M, Reddick M, et al. A convolutional neural network algorithm for automatic segmentation of head and neck organs at risk using deep lifelong learning. Med Phys. 2019;46(5):2204\u201313. https:\/\/doi.org\/10.1002\/mp.13495. (Epub 20190404).","journal-title":"Med Phys"},{"issue":"4","key":"1159_CR31","doi-asserted-by":"publisher","first-page":"1961","DOI":"10.1007\/s00330-018-5748-9","volume":"29","author":"S Liang","year":"2019","unstructured":"Liang S, Tang F, Huang X, Yang K, Zhong T, Hu R, et al. Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning. Eur Radiol. 2019;29(4):1961\u20137. https:\/\/doi.org\/10.1007\/s00330-018-5748-9. (Epub 20181009).","journal-title":"Eur Radiol"},{"key":"1159_CR32","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13040702","author":"N Kim","year":"2021","unstructured":"Kim N, Chun J, Chang JS, Lee CG, Keum KC, Kim JS. Feasibility of continual deep learning-based segmentation for personalized adaptive radiation therapy in head and neck area. Cancers. 2021. https:\/\/doi.org\/10.3390\/cancers13040702. (Epub 20210209).","journal-title":"Cancers"},{"key":"1159_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101831","volume":"67","author":"Y Gao","year":"2021","unstructured":"Gao Y, Huang R, Yang Y, Zhang J, Shao K, Tao C, et al. Focusnetv 2: imbalanced large and small organ segmentation with adversarial shape constraint for head and neck ct images. Med Image Anal. 2021;67: 101831. https:\/\/doi.org\/10.1016\/j.media.2020.101831. (Epub 20201010).","journal-title":"Med Image Anal"},{"issue":"10","key":"1159_CR34","doi-asserted-by":"publisher","first-page":"4558","DOI":"10.1002\/mp.13147","volume":"45","author":"N Tong","year":"2018","unstructured":"Tong N, Gou S, Yang S, Ruan D, Sheng K. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks. Med Phys. 2018;45(10):4558\u201367. https:\/\/doi.org\/10.1002\/mp.13147. (Epub 20180919).","journal-title":"Med Phys"},{"key":"1159_CR35","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac2206","author":"Y Fang","year":"2021","unstructured":"Fang Y, Wang J, Ou X, Ying H, Hu C, Zhang Z, et al. the impact of training sample size on deep learning-based organ auto-segmentation for head-and-neck patients. Phys Med Biol. 2021. https:\/\/doi.org\/10.1088\/1361-6560\/ac2206. (Epub 20210914).","journal-title":"Phys Med Biol"},{"key":"1159_CR36","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.radonc.2019.09.022","volume":"142","author":"LV Van Dijk","year":"2020","unstructured":"Van Dijk LV, Van den Bosch L, Aljabar P, Peressutti D, Both S, Steenbakkers RJ, et al. Improving automatic delineation for head and neck organs at risk by deep learning contouring. Radiother Oncol. 2020;142:115\u201323. https:\/\/doi.org\/10.1016\/j.radonc.2019.09.022. (Epub 20191022).","journal-title":"Radiother Oncol"},{"key":"1159_CR37","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac3b34","author":"X Dai","year":"2022","unstructured":"Dai X, Lei Y, Wang T, Zhou J, Rudra S, McDonald M, et al. Multi-organ auto-delineation in head-and-neck mri for radiation therapy using regional convolutional neural network. Phys Med Biol. 2022. https:\/\/doi.org\/10.1088\/1361-6560\/ac3b34. (Epub 20220121).","journal-title":"Phys Med Biol"},{"issue":"5","key":"1159_CR38","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1007\/s11548-019-01922-4","volume":"14","author":"E Tappeiner","year":"2019","unstructured":"Tappeiner E, Pr\u00f6ll S, H\u00f6nig M, Raudaschl PF, Zaffino P, Spadea MF, et al. Multi-organ segmentation of the head and neck area: an efficient hierarchical neural networks approach. Int J Comput Assist Radiol Surg. 2019;14(5):745\u201354. https:\/\/doi.org\/10.1007\/s11548-019-01922-4. (Epub 20190307).","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"24","key":"1159_CR39","doi-asserted-by":"publisher","first-page":"245034","DOI":"10.1088\/1361-6560\/ab79c3","volume":"65","author":"S Gou","year":"2020","unstructured":"Gou S, Tong N, Qi S, Yang S, Chin R, Sheng K. Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck Ct images. Phys Med Biol. 2020;65(24):245034. https:\/\/doi.org\/10.1088\/1361-6560\/ab79c3. (Epub 20201211).","journal-title":"Phys Med Biol"},{"issue":"6","key":"1159_CR40","doi-asserted-by":"publisher","first-page":"2669","DOI":"10.1002\/mp.13553","volume":"46","author":"N Tong","year":"2019","unstructured":"Tong N, Gou S, Yang S, Cao M, Sheng K. Shape constrained fully convolutional densenet with adversarial training for multiorgan segmentation on head and neck ct and low-field mr images. Med Phys. 2019;46(6):2669\u201382. https:\/\/doi.org\/10.1002\/mp.13553. (Epub 20190506).","journal-title":"Med Phys"},{"issue":"1","key":"1159_CR41","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1093\/jrr\/rraa094","volume":"62","author":"S Zhang","year":"2021","unstructured":"Zhang S, Wang H, Tian S, Zhang X, Li J, Lei R, et al. A slice classification model-facilitated 3d encoder-decoder network for segmenting organs at risk in head and neck cancer. J Radiat Res. 2021;62(1):94\u2013103. https:\/\/doi.org\/10.1093\/jrr\/rraa094.","journal-title":"J Radiat Res"},{"issue":"11","key":"1159_CR42","doi-asserted-by":"publisher","first-page":"7052","DOI":"10.1002\/mp.15287","volume":"48","author":"Z Zhang","year":"2021","unstructured":"Zhang Z, Zhao T, Gay H, Zhang W, Sun B. Weaving attention U-Net: a novel hybrid cnn and attention-based method for organs-at-risk segmentation in head and neck ct images. Med Phys. 2021;48(11):7052\u201362. https:\/\/doi.org\/10.1002\/mp.15287.","journal-title":"Med Phys"},{"issue":"9","key":"1159_CR43","doi-asserted-by":"publisher","first-page":"2794","DOI":"10.1109\/tmi.2020.2975853","volume":"39","author":"S Liang","year":"2020","unstructured":"Liang S, Thung KH, Nie D, Zhang Y, Shen D. Multi-view spatial aggregation framework for joint localization and segmentation of organs at risk in head and neck Ct images. IEEE Trans Med Imaging. 2020;39(9):2794\u2013805. https:\/\/doi.org\/10.1109\/tmi.2020.2975853.","journal-title":"IEEE Trans Med Imaging"},{"key":"1159_CR44","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.radonc.2022.06.024","volume":"174","author":"J Koo","year":"2022","unstructured":"Koo J, Caudell JJ, Latifi K, Jordan P, Shen S, Adamson PM, et al. Comparative evaluation of a prototype deep learning algorithm for autosegmentation of normal tissues in head and neck radiotherapy. Radiother Oncol. 2022;174:52\u20138. https:\/\/doi.org\/10.1016\/j.radonc.2022.06.024.","journal-title":"Radiother Oncol"},{"issue":"6","key":"1159_CR45","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1212\/wnl.39.6.789","volume":"39","author":"LM DeAngelis","year":"1989","unstructured":"DeAngelis LM, Delattre JY, Posner JB. Radiation-induced dementia in patients cured of brain metastases. Neurology. 1989;39(6):789\u201396. https:\/\/doi.org\/10.1212\/wnl.39.6.789.","journal-title":"Neurology"},{"issue":"1","key":"1159_CR46","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.ijrobp.2010.05.013","volume":"81","author":"AH Wolfson","year":"2011","unstructured":"Wolfson AH, Bae K, Komaki R, et al. Primary analysis of a phase II randomized trial Radiation Therapy Oncology Group (RTOG) 0212: impact of different total doses and schedules of prophylactic cranial irradiation on chronic neurotoxicity and quality of life for patients with limited-disease small-cell lung cancer. Int J Radiat Oncol Biol Phys. 2011;81(1):77\u201384. https:\/\/doi.org\/10.1016\/j.ijrobp.2010.05.013.","journal-title":"Int J Radiat Oncol Biol Phys"},{"issue":"1","key":"1159_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.31661\/jbpe.v0i0.1055","volume":"10","author":"H Goyal","year":"2020","unstructured":"Goyal H, Singh N, Gurjar OP, Tanwar RK. Radiation induced demyelination in cervical spinal cord of the head and neck cancer patients after receiving radiotherapy. J Biomed Phys Eng. 2020;10(1):1\u20136. https:\/\/doi.org\/10.31661\/jbpe.v0i0.1055.","journal-title":"J Biomed Phys Eng."},{"issue":"1","key":"1159_CR48","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1016\/j.ijrobp.2010.10.019","volume":"82","author":"BE Nelms","year":"2012","unstructured":"Nelms BE, Tom\u00e9 WA, Robinson G, Wheeler J. Variations in the contouring of organs at risk: test case from a patient with oropharyngeal cancer. Int J Radiat Oncol Biol Phys. 2012;82(1):368\u201378. https:\/\/doi.org\/10.1016\/j.ijrobp.2010.10.019.","journal-title":"Int J Radiat Oncol Biol Phys"},{"issue":"5","key":"1159_CR49","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/S1470-2045(17)30252-8","volume":"18","author":"JJ Caudell","year":"2017","unstructured":"Caudell JJ, Torres-Roca JF, Gillies RJ, et al. The future of personalised radiotherapy for head and neck cancer[J]. Lancet Oncol. 2017;18(5):266\u201373.","journal-title":"Lancet Oncol"},{"issue":"7","key":"1159_CR50","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/S1470-2045(12)70237-1","volume":"13","author":"V Grkgoire","year":"2012","unstructured":"Grkgoire V, Jeraj R, Lee JA, et al. Radiotherapy for head and neck tumours in 2012 and beyond:Conformal, tailored, and adaptive[J]. Lancet Oncol. 2012;13(7):292\u2013300.","journal-title":"Lancet Oncol"},{"issue":"10","key":"1159_CR51","doi-asserted-by":"publisher","first-page":"1284","DOI":"10.1080\/0284186X.2018.1505053","volume":"57","author":"J Castelli","year":"2018","unstructured":"Castelli J, Simon A, Lafond C, et al. Adaptive radiotherapyfor head andneck cancer[J]. Acta Oncologica Taylor Francis. 2018;57(10):1284\u201392.","journal-title":"Acta Oncologica Taylor Francis"},{"key":"1159_CR52","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1002\/mp.12045","volume":"44","author":"B Ibragimov","year":"2017","unstructured":"Ibragimov B, Xing L. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys. 2017;44:547\u201357. https:\/\/doi.org\/10.1002\/mp.12045.","journal-title":"Med Phys"},{"key":"1159_CR53","doi-asserted-by":"publisher","first-page":"1673","DOI":"10.1002\/mp.14619","volume":"48","author":"JP Kieselmann","year":"2021","unstructured":"Kieselmann JP, Fuller CD, Gurney-Champion OJ, Oelfke U. Cross-modality deep learning: contouring of MRI data from annotated CT data only. Med Phys. 2021;48:1673\u201384. https:\/\/doi.org\/10.1002\/mp.14619.","journal-title":"Med Phys"},{"key":"1159_CR54","doi-asserted-by":"publisher","first-page":"4294","DOI":"10.1002\/mp.14378","volume":"47","author":"Y Liu","year":"2020","unstructured":"Liu Y, Lei Y, Fu Y, Wang T, Zhou J, Jiang X, et al. Head and neck multi-organ auto-segmentation on CT images aided by synthetic MRI. Med Phys. 2020;47:4294\u2013302. https:\/\/doi.org\/10.1002\/mp.14378.","journal-title":"Med Phys"},{"key":"1159_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2021.102000","volume":"95","author":"E Yee","year":"2022","unstructured":"Yee E, Ma D, Popuri K, Chen S, Lee H, Chow V, et al. 3D hemisphere-based convolutional neural network for whole-brain MRI segmentation. Comput Med Imaging Graph. 2022;95: 102000. https:\/\/doi.org\/10.1016\/j.compmedimag.2021.102000.","journal-title":"Comput Med Imaging Graph"},{"key":"1159_CR56","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.neuroimage.2019.03.041","volume":"194","author":"Y Huo","year":"2019","unstructured":"Huo Y, Xu Z, Xiong Y, Aboud K, Parvathaneni P, Bao S, et al. 3D whole brain segmentation using spatially localized atlas network tiles. Neuroimage. 2019;194:105\u201319. https:\/\/doi.org\/10.1016\/j.neuroimage.2019.03.041.","journal-title":"Neuroimage"},{"key":"1159_CR57","doi-asserted-by":"publisher","DOI":"10.1117\/1.Jmi.7.5.051202","volume":"7","author":"J Yu","year":"2020","unstructured":"Yu J, Yang B, Wang J, Leader J, Wilson D, Pu J. 2D CNN versus 3D CNN for false-positive reduction in lung cancer screening. J Med Imag. 2020;7: 051202. https:\/\/doi.org\/10.1117\/1.Jmi.7.5.051202.","journal-title":"J Med Imag"},{"key":"1159_CR58","doi-asserted-by":"publisher","DOI":"10.1117\/1.Jmi.9.3.036001","volume":"9","author":"R Gaikar","year":"2022","unstructured":"Gaikar R, Zabihollahy F, Elfaal MW, Azad A, Schieda N, Ukwatta E. Transfer learning-based approach for automated kidney segmentation on multiparametric MRI sequences. J Med Imaging. 2022;9: 036001. https:\/\/doi.org\/10.1117\/1.Jmi.9.3.036001.","journal-title":"J Med Imaging"},{"key":"1159_CR59","doi-asserted-by":"publisher","first-page":"6216","DOI":"10.1002\/mp.14391","volume":"47","author":"MH Vu","year":"2020","unstructured":"Vu MH, Grimbergen G, Nyholm T, L\u00f6fstedt T. Evaluation of multislice inputs to convolutional neural networks for medical image segmentation. Med Phys. 2020;47:6216\u201331. https:\/\/doi.org\/10.1002\/mp.14391.","journal-title":"Med Phys"},{"key":"1159_CR60","doi-asserted-by":"publisher","first-page":"6557593","DOI":"10.1155\/2022\/6557593","volume":"2022","author":"R Zhang","year":"2022","unstructured":"Zhang R, Zhuo L, Chen M, Yin H, Li X, Wang Z. Hybrid Deep Feature Fusion of 2D CNN and 3D CNN for Vestibule Segmentation from CT Images. Comput Math Methods Med. 2022;2022:6557593. https:\/\/doi.org\/10.1155\/2022\/6557593.","journal-title":"Comput Math Methods Med"},{"key":"1159_CR61","doi-asserted-by":"publisher","DOI":"10.3390\/s22041669","author":"JE Valdez-Rodr\u00edguez","year":"2022","unstructured":"Valdez-Rodr\u00edguez JE, Calvo H, Felipe-River\u00f3n E, Moreno-Armend\u00e1riz MA. Improving depth estimation by embedding semantic segmentation: a hybrid CNN model. Sensors. 2022. https:\/\/doi.org\/10.3390\/s22041669.","journal-title":"Sensors"},{"key":"1159_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102189","volume":"121","author":"L Gu","year":"2021","unstructured":"Gu L, Cai XC. Fusing 2D and 3D convolutional neural networks for the segmentation of aorta and coronary arteries from CT images. Artif Intell Med. 2021;121: 102189. https:\/\/doi.org\/10.1016\/j.artmed.2021.102189.","journal-title":"Artif Intell Med"},{"key":"1159_CR63","doi-asserted-by":"publisher","first-page":"210194","DOI":"10.1109\/access.2020.3038909","volume":"8","author":"J Lee","year":"2020","unstructured":"Lee J, Nishikawa RM. Cross-organ, cross-modality transfer learning: feasibility study for segmentation and classification. IEEE Access. 2020;8:210194\u2013205. https:\/\/doi.org\/10.1109\/access.2020.3038909.","journal-title":"IEEE Access"},{"key":"1159_CR64","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1007\/s10278-020-00347-9","volume":"33","author":"H Mzoughi","year":"2020","unstructured":"Mzoughi H, Njeh I, Wali A, Slima MB, BenHamida A, Mhiri C, et al. Deep multi-scale 3D convolutional neural network (CNN) for MRI gliomas brain tumor classification. J Digit Imaging. 2020;33:903\u201315. https:\/\/doi.org\/10.1007\/s10278-020-00347-9.","journal-title":"J Digit Imaging"},{"key":"1159_CR65","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab99e5","volume":"65","author":"S Yu","year":"2020","unstructured":"Yu S, Chen M, Zhang E, Wu J, Yu H, Yang Z, et al. Robustness study of noisy annotation in deep learning based medical image segmentation. Phys Med Biol. 2020;65: 175007. https:\/\/doi.org\/10.1088\/1361-6560\/ab99e5.","journal-title":"Phys Med Biol"},{"key":"1159_CR66","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.phro.2022.04.003","volume":"22","author":"EGA Henderson","year":"2022","unstructured":"Henderson EGA, Vasquez Osorio EM, van Herk M, Green AF. Optimising a 3D convolutional neural network for head and neck computed tomography segmentation with limited training data. Phys Imag Radiat Oncol. 2022;22:44\u201350. https:\/\/doi.org\/10.1016\/j.phro.2022.04.003.","journal-title":"Phys Imag Radiat Oncol"},{"key":"1159_CR67","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1002\/mp.14728","volume":"48","author":"Y Zhao","year":"2021","unstructured":"Zhao Y, Rhee DJ, Cardenas C, Court LE, Yang J. Training deep-learning segmentation models from severely limited data. Med Phys. 2021;48:1697\u2013706. https:\/\/doi.org\/10.1002\/mp.14728.","journal-title":"Med Phys"},{"key":"1159_CR68","doi-asserted-by":"publisher","first-page":"1686","DOI":"10.1002\/mp.15507","volume":"49","author":"A Amjad","year":"2022","unstructured":"Amjad A, Xu J, Thill D, Lawton C, Hall W, Awan MJ, et al. General and custom deep learning autosegmentation models for organs in head and neck, abdomen, and male pelvis. Med Phys. 2022;49:1686\u2013700. https:\/\/doi.org\/10.1002\/mp.15507.","journal-title":"Med Phys"},{"key":"1159_CR69","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.604","volume":"7","author":"P Gnip","year":"2021","unstructured":"Gnip P, Vokorokos L, Drot\u00e1r P. Selective oversampling approach for strongly imbalanced data. PeerJ Comput Sci. 2021;7: e604.","journal-title":"PeerJ Comput Sci"},{"issue":"2","key":"1159_CR70","doi-asserted-by":"publisher","first-page":"ooad033","DOI":"10.1093\/jamiaopen\/ooad033","volume":"6","author":"K Welvaars","year":"2023","unstructured":"Welvaars K, Oosterhoff JHF, van den Bekerom MPJ, Doornberg JN, van Haarst EP. Implications of resampling data to address the class imbalance problem (IRCIP): an evaluation of impact on performance between classification algorithms in medical data. JAMIA Open. 2023;6(2):ooad033.","journal-title":"JAMIA Open"},{"issue":"15","key":"1159_CR71","doi-asserted-by":"publisher","first-page":"6836","DOI":"10.3390\/s23156836","volume":"23","author":"M Priyadharshini","year":"2023","unstructured":"Priyadharshini M, Banu AF, Sharma B, Chowdhury S, Rabie K, Shongwe T. Hybrid multi-label classification model for medical applications based on adaptive synthetic data and ensemble learning. Sensors. 2023;23(15):6836.","journal-title":"Sensors"},{"key":"1159_CR72","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-023-02892-1","author":"A Kishore","year":"2023","unstructured":"Kishore A, Venkataramana L, Prasad DVV, Mohan A, Jha B. Enhancing the prediction of IDC breast cancer staging from gene expression profiles using hybrid feature selection methods and deep learning architecture. Med Biol Eng Comput. 2023. https:\/\/doi.org\/10.1007\/s11517-023-02892-1.","journal-title":"Med Biol Eng Comput"},{"key":"1159_CR73","doi-asserted-by":"publisher","first-page":"2103","DOI":"10.1007\/s11548-022-02649-5","volume":"17","author":"E Tappeiner","year":"2022","unstructured":"Tappeiner E, Welk M, Schubert R. Tackling the class imbalance problem of deep learning-based head and neck organ segmentation. Int J Comput Assist Radiol Surg. 2022;17:2103\u201311. https:\/\/doi.org\/10.1007\/s11548-022-02649-5.","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"1159_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2021.102026","volume":"95","author":"M Yeung","year":"2022","unstructured":"Yeung M, Sala E, Sch\u00f6nlieb CB, Rundo L. Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Comput Med Imaging Graph. 2022;95: 102026.","journal-title":"Comput Med Imaging Graph"},{"key":"1159_CR75","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab79c3","volume":"65","author":"S Gou","year":"2020","unstructured":"Gou S, Tong N, Qi S, Yang S, Chin R, Sheng K. Self-channel-and-spatial-attention neural network for automated multi- organ segmentation on head and neck CT images. Phys Med Biol. 2020;65: 245034. https:\/\/doi.org\/10.1088\/1361-6560\/ab79c3.","journal-title":"Phys Med Biol"},{"key":"1159_CR76","doi-asserted-by":"publisher","first-page":"e271","DOI":"10.1016\/s2589-7500(19)30123-2","volume":"1","author":"X Liu","year":"2019","unstructured":"Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. 2019;1:e271\u201397. https:\/\/doi.org\/10.1016\/s2589-7500(19)30123-2.","journal-title":"Lancet Digit Health"},{"key":"1159_CR77","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.jclinepi.2021.06.017","volume":"138","author":"E Venema","year":"2021","unstructured":"Venema E, Wessler BS, Paulus JK, Salah R, Raman G, Leung LY, et al. Large-scale validation of the prediction model risk of bias assessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination. J Clin Epidemiol. 2021;138:32\u20139. https:\/\/doi.org\/10.1016\/j.jclinepi.2021.06.017.","journal-title":"J Clin Epidemiol"},{"key":"1159_CR78","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.g7594","volume":"350","author":"GS Collins","year":"2015","unstructured":"Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350: g7594. https:\/\/doi.org\/10.1136\/bmj.g7594.","journal-title":"BMJ"},{"key":"1159_CR79","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1111\/nep.13913","volume":"26","author":"Y de Jong","year":"2021","unstructured":"de Jong Y, Ramspek CL, Zoccali C, Jager KJ, Dekker FW, van Diepen M. Appraising prediction research: a guide and meta-review on bias and applicability assessment using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Nephrology. 2021;26:939\u201347. https:\/\/doi.org\/10.1111\/nep.13913.","journal-title":"Nephrology"},{"key":"1159_CR80","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2022.05.022","author":"MJ Belue","year":"2022","unstructured":"Belue MJ, Harmon SA, Lay NS, Daryanani A, Phelps TE, Choyke PL, et al. The low rate of adherence to checklist for artificial intelligence in medical imaging criteria among published prostate MRI artificial intelligence algorithms. J Am Coll Radiol. 2022. https:\/\/doi.org\/10.1016\/j.jacr.2022.05.022.","journal-title":"J Am Coll Radiol"},{"key":"1159_CR81","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aacb65","volume":"63","author":"JP Kieselmann","year":"2018","unstructured":"Kieselmann JP, Kamerling CP, Burgos N, Menten MJ, Fuller CD, Nill S, et al. Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region. Phys Med Biol. 2018;63: 145007. https:\/\/doi.org\/10.1088\/1361-6560\/aacb65.","journal-title":"Phys Med Biol"},{"key":"1159_CR82","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.radonc.2018.03.012","volume":"127","author":"AR Delaney","year":"2018","unstructured":"Delaney AR, Dahele M, Slotman BJ, Verbakel W. Is accurate contouring of salivary and swallowing structures necessary to spare them in head and neck VMAT plans? Radiother Oncol. 2018;127:190\u20136. https:\/\/doi.org\/10.1016\/j.radonc.2018.03.012.","journal-title":"Radiother Oncol"},{"key":"1159_CR83","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.radonc.2021.09.019","volume":"164","author":"Y Gan","year":"2021","unstructured":"Gan Y, Langendijk JA, Oldehinkel E, Scandurra D, Sijtsema NM, Lin Z, et al. A novel semi auto-segmentation method for accurate dose and NTCP evaluation in adaptive head and neck radiotherapy. Radiother Oncol. 2021;164:167\u201374. https:\/\/doi.org\/10.1016\/j.radonc.2021.09.019.","journal-title":"Radiother Oncol"}],"container-title":["BioMedical Engineering OnLine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12938-023-01159-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12938-023-01159-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12938-023-01159-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T11:53:12Z","timestamp":1698839592000},"score":1,"resource":{"primary":{"URL":"https:\/\/biomedical-engineering-online.biomedcentral.com\/articles\/10.1186\/s12938-023-01159-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,1]]},"references-count":83,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["1159"],"URL":"https:\/\/doi.org\/10.1186\/s12938-023-01159-y","relation":{},"ISSN":["1475-925X"],"issn-type":[{"value":"1475-925X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,1]]},"assertion":[{"value":"14 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The review did not require approval by an Ethical Committee.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"104"}}