{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T08:20:41Z","timestamp":1793780441508,"version":"4.6.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,7,15]],"date-time":"2020-07-15T00:00:00Z","timestamp":1594771200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,7,15]],"date-time":"2020-07-15T00:00:00Z","timestamp":1594771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p><jats:italic>Background<\/jats:italic> Deep learning has presented considerable potential and is gaining more importance in computer assisted diagnosis. As the gold standard for pathologically diagnosing cervical intraepithelial lesions and invasive cervical cancer, colposcopy-guided biopsy faces challenges in improving accuracy and efficiency worldwide, especially in developing countries. To ease the heavy burden of cervical cancer screening, it is urgent to establish a scientific, accurate and efficient method for assisting diagnosis and biopsy. <jats:italic>Methods<\/jats:italic> The data were collected to establish three deep-learning-based models. For every case, one saline image, one acetic image, one iodine image and the corresponding clinical information, including age, the results of human papillomavirus testing and cytology, type of transformation zone, and pathologic diagnosis, were collected. The dataset was proportionally divided into three subsets including the training set, the test set and the validation set, at a ratio of 8:1:1. The validation set was used to evaluate model performance. After model establishment, an independent dataset of high-definition images was collected to further evaluate the model performance. In addition, the comparison of diagnostic accuracy between colposcopists and models weas performed. <jats:italic>Results<\/jats:italic> The sensitivity, specificity and accuracy of the classification model to differentiate negative cases from positive cases were 85.38%, 82.62% and 84.10% respectively, with an AUC of 0.93. The recall and DICE of the segmentation model to segment suspicious lesions in acetic images were 84.73% and 61.64%, with an average accuracy of 95.59%. Furthermore, 84.67% of high-grade lesions were detected by the acetic detection model. Compared to colposcopists, the diagnostic system performed better in ordinary colposcopy images but slightly unsatisfactory in high-definition images. <jats:italic>Implications<\/jats:italic> The deep learning-based diagnostic system could help assist colposcopy diagnosis and biopsy for HSILs.<\/jats:p>","DOI":"10.1038\/s41598-020-68252-3","type":"journal-article","created":{"date-parts":[[2020,7,15]],"date-time":"2020-07-15T10:04:02Z","timestamp":1594807442000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":127,"title":["The application of deep learning based diagnostic system to cervical squamous intraepithelial lesions recognition in colposcopy images"],"prefix":"10.1038","volume":"10","author":[{"given":"Chunnv","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeli","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bei","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuechen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiguo","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,7,15]]},"reference":[{"key":"68252_CR1","unstructured":"Bruni LAG, Serrano B, Mena M, G\u00f3mez D, Mu\u00f1oz J, Bosch FX, de Sanjos\u00e9 S.ICO\/IARC Information Centre on HPV and Cancer (HPV Information Centre). Human papillomavirus and related diseases in china. Summary report. 17 June 2019."},{"key":"68252_CR2","doi-asserted-by":"crossref","unstructured":"Goodman A. Hpv testing as a screen for cervical cancer. BMJ, 2015,350(h2372).","DOI":"10.1136\/bmj.h2372"},{"issue":"10","key":"68252_CR3","first-page":"1058","volume":"319","author":"J Jin","year":"2018","unstructured":"Jin, J. Hpv infection and cancer. JAMA 319(10), 1058 (2018).","journal-title":"Hpv infection and cancer. JAMA"},{"issue":"3 Suppl 2","key":"68252_CR4","doi-asserted-by":"crossref","first-page":"S4","DOI":"10.1016\/j.ygyno.2008.07.045","volume":"110","author":"X Castellsague","year":"2008","unstructured":"Castellsague, X. Natural history and epidemiology of hpv infection and cervical cancer. Gynecol. Oncol. 110(3 Suppl 2), S4-7 (2008).","journal-title":"Gynecol. Oncol."},{"key":"68252_CR5","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1002\/(SICI)1096-9896(199909)189:1<12::AID-PATH431>3.0.CO;2-F","volume":"189","author":"JMM Walboomers","year":"1999","unstructured":"Walboomers, J. M. M. et al. Human papillomavirus is a necessary cause of invasive cervical cancer worldwide. J. Pathol. 189, 12\u201319 (1999).","journal-title":"J. Pathol."},{"issue":"10","key":"68252_CR6","doi-asserted-by":"crossref","first-page":"2223","DOI":"10.1002\/ijc.27790","volume":"132","author":"PJ Snijders","year":"2013","unstructured":"Snijders, P. J. et al. High-risk hpv testing on self-sampled versus clinician-collected specimens: a review on the clinical accuracy and impact on population attendance in cervical cancer screening. Int. J. Cancer 132(10), 2223\u20132236 (2013).","journal-title":"Int. J. Cancer"},{"issue":"1","key":"68252_CR7","doi-asserted-by":"crossref","first-page":"e7517","DOI":"10.1590\/1414-431x20187517","volume":"52","author":"J Wang","year":"2018","unstructured":"Wang, J. Analysis of the application values of different combination schemes of liquid-based cytology and high-risk human papilloma virus test in the screening of high-grade cervical lesions. Braz. J. Med. Biol. Res. 52(1), e7517 (2018).","journal-title":"Braz. J. Med. Biol. Res."},{"issue":"2","key":"68252_CR8","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/S1470-2045(13)70570-9","volume":"15","author":"M Arbyn","year":"2014","unstructured":"Arbyn, M. et al. Accuracy of human papillomavirus testing on self-collected versus clinician-collected samples: a meta-analysis. Lancet Oncol. 15(2), 172\u2013183 (2014).","journal-title":"Lancet Oncol."},{"key":"68252_CR9","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.ejogrb.2019.07.003","volume":"240","author":"BH Brown","year":"2019","unstructured":"Brown, B. H. & Tidy, J. A. The diagnostic accuracy of colposcopy - a review of research methodology and impact on the outcomes of quality assurance. Eur. J. Obstetrics Gynecol. Reprod. Biol. 240, 182\u2013186 (2019).","journal-title":"Eur. J. Obstetrics Gynecol. Reprod. Biol."},{"issue":"11","key":"68252_CR10","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1111\/j.1471-0528.2012.03444.x","volume":"119","author":"M Underwood","year":"2012","unstructured":"Underwood, M. et al. Accuracy of colposcopy-directed punch biopsies: a systematic review and meta-analysis. BJOG: Int. J. Obstetrics Gynaecol. 119(11), 1293\u20131301 (2012).","journal-title":"BJOG: Int. J. Obstetrics Gynaecol."},{"key":"68252_CR11","doi-asserted-by":"crossref","unstructured":"Sideri M, Garutti P, Costa S, et al. Accuracy of colposcopically directed biopsy: Results from an online quality assurance programme for colposcopy in a population-based cervical screening setting in Italy. BioMed Res. Int., 2015, 614035.","DOI":"10.1155\/2015\/614035"},{"issue":"3","key":"68252_CR12","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1097\/LGT.0b013e31819308d4","volume":"13","author":"LS Massad","year":"2009","unstructured":"Massad, L. S. et al. The accuracy of colposcopic grading for detection of high-grade cervical intraepithelial neoplasia. J. Lower Genital Tract Disease 13(3), 137\u2013144 (2009).","journal-title":"J. Lower Genital Tract Disease"},{"issue":"2","key":"68252_CR13","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1097\/LGT.0000000000000454","volume":"23","author":"WK Huh","year":"2019","unstructured":"Huh, W. K., Papagiannakis, E. & Gold, M. A. Observed colposcopy practice in us community-based clinics: The retrospective control arm of the improve-colpo study. J. Lower Genital Tract Disease 23(2), 110\u2013115 (2019).","journal-title":"J. Lower Genital Tract Disease"},{"issue":"9","key":"68252_CR14","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1038\/s41591-018-0181-9","volume":"24","author":"CJLC Liston","year":"2018","unstructured":"Liston, C. J. L. C. New machine-learning technologies for computer-aided diagnosis. Nat. Med. 24(9), 1306\u20131307 (2018).","journal-title":"Nat. Med."},{"issue":"10","key":"68252_CR15","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","volume":"24","author":"N Coudray","year":"2018","unstructured":"Coudray, N. et al. Classification and mutation prediction from non\u2013small cell lung cancer histopathology images using deep learning. Nat. Med. 24(10), 1559\u20131567 (2018).","journal-title":"Nat. Med."},{"issue":"5","key":"68252_CR16","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1038\/s41591-018-0029-3","volume":"24","author":"DSW Ting","year":"2018","unstructured":"Ting, D. S. W. et al. Ai for medical imaging goes deep. Nat Med 24(5), 539\u2013540 (2018).","journal-title":"Nat Med"},{"issue":"5","key":"68252_CR17","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany, D. S. et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122\u20131131 (2018).","journal-title":"Cell"},{"issue":"8","key":"68252_CR18","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","volume":"18","author":"A Hosny","year":"2018","unstructured":"Hosny, A. et al. Artificial intelligence in radiology. Nat. Rev. Cancer 18(8), 500\u2013510 (2018).","journal-title":"Nat. Rev. Cancer"},{"issue":"10162","key":"68252_CR19","doi-asserted-by":"crossref","first-page":"2388","DOI":"10.1016\/S0140-6736(18)31645-3","volume":"392","author":"S Chilamkurthy","year":"2018","unstructured":"Chilamkurthy, S. et al. Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study. Lancet 392(10162), 2388\u20132396 (2018).","journal-title":"Lancet"},{"key":"68252_CR20","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/S2468-1253(18)30282-6","volume":"4","author":"OF Ahmad","year":"2019","unstructured":"Ahmad, O. F. et al. Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions. Lancet Gastroenterol. Hepatol. 4, 71\u201380 (2019).","journal-title":"Lancet Gastroenterol. Hepatol."},{"issue":"9","key":"68252_CR21","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1016\/j.compbiomed.2009.06.006","volume":"39","author":"HG Acosta-Mesa","year":"2009","unstructured":"Acosta-Mesa, H. G., Cruz-Ramirez, N. & Hernandez-Jimenez, R. Aceto-white temporal pattern classification using k-nn to identify precancerous cervical lesion in colposcopic images. Comput. Biol. Med. 39(9), 778\u2013784 (2009).","journal-title":"Comput. Biol. Med."},{"issue":"8","key":"68252_CR22","doi-asserted-by":"crossref","first-page":"2306","DOI":"10.1109\/TBME.2018.2887208","volume":"66","author":"MN Asiedu","year":"2019","unstructured":"Asiedu, M. N. et al. Development of algorithms for automated detection of cervical pre-cancers with a low-cost, point-of-care, pocket colposcope. IEEE Trans. Bio-Med. Eng. 66(8), 2306\u20132318 (2019).","journal-title":"IEEE Trans. Bio-Med. Eng."},{"issue":"9","key":"68252_CR23","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1093\/jnci\/djy225","volume":"111","author":"L Hu","year":"2019","unstructured":"Hu, L. et al. An observational study of deep learning and automated evaluation of cervical images for cancer screening. J. Natl. Cancer Inst. 111(9), 923\u2013932 (2019).","journal-title":"J. Natl. Cancer Inst."},{"issue":"4","key":"68252_CR24","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1007\/s13246-018-0678-z","volume":"41","author":"B Bai","year":"2018","unstructured":"Bai, B. et al. Automatic segmentation of cervical region in colposcopic images using k-means. Australas. Phys. Eng. Sci. Med. 41(4), 1077\u20131085 (2018).","journal-title":"Australas. Phys. Eng. Sci. Med."},{"key":"68252_CR25","doi-asserted-by":"crossref","unstructured":"T. Xu, H. Zhang, X. Huang, S. Zhang, D.N. Metaxas. Multimodal deep learning for cervical dysplasia diagnosis. MICCAI, 2016.","DOI":"10.1007\/978-3-319-46723-8_14"},{"issue":"5","key":"68252_CR26","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1159\/000341546","volume":"56","author":"G Mehlhorn","year":"2012","unstructured":"Mehlhorn, G. et al. Computer-assisted diagnosis in colposcopy: results of a preliminary experiment?. Acta Cytol. 56(5), 554\u2013559 (2012).","journal-title":"Acta Cytol."},{"issue":"3","key":"68252_CR27","first-page":"3518","volume":"15","author":"M Sato","year":"2018","unstructured":"Sato, M. et al. Application of deep learning to the classification of images from colposcopy. Oncol. Lett. 15(3), 3518\u20133523 (2018).","journal-title":"Oncol. Lett."},{"issue":"6","key":"68252_CR28","first-page":"583","volume":"11","author":"Y Miyagi","year":"2019","unstructured":"Miyagi, Y., Takehara, K. & Miyake, T. Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images. Mol. Clin. Oncol. 11(6), 583\u2013589 (2019).","journal-title":"Mol. Clin. Oncol."},{"key":"68252_CR29","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.canep.2017.06.008","volume":"49","author":"P Adsul","year":"2017","unstructured":"Adsul, P. et al. Implementing community-based cervical cancer screening programs using visual inspection with acetic acid in India: a systematic review. Cancer Epidemiol. 49, 161\u2013174 (2017).","journal-title":"Cancer Epidemiol."},{"issue":"3","key":"68252_CR30","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1016\/j.ygyno.2006.06.002","volume":"103","author":"ME Baum","year":"2006","unstructured":"Baum, M. E. et al. Colposcopic accuracy of obstetrics and gynecology residents. Gynecol. Oncol. 103(3), 966\u2013970 (2006).","journal-title":"Gynecol. Oncol."},{"key":"68252_CR31","first-page":"626","volume":"91","author":"MF Mitchell","year":"1998","unstructured":"Mitchell, M. F., Schottenfeld, D., Tortolero-Luna, G., Cantor, S. B. & Richards-Kortum, R. Colposcopy for the diagnosis of squamous intraepithelial lesions: a meta-analysis. Obstet. Gynecol. 91, 626\u2013631 (1998).","journal-title":"Obstet. Gynecol."},{"issue":"3","key":"68252_CR32","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1097\/AOG.0000000000001136","volume":"127","author":"GF Sawaya","year":"2016","unstructured":"Sawaya, G. F. & Smith-McCune, K. Cervical cancer screening. Obstet. Gynecol. 127(3), 459\u2013467 (2016).","journal-title":"Obstet. Gynecol."},{"issue":"10","key":"68252_CR33","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1016\/S1470-2045(08)70210-9","volume":"9","author":"Y-l Qiao","year":"2008","unstructured":"Qiao, Y.-l et al. A new hpv-DNA test for cervical-cancer screening in developing regions: A cross-sectional study of clinical accuracy in rural china. Lancet Oncol. 9(10), 929\u2013936 (2008).","journal-title":"Lancet Oncol."},{"key":"68252_CR34","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1586\/14737159.2014.865521","volume":"1","author":"SD Isidean","year":"2014","unstructured":"Isidean, S. D., Coutlee, F. & Franco, E. L. Cobas 4800 hpv test, a real-time polymerase chain reaction assay for the detection of human papillomavirus in cervical specimens. Expert Rev. Mol. Diagn. 1, 5\u201316 (2014).","journal-title":"Expert Rev. Mol. Diagn."},{"issue":"12","key":"68252_CR35","doi-asserted-by":"crossref","first-page":"4391","DOI":"10.1128\/JCM.02716-14","volume":"52","author":"A Boers","year":"2014","unstructured":"Boers, A. et al. Clinical validation of the cervista hpv hr test according to the international guidelines for human papillomavirus test requirements for cervical cancer screening. J. Clin. Microbiol. 52(12), 4391\u20134393 (2014).","journal-title":"J. Clin. Microbiol."},{"issue":"1","key":"68252_CR36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1128\/JCM.01177-18","volume":"57","author":"T Iftner","year":"2019","unstructured":"Iftner, T. et al. Longitudinal clinical performance of the rna-based aptima human papillomavirus (ahpv) assay in comparison to the DNA-based hybrid capture 2 hpv test in two consecutive screening rounds with a 6-year interval in germany. J. Clin. Microbio. 57(1), 1\u201312 (2019).","journal-title":"J. Clin. Microbio."},{"issue":"5","key":"68252_CR37","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1002\/cncy.21521","volume":"123","author":"R Nayar","year":"2015","unstructured":"Nayar, R. & Wilbur, D. C. The pap test and Bethesda 2014. Cancer Cytopathol. 123(5), 271\u2013281 (2015).","journal-title":"Cancer Cytopathol."},{"issue":"2","key":"68252_CR38","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.ogc.2013.02.002","volume":"40","author":"S Tatti","year":"2013","unstructured":"Tatti, S., Bornstein, J. & Prendiville, W. Colposcopy: a global perspective: Introduction of the new ifcpc colposcopy terminology. Obstet. Gynecol. Clin. North Am. 40(2), 235\u2013250 (2013).","journal-title":"Obstet. Gynecol. Clin. North Am."},{"issue":"10","key":"68252_CR39","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1055\/s-0035-1558052","volume":"75","author":"O Reich","year":"2015","unstructured":"Reich, O. et al. Precancerous lesions of the cervix, vulva and vagina according to the 2014 who classification of tumors of the female genital tract. Geburtshilfe Frauenheilkd. 75(10), 1018\u20131020 (2015).","journal-title":"Geburtshilfe Frauenheilkd."},{"key":"68252_CR40","unstructured":"Wada K. Labelme: Image polygonal annotation with python. https:\/\/github.com\/wkentaro\/labelme, 2016."},{"issue":"9","key":"68252_CR41","doi-asserted-by":"crossref","first-page":"212","DOI":"10.3390\/brainsci9090212","volume":"9","author":"LV Fulton","year":"2019","unstructured":"Fulton, L. V. et al. Classification of alzheimer\u2019s disease with and without imagery using gradient boosted machines and resnet-50. Brain Sci. 9(9), 212 (2019).","journal-title":"Brain Sci."},{"key":"68252_CR42","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.cmpb.2019.07.005","volume":"178","author":"P Tang","year":"2019","unstructured":"Tang, P. et al. Efficient skin lesion segmentation using separable-unet with stochastic weight averaging. Comput. Methods Prog. Biomed. 178, 289\u2013301 (2019).","journal-title":"Comput. Methods Prog. Biomed."},{"key":"68252_CR43","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Dollar P, et al. Mask r-cnn. IEEE transactions on pattern analysis and machine intelligence, 2018.","DOI":"10.1109\/ICCV.2017.322"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-68252-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-68252-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-68252-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T13:52:57Z","timestamp":1670334777000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-68252-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,15]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["68252"],"URL":"https:\/\/doi.org\/10.1038\/s41598-020-68252-3","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,15]]},"assertion":[{"value":"24 March 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"11639"}}