{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T22:20:07Z","timestamp":1771539607514,"version":"3.50.1"},"reference-count":47,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T00:00:00Z","timestamp":1710460800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["21K12111"],"award-info":[{"award-number":["21K12111"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["21KK0183"],"award-info":[{"award-number":["21KK0183"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>The purpose of this study is to demonstrate the use of a deep learning model in quantitatively evaluating clinical findings typically subject to uncertain evaluations by physicians, using binary test results based on routine protocols. A chest X-ray is the most commonly used diagnostic tool for the detection of a wide range of diseases and is generally performed as a part of regular medical checkups. However, when it comes to findings that can be classified as within the normal range but are not considered disease-related, the thresholds of physicians\u2019 findings can vary to some extent, therefore it is necessary to define a new evaluation method and quantify it. The implementation of such methods is difficult and expensive in terms of time and labor. In this study, a total of 83,005 chest X-ray images were used to diagnose the common findings of pleural thickening and scoliosis. A novel method for quantitatively evaluating the probability that a physician would judge the images to have these findings was established. The proposed method successfully quantified the variation in physicians\u2019 findings using a deep learning model trained only on binary annotation data. It was also demonstrated that the developed method could be applied to both transfer learning using convolutional neural networks for general image analysis and a newly learned deep learning model based on vector quantization variational autoencoders with high correlations ranging from 0.89 to 0.97.<\/jats:p>","DOI":"10.1371\/journal.pdig.0000460","type":"journal-article","created":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T17:53:26Z","timestamp":1710525206000},"page":"e0000460","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Quantitative evaluation model of variable diagnosis for chest X-ray images using deep learning"],"prefix":"10.1371","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6408-1387","authenticated-orcid":true,"given":"Shota","family":"Nakagawa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7722-055X","authenticated-orcid":true,"given":"Naoaki","family":"Ono","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yukichika","family":"Hakamata","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takashi","family":"Ishii","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akira","family":"Saito","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shintaro","family":"Yanagimoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shigehiko","family":"Kanaya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2024,3,15]]},"reference":[{"issue":"1","key":"pdig.0000460.ref001","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/s12194-019-00552-4","article-title":"AI-based computer-aided diagnosis (AI-CAD): the latest review to read first","volume":"13","author":"H Fujita","year":"2020","journal-title":"Radiol Phys Technol"},{"key":"pdig.0000460.ref002","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.patrec.2019.11.013","article-title":"Deep-learning framework to detect lung abnormality\u2013A study with chest X-Ray and lung CT scan images","volume":"129","author":"A Bhandary","year":"2020","journal-title":"Pattern Recognit Lett"},{"key":"pdig.0000460.ref003","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.1016\/j.procs.2019.09.314","article-title":"Lung boundary detection for chest X-ray images classification based on GLCM and probabilistic neural networks","volume":"159","author":"A Zotin","year":"2019","journal-title":"Procedia Comput Sci"},{"key":"pdig.0000460.ref004","first-page":"1242","article-title":"Dense-Unet: a light model for lung fields segmentation in Chest X-Ray images","volume":"2020","author":"M Yahyatabar","year":"2020","journal-title":"Conf Proc IEEE Eng Med Biol Soc"},{"key":"pdig.0000460.ref005","doi-asserted-by":"crossref","first-page":"101600","DOI":"10.1016\/j.bspc.2019.101600","article-title":"Low-dose chest X-ray image super-resolution using generative adversarial nets with spectral normalization","volume":"55","author":"L Xu","year":"2020","journal-title":"Biomed Signal Process Control"},{"issue":"2","key":"pdig.0000460.ref006","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/S0951-8320(96)00077-4","article-title":"Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management","volume":"54","author":"SC Hora","year":"1996","journal-title":"Reliab Eng Syst Saf"},{"issue":"3","key":"pdig.0000460.ref007","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s10994-021-05946-3","article-title":"Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods","volume":"110","author":"E H\u00fcllermeier","year":"2021","journal-title":"Mach Learn"},{"key":"pdig.0000460.ref008","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/TREX53765.2021.00014","volume-title":"2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)","author":"C Gillmann","year":"2021"},{"issue":"1","key":"pdig.0000460.ref009","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1038\/s41746-020-00367-3","article-title":"Second opinion needed: communicating uncertainty in medical machine learning","volume":"4","author":"B Kompa","year":"2021","journal-title":"NPJ Digit Med"},{"key":"pdig.0000460.ref010","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","article-title":"A review of uncertainty quantification in deep learning: Techniques, applications and challenges","volume":"76","author":"M Abdar","year":"2021","journal-title":"Inf Fusion"},{"issue":"8","key":"pdig.0000460.ref011","doi-asserted-by":"crossref","first-page":"e36427","DOI":"10.2196\/36427","article-title":"Uncertainty Estimation in Medical Image Classification: Systematic Review","volume":"10","author":"A Kurz","year":"2022","journal-title":"JMIR Med Inform"},{"issue":"8","key":"pdig.0000460.ref012","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pdig.0000085","article-title":"Uncertainty-aware deep learning in healthcare: A scoping review","volume":"1","author":"TJ Loftus","year":"2022","journal-title":"PLOS Digit Health"},{"key":"pdig.0000460.ref013","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/978-3-030-60365-6_9","volume-title":"Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis","author":"MH Laves","year":"2020"},{"key":"pdig.0000460.ref014","first-page":"20063","author":"R Rahaman","year":"2020","journal-title":"Uncertainty Quantification and Deep Ensembles"},{"key":"pdig.0000460.ref015","unstructured":"Gal Y, Ghahramani Z. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. In: Balcan MF, Weinberger KQ, editors. Proceedings of The 33rd International Conference on Machine Learning. vol. 48 of Proceedings of Machine Learning Research. New York, New York, USA: PMLR; 2016. p. 1050\u20131059."},{"key":"pdig.0000460.ref016","first-page":"5","article-title":"Learn To Be Uncertain: Leveraging Uncertain Labels In Chest X-rays With Bayesian Neural Networks","author":"HY Yang","year":"2019","journal-title":"CVPR Workshops"},{"key":"pdig.0000460.ref017","unstructured":"Nagatsuta. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory; 2010. https:\/\/www.jmlr.org\/papers\/volume11\/watanabe10a\/watanabe10a.pdf."},{"issue":"6","key":"pdig.0000460.ref018","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1007\/s11222-013-9416-2","article-title":"Understanding predictive information criteria for Bayesian models","volume":"24","author":"A Gelman","year":"2014","journal-title":"Stat Comput"},{"issue":"8","key":"pdig.0000460.ref019","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1016\/j.crad.2004.01.011","article-title":"Inter-observer variation in the interpretation of chest radiographs for pneumonia in community-acquired lower respiratory tract infections","volume":"59","author":"RM Hopstaken","year":"2004","journal-title":"Clin Radiol"},{"issue":"10","key":"pdig.0000460.ref020","first-page":"1123","article-title":"Intra-observer and overall agreement in the radiological assessment of tuberculosis","volume":"10","author":"JP Zellweger","year":"2006","journal-title":"Int J Tuberc Lung Dis"},{"issue":"8","key":"pdig.0000460.ref021","doi-asserted-by":"crossref","first-page":"e106051","DOI":"10.1371\/journal.pone.0106051","article-title":"Accuracy of the interpretation of chest radiographs for the diagnosis of paediatric pneumonia","volume":"9","author":"MA Elemraid","year":"2014","journal-title":"PLoS One"},{"key":"pdig.0000460.ref022","doi-asserted-by":"crossref","DOI":"10.1002\/0470033312","volume-title":"Uncertain Judgements: Eliciting Experts\u2019 Probabilities","author":"A O\u2019Hagan","year":"2006"},{"issue":"1","key":"pdig.0000460.ref023","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1186\/s12931-019-1116-9","article-title":"Pleural thickening on screening chest X-rays: a single institutional study","volume":"20","author":"A Saito","year":"2019","journal-title":"Respir Res"},{"issue":"4","key":"pdig.0000460.ref024","doi-asserted-by":"crossref","first-page":"238","DOI":"10.2174\/1573398X113096660025","article-title":"Pleuroparenchymal Fibroelastosis: Its Pathological Characteristics","volume":"9","author":"JH von der Th\u00fcsen","year":"2013","journal-title":"Curr Respir Med Rev"},{"issue":"11","key":"pdig.0000460.ref025","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1513\/AnnalsATS.201902-181CME","article-title":"Pleuroparenchymal Fibroelastosis. A Review of Clinical, Radiological, and Pathological Characteristics","volume":"16","author":"F Chua","year":"2019","journal-title":"Ann Am Thorac Soc"},{"issue":"1","key":"pdig.0000460.ref026","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s11832-012-0457-4","article-title":"Epidemiology of adolescent idiopathic scoliosis","volume":"7","author":"MR Konieczny","year":"2013","journal-title":"J Child Orthop"},{"issue":"2","key":"pdig.0000460.ref027","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1001\/jama.2017.11669","article-title":"Screening for Adolescent Idiopathic Scoliosis: Evidence Report and Systematic Review for the US Preventive Services Task Force","volume":"319","author":"J Dunn","year":"2018","journal-title":"JAMA"},{"key":"pdig.0000460.ref028","author":"H Wang","year":"2018","journal-title":"ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography"},{"key":"pdig.0000460.ref029","doi-asserted-by":"crossref","first-page":"6357171","DOI":"10.1155\/2019\/6357171","article-title":"Cobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network","volume":"2019","author":"MH Horng","year":"2019","journal-title":"Comput Math Methods Med"},{"issue":"5","key":"pdig.0000460.ref030","doi-asserted-by":"crossref","first-page":"e0267851","DOI":"10.1371\/journal.pone.0267851","article-title":"Using deep transfer learning to detect scoliosis and spondylolisthesis from x-ray images","volume":"17","author":"M Fraiwan","year":"2022","journal-title":"PLoS One"},{"key":"pdig.0000460.ref031","author":"P Rajpurkar","year":"2017","journal-title":"CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning"},{"issue":"9","key":"pdig.0000460.ref032","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1038\/nn.3495","article-title":"Probabilistic brains: knowns and unknowns","volume":"16","author":"A Pouget","year":"2013","journal-title":"Nat Neurosci"},{"key":"pdig.0000460.ref033","volume-title":"Surfing Uncertainty: Prediction, Action, and the Embodied Mind","author":"A Clark","year":"2015"},{"key":"pdig.0000460.ref034","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.ins.2018.08.013","article-title":"An evidential Markov decision making model","volume":"467","author":"Z He","year":"2018","journal-title":"Inf Sci"},{"key":"pdig.0000460.ref035","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1146\/annurev-vision-082114-035447","article-title":"Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing","volume":"1","author":"N Kriegeskorte","year":"2015","journal-title":"Annu Rev Vis Sci"},{"issue":"3","key":"pdig.0000460.ref036","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1038\/nn.4240","article-title":"Confidence and certainty: distinct probabilistic quantities for different goals","volume":"19","author":"A Pouget","year":"2016","journal-title":"Nat Neurosci"},{"issue":"1","key":"pdig.0000460.ref037","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1037\/rev0000045","article-title":"Self-evaluation of decision-making: A general Bayesian framework for metacognitive computation","volume":"124","author":"SM Fleming","year":"2017","journal-title":"Psychol Rev"},{"key":"pdig.0000460.ref038","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2016. p. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"pdig.0000460.ref039","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"O Russakovsky","year":"2015","journal-title":"Int J Comput Vis"},{"key":"pdig.0000460.ref040","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Maaten Lvd, Weinberger KQ. Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2017. p. 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"pdig.0000460.ref041","unstructured":"Tan M, Le Q. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In: Chaudhuri K, Salakhutdinov R, editors. Proceedings of the 36th International Conference on Machine Learning. vol. 97 of Proceedings of Machine Learning Research. PMLR; 2019. p. 6105\u20136114."},{"key":"pdig.0000460.ref042","first-page":"11976","author":"Z Liu","year":"2022","journal-title":"A ConvNet for the 2020s"},{"key":"pdig.0000460.ref043","article-title":"Neural discrete representation learning","volume":"30","author":"A Van Den Oord","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"pdig.0000460.ref044","author":"DP Kingma","year":"2017","journal-title":"Adam: A Method for Stochastic Optimization"},{"key":"pdig.0000460.ref045","first-page":"618","author":"RR Selvaraju","year":"2016","journal-title":"Grad-CAM: Visual explanations from deep networks via Gradient-based localization"},{"issue":"1","key":"pdig.0000460.ref046","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"J Cohen","year":"1960","journal-title":"Educational and psychological measurement"},{"issue":"1","key":"pdig.0000460.ref047","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/0004-3702(78)90014-0","article-title":"Categorical and probabilistic reasoning in medical diagnosis","volume":"11","author":"P Szolovits","year":"1978","journal-title":"Artif Intell"}],"container-title":["PLOS Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0000460","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T17:53:55Z","timestamp":1710525235000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0000460"}},"subtitle":[],"editor":[{"given":"Ryan S.","family":"McGinnis","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2024,3,15]]},"references-count":47,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3,15]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pdig.0000460","relation":{},"ISSN":["2767-3170"],"issn-type":[{"value":"2767-3170","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,15]]}}}