{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T22:53:53Z","timestamp":1761778433729,"version":"build-2065373602"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 111-2118-M-A49-003-MY2"],"award-info":[{"award-number":["MOST 111-2118-M-A49-003-MY2"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 113-2118-M-A49-006"],"award-info":[{"award-number":["NSTC 113-2118-M-A49-006"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100024990","name":"National Yang Ming Chiao Tung University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100024990","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Parkinson\u2019s disease (PD), a degenerative disorder of the central nervous system, is commonly diagnosed using functional medical imaging techniques such as single-photon emission computed tomography (SPECT). In this study, we utilized two SPECT data sets (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009634 and\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009202) from different hospitals to develop a model capable of accurately predicting PD stages, a multiclass classification task. We used the entire three-dimensional (3D) brain images as input and experimented with various model architectures. Initially, we treated the 3D images as sequences of two-dimensional (2D) slices and fed them sequentially into 2D convolutional neural network (CNN) models pretrained on ImageNet, averaging the outputs to obtain the final predicted stage. We also applied 3D CNN models pretrained on Kinetics-400. Additionally, we incorporated an attention mechanism to account for the varying importance of different slices in the prediction process. To further enhance model efficacy and robustness, we simultaneously trained the two data sets using weight sharing, a technique known as cotraining. Our results demonstrated that 2D models pretrained on ImageNet outperformed 3D models pretrained on Kinetics-400, and models utilizing the attention mechanism outperformed both 2D and 3D models. The cotraining technique proved effective in improving model performance when the cotraining data sets were sufficiently large.\n                  <\/jats:p>","DOI":"10.1007\/s10278-025-01402-z","type":"journal-article","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T16:51:03Z","timestamp":1737651063000},"page":"2934-2950","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Convolutional Neural Networks on Multiclass Classification of Three-Dimensional Brain Images for Parkinson\u2019s Disease Stage Prediction"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1802-3855","authenticated-orcid":false,"given":"Guan-Hua","family":"Huang","sequence":"first","affiliation":[]},{"given":"Wan-Chen","family":"Lai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3348-4422","authenticated-orcid":false,"given":"Tai-Been","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Chien-Chin","family":"Hsu","sequence":"additional","affiliation":[]},{"given":"Huei-Yung","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yi-Chen","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Li-Ren","family":"Yeh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,23]]},"reference":[{"issue":"8","key":"1402_CR1","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1007\/s00702-017-1686-y","volume":"124","author":"OB Tysnes","year":"2017","unstructured":"Tysnes OB, Storstein A: Epidemiology of Parkinson\u2019s disease. J Neural Transm 124(8):901-905, 2017","journal-title":"J Neural Transm"},{"issue":"7","key":"1402_CR2","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1016\/j.jfma.2015.05.014","volume":"115","author":"WM Liu","year":"2016","unstructured":"Liu WM, Wu RM, Lin JW, Liu YC, Chang CH, Lin CH: Time trends in the prevalence and incidence of Parkinson\u2019s disease in Taiwan: A nationwide, population-based study. J Formos Med Assoc 115(7):531-538, 2016","journal-title":"J Formos Med Assoc"},{"issue":"5","key":"1402_CR3","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1212\/WNL.17.5.427","volume":"17","author":"MM Hoehn","year":"1967","unstructured":"Hoehn MM, Yahr MD: Parkinsonism: onset, progression and mortality. Neurology 17(5):427-442, 1967","journal-title":"Neurology"},{"unstructured":"Parkinson\u2019s Foundation. Available at https:\/\/www.parkinson.org\/Understanding-Parkinsons\/What-is-Parkinsons\/Stages-of-Parkinsons. Accessed 3 May 2023.","key":"1402_CR4"},{"issue":"1","key":"1402_CR5","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1097\/NRL.0b013e318183fdd8","volume":"15","author":"AC Felicio","year":"2009","unstructured":"Felicio AC, Shih MC, Godeiro-Junior C, Andrade LAF, Bressan RA, Ferraz HB: Molecular imaging studies in Parkinson disease: reducing diagnostic uncertainty. Neurologist 15(1):6-16, 2009","journal-title":"Neurologist"},{"issue":"S2","key":"1402_CR6","doi-asserted-by":"publisher","first-page":"S721","DOI":"10.1002\/mds.22590","volume":"24","author":"C Scherfler","year":"2009","unstructured":"Scherfler C, Nocker M: Dopamine transporter SPECT: how to remove subjectivity? Mov Disord 24(S2):S721-S724, 2009","journal-title":"Mov Disord"},{"issue":"3","key":"1402_CR7","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1097\/MNM.0b013e328314b863","volume":"30","author":"RT Staff","year":"2009","unstructured":"Staff RT, Ahearn TS, Wilson K, Counsell CE, Taylor K, Caslake R, Davidson JE, Gemmell HG, Murray AD: Shape analysis of 123I-N-omega-fluoropropyl-2-beta-carbomethoxy-3beta-(4-iodophenyl) nortropane single-photon emission computed tomography images in the assessment of patients with parkinsonian syndromes. Nucl Med Commun 30(3):194-201, 2009","journal-title":"Nucl Med Commun"},{"issue":"3","key":"1402_CR8","doi-asserted-by":"publisher","first-page":"794","DOI":"10.1109\/JBHI.2016.2547901","volume":"21","author":"R Prashanth","year":"2016","unstructured":"Prashanth R, Roy SD, Mandal PK, Ghosh S: High-accuracy classification of Parkinson\u2019s disease through shape analysis and surface fitting in 123I-Ioflupane SPECT imaging. IEEE J Biomed Health Inform 21(3):794-802, 2016","journal-title":"IEEE J Biomed Health Inform"},{"issue":"8","key":"1402_CR9","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1097\/MNM.0b013e328347cd09","volume":"32","author":"DJ Towey","year":"2011","unstructured":"Towey DJ, Bain PG, Nijran KS: Automatic classification of 123I-FP-CIT (DaTSCAN) SPECT images. Nucl Med Commun 32(8):699-707, 2011","journal-title":"Nucl Med Commun"},{"issue":"10","key":"1402_CR10","doi-asserted-by":"publisher","first-page":"5971","DOI":"10.1118\/1.4742055","volume":"39","author":"I Ill\u00e1n","year":"2012","unstructured":"Ill\u00e1n I, G\u00f3rriz J, Ram\u00edrez J, Segovia F, Jim\u00e9nez-Hoyuela J, Ortega Lozano S: Automatic assistance to Parkinson\u2019s disease diagnosis in DaTSCAN SPECT imaging. Med Phys 39(10):5971-5980, 2012","journal-title":"Med Phys"},{"issue":"7","key":"1402_CR11","doi-asserted-by":"publisher","first-page":"4395","DOI":"10.1118\/1.4730289","volume":"39","author":"F Segovia","year":"2012","unstructured":"Segovia F, G\u00f3rriz JM, Ram\u00edrez J, Alvarez I, Jim\u00e9nez-Hoyuela JM, Ortega SJ: Improved parkinsonism diagnosis using a partial least squares based approach. Med Phys 39(7):4395-4403, 2012","journal-title":"Med Phys"},{"issue":"7","key":"1402_CR12","doi-asserted-by":"publisher","first-page":"2756","DOI":"10.1016\/j.eswa.2012.11.017","volume":"40","author":"A Rojas","year":"2013","unstructured":"Rojas A, G\u00f3rriz J, Ram\u00edrez J, Ill\u00e1n I, Mart\u00ednez-Murcia FJ, Ortiz A, R\u00edo MG, Moreno-Caballero M: Application of Empirical Mode Decomposition (EMD) on DaTSCAN SPECT images to explore Parkinson disease. Expert Syst Appl 40(7):2756-2766, 2013","journal-title":"Expert Syst Appl"},{"issue":"7","key":"1402_CR13","doi-asserted-by":"publisher","first-page":"3333","DOI":"10.1016\/j.eswa.2013.11.031","volume":"41","author":"R Prashanth","year":"2014","unstructured":"Prashanth R, Roy SD, Mandal PK, Ghosh S: Automatic classification and prediction models for early Parkinson\u2019s disease diagnosis from SPECT imaging. Expert Syst Appl 41(7):3333-3342, 2014","journal-title":"Expert Syst Appl"},{"doi-asserted-by":"crossref","unstructured":"Bhalchandra NA, Prashanth R, Roy SD, Noronha S: Early detection of Parkinson\u2019s disease through shape based features from 123I-Ioflupane SPECT imaging. In: IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 963\u2013966, 2015","key":"1402_CR14","DOI":"10.1109\/ISBI.2015.7164031"},{"issue":"7","key":"1402_CR15","first-page":"S176","volume":"18","author":"FL Pagan","year":"2012","unstructured":"Pagan FL: Improving outcomes through early diagnosis of Parkinson\u2019s disease. Am J Manag Care 18(7):S176, 2012","journal-title":"Am J Manag Care"},{"doi-asserted-by":"crossref","unstructured":"Caesarendra W, Ariyanto M, Setiawan JD, Arozi M, Chang CR: A pattern recognition method for stage classification of Parkinson\u2019s disease utilizing voice features. In: IEEE Conference on Biomedical Engineering and Sciences (IECBES), pp. 87\u201392, 2014","key":"1402_CR16","DOI":"10.1109\/IECBES.2014.7047636"},{"key":"1402_CR17","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1016\/j.nicl.2017.09.010","volume":"16","author":"H Choi","year":"2017","unstructured":"Choi H, Ha S, Im HJ, Paek SH, Lee DS: Refining diagnosis of Parkinson\u2019s disease with deep learning based interpretation of dopamine transporter imaging. Neuroimage Clin 16:586-594, 2017","journal-title":"Neuroimage Clin"},{"key":"1402_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104041","volume":"126","author":"PR Magesh","year":"2020","unstructured":"Magesh PR, Myloth RD, Tom RJ: An explainable machine learning model for early detection of Parkinson\u2019s disease using LIME on DaTSCAN imagery. Comput Biol Med 126:104041, 2020","journal-title":"Comput Biol Med"},{"issue":"1","key":"1402_CR19","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3390\/biomedicines9010012","volume":"9","author":"C-Y Chien","year":"2020","unstructured":"Chien C-Y, Hsu S-W, Lee T-L, Sung P-S, Lin C-C: Using artificial neural network to discriminate Parkinson\u2019s disease from other Parkinsonisms by focusing on putamen of dopamine transporter SPECT images. Biomedicines 9(1):12, 2020","journal-title":"Biomedicines"},{"issue":"5","key":"1402_CR20","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1002\/sam.11480","volume":"13","author":"GH Huang","year":"2020","unstructured":"Huang GH, Lin CH, Cai YR, Chen TB, Hsu SY, Lu NH, Chen HY, Wu YC: Multiclass machine learning classification of functional brain images for Parkinson\u2019s disease stage prediction. Stat Anal Data Min 13(5):508-523, 2020","journal-title":"Stat Anal Data Min"},{"doi-asserted-by":"crossref","unstructured":"Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Fei-Fei L: Large-scale video classification with convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725\u20131732, 2014","key":"1402_CR21","DOI":"10.1109\/CVPR.2014.223"},{"issue":"5","key":"1402_CR22","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1109\/TMI.2016.2536809","volume":"35","author":"AAA Setio","year":"2016","unstructured":"Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, van Riel SJ, Wille MMW, Naqibullah M, Sanchez CI, van Ginneken B: Pulmonary nodule detection in ct images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35(5):1160-1169, 2016","journal-title":"IEEE Trans Med Imaging"},{"doi-asserted-by":"crossref","unstructured":"Tran D, Wang H, Torresani L, Ray J, LeCun Y, Paluri M: A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 6450\u20136459, 2018","key":"1402_CR23","DOI":"10.1109\/CVPR.2018.00675"},{"key":"1402_CR24","doi-asserted-by":"publisher","first-page":"5097","DOI":"10.3390\/s20185097","volume":"20","author":"SP Singh","year":"2020","unstructured":"Singh SP, Wang L, Gupta S, Goli H, Padmanabhan P, Guly\u00e1s B: 3D deep learning on medical images: a review. Sensors 20:5097, 2020","journal-title":"Sensors"},{"unstructured":"Yang J, Huang X, He Y, Xu J, Yang C, Xu G, Ni B: Reinventing 2D convolutions for 3D images. arXiv preprint arXiv:1911.10477, 2019","key":"1402_CR25"},{"key":"1402_CR26","doi-asserted-by":"publisher","first-page":"5879","DOI":"10.3390\/s20205879","volume":"20","author":"SF Huang","year":"2020","unstructured":"Huang SF, Wen YH, Chu CH, Hsu CC: A shape approximation for medical imaging data. Sensors 20:5879, 2020","journal-title":"Sensors"},{"issue":"4","key":"1402_CR27","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1136\/jnnp.2003.020982","volume":"75","author":"GF Wooten","year":"2004","unstructured":"Wooten GF, Currie LJ, Bovbjerg VE, Lee JK, Patrie J: Are men at greater risk for Parkinson\u2019s disease than women? J Neurol Neurosurg Psychiatry 75(4):637-639, 2004","journal-title":"J Neurol Neurosurg Psychiatry"},{"issue":"13","key":"1402_CR28","doi-asserted-by":"publisher","first-page":"1583","DOI":"10.1002\/mds.25945","volume":"29","author":"T Pringsheim","year":"2014","unstructured":"Pringsheim T, Jette N, Frolkis A, Steeves TD: The prevalence of Parkinson\u2019s disease: a systematic review and meta-analysis. Mov Disord 29(13):1583-1590, 2014","journal-title":"Mov Disord"},{"issue":"5","key":"1402_CR29","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/S0895-6111(03)00032-6","volume":"27","author":"DA Rajon","year":"2003","unstructured":"Rajon DA, Bolch WE: Marching cube algorithm: review and trilinear interpolation adaptation for image-based dosimetric models. Comput Med Imaging Graph 27(5):411-435, 2003","journal-title":"Comput Med Imaging Graph"},{"key":"1402_CR30","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE: ImageNet classification with deep convolutional neural networks. Commun ACM 60:84-90, 2017","journal-title":"Commun ACM"},{"unstructured":"Kay W, Carreira J, Simonyan K, Zhang B, Hillier C, Vijayanarasimhan S, Viola F, Green T, Back T, Natsev P, Suleyman M, Zisserman M: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950, 2017","key":"1402_CR31"},{"key":"1402_CR32","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J: Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Trans Med Imaging 35:1299-1312, 2016","journal-title":"IEEE Trans Med Imaging"},{"key":"1402_CR33","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.3390\/diagnostics12061457","volume":"12","author":"GH Huang","year":"2020","unstructured":"Huang GH, Fu QJ, Gu MZ, Lu NH, Liu KY, Chen TB: Deep transfer learning for the multilabel classification of chest X-ray images. Diagnostics 12:1457, 2020","journal-title":"Diagnostics"},{"doi-asserted-by":"crossref","unstructured":"Liu S, Xu D, Zhou SK, Pauly O, Grbic S, Mertelmeier T, Wicklein J, Jerebko A, Cai W, Comaniciu D: 3d anisotropic hybrid network: Transferring convolutional features from 2d images to 3d anisotropic volumes. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 851\u2013858, 2018","key":"1402_CR34","DOI":"10.1007\/978-3-030-00934-2_94"},{"unstructured":"Guo C, Berkhahn F: Entity embeddings of categorical variables. arXiv preprint arXiv:1604.06737, 2016","key":"1402_CR35"},{"unstructured":"Bahdanau D, Cho K, Bengio Y: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014","key":"1402_CR36"},{"doi-asserted-by":"crossref","unstructured":"Luong M-T, Pham H, Manning CD: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025, 2015","key":"1402_CR37","DOI":"10.18653\/v1\/D15-1166"},{"unstructured":"Lin Z, Feng M, Nogueira dos Santos C, Yu M, Xiang B, Zhou B, Bengio Y: A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130, 2017","key":"1402_CR38"},{"unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I: Attention is all you need. arXiv preprint arXiv:1706.03762, 2017","key":"1402_CR39"},{"unstructured":"Smith LN, Topin N: Super-convergence: very fast training of neural networks using large learning rates. arXiv preprint arXiv:1708.07120, 2017","key":"1402_CR40"},{"unstructured":"Loshchilov I, Hutter F: SGDR: Stochastic gradient descent with warm restarts. In: 5th International Conference on Learning Representations (ICLR), 2017","key":"1402_CR41"},{"key":"1402_CR42","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s40708-021-00144-2","volume":"8","author":"AB Tufail","year":"2021","unstructured":"Tufail AB, Ma YK, Zhang QN, Khan A, Zhao L, Yang Q, Adeel M, Khan R, Ullah I: 3D convolutional neural networks\u2011based multiclass classification of Alzheimer\u2019s and Parkinson\u2019s diseases using PET and SPECT neuroimaging modalities. Brain Inf 8:23, 2021","journal-title":"Brain Inf"},{"key":"1402_CR43","doi-asserted-by":"publisher","first-page":"5145","DOI":"10.1007\/s00521-021-06163-8","volume":"5","author":"G Huang","year":"2023","unstructured":"Huang G, Jafari AH: Enhanced balancing GAN: minority-class image generation. Neural Comput Appl 5:5145-5154, 2023","journal-title":"Neural Comput Appl"},{"doi-asserted-by":"crossref","unstructured":"Hasan Y, Amerehi F, Healy P, Ryan C: STEM rebalance: A novel approach for tackling imbalanced datasets using SMOTE, edited nearest neighbour, and mixup. arXiv preprint arXiv:2311.07504, 2023","key":"1402_CR44","DOI":"10.1109\/ICCP60212.2023.10398660"},{"issue":"4","key":"1402_CR45","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/j.pneurobio.2011.09.005","volume":"95","author":"Parkinson Progression Marker Initiative","year":"2011","unstructured":"Parkinson Progression Marker Initiative: The Parkinson Progression Marker Initiative (PPMI). Prog Neurobiol 95(4):629-635, 2011","journal-title":"Prog Neurobiol"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01402-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-025-01402-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01402-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T22:48:57Z","timestamp":1761778137000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-025-01402-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,23]]},"references-count":45,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["1402"],"URL":"https:\/\/doi.org\/10.1007\/s10278-025-01402-z","relation":{},"ISSN":["2948-2933"],"issn-type":[{"type":"electronic","value":"2948-2933"}],"subject":[],"published":{"date-parts":[[2025,1,23]]},"assertion":[{"value":"30 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was performed in line with the principles of the Declaration of Helsinki. The Chang Gung data set was approved by the Institutional Review Board of the Chang Gung Medical Foundation, Taipei, Taiwan (IRB No.: 201800973B0). The E-Da data set was approved by the Institutional Review Board of the E-Da Hospital, Kaohsiung, Taiwan (Protocol No.: EMRP-100\u2013054 (R III)).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"For the Chang Gung data set, written informed consent was waived due to its retrospective nature. For the E-Da data set, informed consent was obtained from all subjects involved in the data sets of this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}},{"value":"Author Tai-Been Chen was employed by the companies Infinity Co. Ltd. and Der Lih Fuh Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}