{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T00:38:28Z","timestamp":1776472708161,"version":"3.51.2"},"reference-count":26,"publisher":"National Library of Serbia","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:p>Although age determination by radiographs of the hand and wrist before the age of 18 is an area where there is a lot of radiological knowledge and many studies are carried out, studies on age determination for adults are limited. Studies on adult age determination through sternum multidetector computed tomography (MDCT) images using artificial intelligence algorithms are much fewer. The reason for the very few studies on adult age determination is that most of the changes observed in the human skeleton with age are outside the limits of what can be perceived by the human eye. In this context, with the dual-channel Convolutional Neural Network (CNN) we developed, we were able to predict the age groups defined as 20-35, 35-50, 51-65, and over 65 with 73% accuracy over sternum MDCT images. Our study shows that fusion modeling with dual-channel convolutional neural networks and using more than one image from the same patient is more successful. Fusion models will make adult age determination, which is often a problem in forensic medicine, more accurate.<\/jats:p>","DOI":"10.2298\/csis220825054t","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T16:49:14Z","timestamp":1671209354000},"page":"215-228","source":"Crossref","is-referenced-by-count":3,"title":["Sternum age estimation with dual channel fusion CNN model"],"prefix":"10.2298","volume":"20","author":[{"given":"Fuat","family":"T\u00fcrk","sequence":"first","affiliation":[{"name":"Computer Engineering, Cankiri Karatekin University, Cankiri, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mustafa","family":"Kaya","sequence":"additional","affiliation":[{"name":"Gazi University School of Medicine, Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"suffix":"Mert","given":"Burak","family":"Akhan","sequence":"additional","affiliation":[{"name":"Gazi University School of Medicine, Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S\u00fcmeyra","family":"\u00c7ayir\u00f6z","sequence":"additional","affiliation":[{"name":"Gazi University School of Medicine, Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erhan","family":"Ilgit","sequence":"additional","affiliation":[{"name":"Gazi University School of Medicine, Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Xing, J., Li, K., Hu, W., et al. Diagnosing deep learning models for high accuracy age estimation from a single image. Pattern Recognition, Vol 66, 106-116. (2017) https:\/\/doi.org\/10.1016\/J.PATCOG.2017.01.005","DOI":"10.1016\/j.patcog.2017.01.005"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Mirwald, RL., Dominic, A., Baxter-Jones G., Bailey DA. An assessment of maturity from anthropometric measurements Pediatric Bone Mineral Accrual Study View project Growth and Maturation in Sport and Exercise View project (2002) https:\/\/doi.org\/10.1097\/00005768-200204000-00020","DOI":"10.1249\/00005768-200204000-00020"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Setel, P.W., Macfarlane, S.B., Szreter, S, et al. Who Counts? 1 A scandal of invisibility: making everyone count by counting everyone. Vol 370, 1569-77, (2007). https:\/\/doi.org\/10.1016\/S0140","DOI":"10.1016\/S0140-6736(07)61307-5"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"Satoh, M. Bone age: assessment methods and clinical applications. Clinical Pediatric Endocrinology, Vol 24, 143-152, (2015). https:\/\/doi.org\/10.1297\/CPE.24.143","DOI":"10.1297\/cpe.24.143"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"Nguyen, Q.H, Nguyen, B.P, Nguyen, M.T., et al. Bone age assessment and sex determination using transfer learning. Expert Systems with Applications, Vol 200, 116926, (2022). https:\/\/doi.org\/10.1016\/J.ESWA.2022.116926","DOI":"10.1016\/j.eswa.2022.116926"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Saraf, A., Kanchan, T., Krishan, K., et al. Estimation of stature from sternum-Exploring the quadratic models, (2018). https:\/\/doi.org\/10.1016\/j.jflm.2018.04.004","DOI":"10.1016\/j.jflm.2018.04.004"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Monum, T., Makino, Y, Prasitwattanaseree ,S, et al. Age estimation from ossification of the sternum and true ribs using 3D post-mortem CT images in a Japanese population, (2019). https:\/\/doi.org\/10.1016\/j.legalmed.2019.101663","DOI":"10.1016\/j.legalmed.2019.101663"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"Singh,J., Pathak, RK. Forensic Anthropology Population Data Sex and age-related non-metric variation of the human sternum in a Northwest Indian postmortem sample: A pilot study, (2013). https:\/\/doi.org\/10.1016\/j.forsciint.2013.02.002","DOI":"10.1016\/j.forsciint.2013.02.002"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"Bacci, N, Nchabeleng, EK., Billings, BK Forensic Anthropology Population Data Forensic age-at-death estimation from the sternum in a black South African population, (2017). https:\/\/doi.org\/10.1016\/j.forsciint.2017.11.002","DOI":"10.1016\/j.forsciint.2017.11.002"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Ismail, M., Ali, M., Mosallam, W., et al Sternum as an indicator for sex and age estimation using multidetector computed tomography in an Egyptian population. Forensic Imaging Vol, 26, 200457, (2021). https:\/\/doi.org\/10.1016\/j.fri.2021.200457","DOI":"10.1016\/j.fri.2021.200457"},{"key":"ref11","unstructured":"Radiological Age Estimation From Sternum.: EBSCOhost. Available: https:\/\/web.p.ebscohost.com\/ehost\/pdfviewer\/pdfviewer?vid=0&sid=0b6e5d68-86f6-44f4-aaa6-9b3a8c6e9271%40redis, (2022)."},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Zhang, K, Fan, F, Tu, M, et al. The role of multislice computed tomography of the costal cartilage in adult age estimation. International Journal of Legal Medicine, Vol 132, 791-798, (2018). https:\/\/doi.org\/10.1007\/S00414-017-1646-Y\/TABLES\/3","DOI":"10.1007\/s00414-017-1646-y"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Abdullahi, A., Bawazeer, K., Alotaibai, S, et al. Pretrained Convolutional Neural Networks for Cancer Genome Classification; Pretrained Convolutional Neural Networks for Cancer Genome Classification, (2020).","DOI":"10.1109\/ICCAIS48893.2020.9096808"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zhu, S., Ma, K, et al. Identification of 12 cancer types through genome deep learning. Scientific Reports 2019, Vol 9, No. 1, 1-9, (2019). https:\/\/doi.org\/10.1038\/s41598-019-53989-3","DOI":"10.1038\/s41598-019-53989-3"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Li, S., Xu, P., Li, B, et al Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features. Physics in Medicine & Biology Vol. 64, 175012, (2019).. https:\/\/doi.org\/10.1088\/1361-6560\/AB326A","DOI":"10.1088\/1361-6560\/ab326a"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Ardila, D., Kiraly, AP., Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography, (2019). https:\/\/doi.org\/10.1038\/s41591-019-0447-x","DOI":"10.1038\/s41591-019-0447-x"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Mutasa, S., Sun, S, Ha, R. Understanding artificial intelligence-based radiology studies: CNN architecture. Clinical Imaging, Vol, 80, 72-76, (2021). https:\/\/doi.org\/10.1016\/J.CLINIMAG.2021.06.033","DOI":"10.1016\/j.clinimag.2021.06.033"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"T\u00fcrk, F., L\u00fcy, M., Bar\u0131\u015f\u00e7\u0131, N. Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model. Mathematics 2020, Vol 8, 1772 (2020). https:\/\/doi.org\/10.3390\/MATH8101772","DOI":"10.3390\/math8101772"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Morel, O., Blanchon, M., et al. Exploration of Deep Learning-based Multimodal Fusion for Semantic Road Scene Segmentation. VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Vol 5, 336-343, (2019). https:\/\/doi.org\/10.5220\/0007360403360343","DOI":"10.5220\/0007360400002108"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Mezina, I., Qingjie, L., Yunhong, W. Very High-Resolution Images Classification by Fusing Deep Convolutional Neural Networks, The 5th International Conference on Advanced Computer Science Applications and Technologies ,(2017). https:\/\/doi.org\/10.23977\/ACSAT.2017.1022","DOI":"10.23977\/acsat.2017.1022"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Tenenbaum, J.B, Freeman, W.T. Separating style and content with bilinear models. Neural Computation, Vol, 12, 1247-1283, (2000). https:\/\/doi.org\/10.1162\/089976600300015349","DOI":"10.1162\/089976600300015349"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Maniatopoulos, A., Mitianoudis, N. Learnable Leaky ReLU (LeLeLU): An Alternative Accuracy-Optimized Activation Function, (2021). https:\/\/doi.org\/10.3390\/info12120513","DOI":"10.3390\/info12120513"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Eckle, K., Schmidt-Hieber, J. A comparison of deep networks with ReLU activation function and linear spline-type methods. Neural Networks, Vol 110, 232-242, (2019). https:\/\/doi.org\/10.1016\/J.NEUNET.2018.11.005","DOI":"10.1016\/j.neunet.2018.11.005"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"Khaire, U.M., Dhanalakshmi, R. High-dimensional microarray dataset classification using an improved adam optimizer (Adam). Journal of Ambient Intelligence and Humanized Computing, Vol 11, 5187-5204, (2020). https:\/\/doi.org\/10.1007\/s12652-020-01832-3","DOI":"10.1007\/s12652-020-01832-3"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Sharma, J., Soni, S., Paliwal, P., et al. A novel long-term solar photovoltaic power forecasting approach using LSTM with Nadam optimizer: A case study of India, (2022). https:\/\/doi.org\/10.1002\/ese3.1178","DOI":"10.1002\/ese3.1178"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"Nisha, J.P., Gopi, V., Palanisamy, P. Automated colorectal polyp detection based on image enhancement and dual-path CNN architecture. Biomedical Signal Processing and Control Vol, 73, 103465, (2022). https:\/\/doi.org\/10.1016\/J.BSPC.2021.103465","DOI":"10.1016\/j.bspc.2021.103465"}],"container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T06:31:11Z","timestamp":1721889071000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02142200054T"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":26,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.2298\/csis220825054t","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}