{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T18:52:57Z","timestamp":1789671177728,"version":"4.0.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Clin Oral Invest"],"DOI":"10.1007\/s00784-024-05977-9","type":"journal-article","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T09:29:07Z","timestamp":1730107747000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["ADGRU: Adaptive DenseNet with gated recurrent unit for automatic diagnosis of periodontal bone loss and stage periodontitis with tooth segmentation mechanism"],"prefix":"10.1007","volume":"28","author":[{"given":"M. S. Antony","family":"Vigil","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V.","family":"Gowri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. S. Subashka","family":"Ramesh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. S. Bennet","family":"Praba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P.","family":"Sabitha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"issue":"7","key":"5977_CR1","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.1902\/jop.2008.070578","volume":"79","author":"TS Kim","year":"2008","unstructured":"Kim TS, Obst C, Zehaczek S, Geenen C (2008) Detection of bone loss with different X-ray techniques in periodontal patients. J Periodontol 79(7):1141\u20131149. https:\/\/doi.org\/10.1902\/jop.2008.070578","journal-title":"J Periodontol"},{"issue":"4","key":"5977_CR2","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1109\/10.133227","volume":"38","author":"VDPF Stelt","year":"1991","unstructured":"Stelt VDPF, Geraets WG (1991) Computer-aided interpretation and quantification of angular periodontal bone defects on dental radiographs. IEEE Trans Biomed Eng 38(4):334\u2013338. https:\/\/doi.org\/10.1109\/10.133227","journal-title":"IEEE Trans Biomed Eng"},{"issue":"4","key":"5977_CR3","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1109\/10.133226","volume":"38","author":"MK Jeffcoat","year":"1991","unstructured":"Jeffcoat MK, Jeffcoat RL, Captain K (1991) A periodontal probe with automated cemento-enamel detection-design and clinical trials. IEEE Trans Biomed Eng 38(4):330\u2013334. https:\/\/doi.org\/10.1109\/10.133226","journal-title":"IEEE Trans Biomed Eng"},{"key":"5977_CR4","doi-asserted-by":"publisher","unstructured":"Verma D, Puri S, Prabhu S, Smriti K (2020) Anomaly detection in panoramic dental x-rays using a hybrid Deep Learning and Machine Learning approach. IEEE Region 263\u2013268.\u00a0\u00a0https:\/\/doi.org\/10.1109\/TENCON50793.2020.9293765","DOI":"10.1109\/TENCON50793.2020.9293765"},{"issue":"5","key":"5977_CR5","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1259\/dmfr\/57711133","volume":"37","author":"B Vandenberghe","year":"2014","unstructured":"Vandenberghe B, Jacobs R, Yang J (2014) Detection of periodontal bone loss using digital intraoral and cone beam computed tomography images: an in vitro assessment of bony and\/or infrabony defects. Dentomaxillofacial Radiol 37(5):252\u2013260. https:\/\/doi.org\/10.1259\/dmfr\/57711133","journal-title":"Dentomaxillofacial Radiol"},{"key":"5977_CR6","doi-asserted-by":"publisher","DOI":"10.1902\/jop.2000.71.4.598","author":"PV Nummikoski","year":"2000","unstructured":"Nummikoski PV, Steffensen B, Hamilton K, Dove SB (2000) Clinical validation of a new subtraction radiography technique for periodontal bone loss detection. J Periodontol. https:\/\/doi.org\/10.1902\/jop.2000.71.4.598","journal-title":"J Periodontol"},{"key":"5977_CR7","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1007\/s11548-021-02431-z","volume":"16","author":"RP Danks","year":"2021","unstructured":"Danks RP, Bano S, Orishko A, Tan HJ, Sancho MF, D\u2019Aiuto F, Stoyanov D (2021) Automating Periodontal bone loss measurement via dental landmark localisation. Int J Comput Assist Radiol Surg 16:1189\u20131199. https:\/\/doi.org\/10.1007\/s11548-021-02431-z","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"7","key":"5977_CR8","first-page":"513","volume":"12","author":"DG Pour","year":"2015","unstructured":"Pour DG, Romoozi E, Shayesteh YS (2015) Accuracy of cone beam computed tomography for detection of bone loss. J Dent 12(7):513\u2013523","journal-title":"J Dent"},{"key":"5977_CR9","doi-asserted-by":"publisher","DOI":"10.1196\/annals.1384.028","author":"JS Kinney","year":"2022","unstructured":"Kinney JS, Ramseier CA, Giannobile WV (2022) Oral fluid-based biomarkers of alveolar bone loss in periodontitis. Ann N Y Acad Sci. https:\/\/doi.org\/10.1196\/annals.1384.028","journal-title":"Ann N Y Acad Sci"},{"issue":"4","key":"5977_CR10","first-page":"495","volume":"11","author":"M Sheikhi","year":"2014","unstructured":"Sheikhi M, Pozve NJ, Khorrami L (2014) Using cone beam computed tomography to detect the relationship between the periodontal bone loss and mucosal thickening of the maxillary sinus. Dent Res J 11(4):495\u2013501","journal-title":"Dent Res J"},{"issue":"5","key":"5977_CR11","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1016\/j.identj.2022.02.009","volume":"72","author":"N Tsoromokos","year":"2022","unstructured":"Tsoromokos N, Parinussa S, Claessen F, Moin DA, Loos BG (2022) Estimation of alveolar bone loss in periodontitis using machine learning. Int Dent J 72(5):621\u2013627. https:\/\/doi.org\/10.1016\/j.identj.2022.02.009","journal-title":"Int Dent J"},{"key":"5977_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2017.06.012","volume":"148","author":"PL Lin","year":"2017","unstructured":"Lin PL, Huang PY, Huang PW (2017) Automatic methods for alveolar bone loss degree measurement in periodontitis periapical radiographs. Comput Methods Programs Biomed 148:1\u201311. https:\/\/doi.org\/10.1016\/j.cmpb.2017.06.012","journal-title":"Comput Methods Programs Biomed"},{"key":"5977_CR13","doi-asserted-by":"publisher","unstructured":"Tonetti MS, Prato GP, Williams RC, Cortellini P (1993) Periodontal regeneration of human infrabony defects. III. Diagnostic strategies to detect bone gain. J Periodontol 64.\u00a0\u00a0https:\/\/doi.org\/10.1902\/jop.1993.64.4.269","DOI":"10.1902\/jop.1993.64.4.269"},{"key":"5977_CR14","doi-asserted-by":"crossref","unstructured":"Amasya H, Jaju PP, Ezhov M, Gusarev M, Atakan C, Sanders A, Manulius D, Golitskya M, Shrivastava K, Singh A, Gupta A (2023) Development and validation of an artificial intelligence software for periodontal bone loss in panoramic imaging. Int J Imaging Syst Technol. https:\/\/doi.org\/10.1002\/ima.22973","DOI":"10.1002\/ima.22973"},{"issue":"6","key":"5977_CR15","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1259\/dmfr\/26475758","volume":"37","author":"A Mol","year":"2014","unstructured":"Mol A, Balasundaram A (2014) In vitro cone beam computed tomography imaging of periodontal bone. Dentomaxillofacial Radiol 37(6):319\u2013324","journal-title":"Dentomaxillofacial Radiol"},{"issue":"5","key":"5977_CR16","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1016\/0030-4220(92)90361-S","volume":"74","author":"AJ Furkart","year":"1992","unstructured":"Furkart AJ, Dove SB, McDavid WD, Nummikoski P, Matteson S (1992) Direct digital radiography for the detection of periodontal bone lesions. Oral Surg Oral Med Oral Pathol 74(5):652\u2013660. https:\/\/doi.org\/10.1016\/0030-4220(92)90361-S","journal-title":"Oral Surg Oral Med Oral Pathol"},{"key":"5977_CR17","doi-asserted-by":"crossref","unstructured":"Pinheiro LR, Scarfe WC, Augusto de Oliveira Sales M, Gaia BF, Cortes AR, Cavalcanti MG (2022) Effect of cone-beam computed tomography field of view and acquisition frame on the detection of chemically simulated peri-implant bone loss in vitro. J Periodontol. https:\/\/doi.org\/10.1902\/jop.2015.150223","DOI":"10.1902\/jop.2015.150223"},{"key":"5977_CR18","doi-asserted-by":"publisher","first-page":"7531","DOI":"10.1038\/s41598-020-64509-z","volume":"10","author":"HJ Chang","year":"2020","unstructured":"Chang HJ, Lee SJ, Yong TH, Shin NY, Jang BG, Kim JE, Huh KH, Lee SS, Heo MS, Choi SC, Kim TI (2020) Deep learning hybrid method to automatically diagnose periodontal bone loss and stage periodontitis. Sci Rep 10:7531. https:\/\/doi.org\/10.1038\/s41598-020-64509-z","journal-title":"Sci Rep"},{"issue":"23","key":"5977_CR19","doi-asserted-by":"publisher","first-page":"3562","DOI":"10.3390\/diagnostics13233562","volume":"13","author":"H Dujic","year":"2023","unstructured":"Dujic H, Meyer O, Hoss P, W\u00f6lfle UC, W\u00fclk A, Meusburger T, Meier L, Gruhn V, Hesenius M, Hickel R, K\u00fchnisch J (2023) Automatized detection of periodontal bone loss on periapical radiographs by vision transformer networks. Diagnostics 13(23):3562. https:\/\/doi.org\/10.3390\/diagnostics13233562","journal-title":"Diagnostics"},{"key":"5977_CR20","doi-asserted-by":"publisher","unstructured":"Chen CC, Wu YF, Aung LM, Lin JC, Ngo ST, Su JN, Lin YM, Chang WJ (2023) Automatic recognition of teeth and periodontal bone loss measurement in digital radiographs using deep-learning artificial intelligence. J Dent Sci 18(3). https:\/\/doi.org\/10.1016\/j.jds.2023.03.020","DOI":"10.1016\/j.jds.2023.03.020"},{"key":"5977_CR21","doi-asserted-by":"publisher","first-page":"103844","DOI":"10.1016\/j.bspc.2022.103844","volume":"77","author":"KM Sunnetci","year":"2022","unstructured":"Sunnetci KM, Ulukaya S, Alkan A (2022) Periodontal bone loss detection based on hybrid deep learning and machine learning models with a user-friendly application. Biomed Signal Process Control 77:103844. https:\/\/doi.org\/10.1016\/j.bspc.2022.103844","journal-title":"Biomed Signal Process Control"},{"key":"5977_CR22","doi-asserted-by":"publisher","first-page":"17615","DOI":"10.1038\/s41598-019-53758-2","volume":"9","author":"J Kim","year":"2019","unstructured":"Kim J, Lee HS, Song IS, Jung KH (2019) DeNTNet: Deep Neural Transfer Network for the detection of periodontal bone loss using panoramic dental radiographs. Sci Rep 9:17615. https:\/\/doi.org\/10.1038\/s41598-019-53758-2","journal-title":"Sci Rep"},{"key":"5977_CR23","doi-asserted-by":"publisher","first-page":"106374","DOI":"10.1016\/j.compbiomed.2022.106374","volume":"152","author":"Z Kong","year":"2023","unstructured":"Kong Z, Ouyang H, Cao Y, Huang T, Ahn E, Zhang M, Liu H (2023) Automated periodontitis bone loss diagnosis in panoramic radiographs using a bespoke two-stage detector. Comput Biol Med 152:106374. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106374","journal-title":"Comput Biol Med"},{"key":"5977_CR24","doi-asserted-by":"crossref","unstructured":"Chen IH, Lin CH, Lee MK, Chen TE, Lan TH, Chang CM, Tseng TY, Wang T, Du JK (2023) Convolutional-neural-network-basedradiographs evaluation assisting in early diagnosis of the periodontal bone loss via periapical radiograph. J Dent Sci. https:\/\/doi.org\/10.1016\/j.jds.2023.09.032","DOI":"10.1016\/j.jds.2023.09.032"},{"key":"5977_CR25","doi-asserted-by":"crossref","unstructured":"Vigil MA, Bharathi VS (2021) Detection of periodontal bone loss in mandibular area from dental panoramic radiograph using image processing techniques. Wiley Online Library. https:\/\/doi.org\/10.1002\/cpe.6323","DOI":"10.1002\/cpe.6323"},{"key":"5977_CR26","doi-asserted-by":"publisher","first-page":"110539","DOI":"10.1016\/j.dib.2024.110539","volume":"54","author":"N Thalji","year":"2024","unstructured":"Thalji N, Aljarrah E, Almomani MH, Raza A, Migdady H, Abualigah L (2024) Segmented X-ray image data for diagnosing dental periapical diseases using deep learning. Data Brief 54:110539. https:\/\/doi.org\/10.1016\/j.dib.2024.110539","journal-title":"Data Brief"},{"key":"5977_CR27","doi-asserted-by":"publisher","unstructured":"Kurt-Bayrakdar S, Bayrakdar \u0130\u015e, Yavuz MB, Sali N, \u00c7elik \u00d6, K\u00f6se O, Saylan BCU, Kuleli B, Jagtap R, Orhan K (2024) Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study. BMC Oral Health 24. https:\/\/doi.org\/10.1186\/s12903-024-03896-5","DOI":"10.1186\/s12903-024-03896-5"},{"key":"5977_CR28","doi-asserted-by":"publisher","first-page":"115945","DOI":"10.1109\/ACCESS.2022.3218374","volume":"10","author":"M Liu","year":"2022","unstructured":"Liu M, Qin H, Cao R, Deng S (2022) Short-term load forecasting based on improved TCN and DenseNet. IEEE Access 10:115945\u2013115957. https:\/\/doi.org\/10.1109\/ACCESS.2022.3218374","journal-title":"IEEE Access"},{"issue":"6","key":"5977_CR29","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2019","unstructured":"Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2019) Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging 39(6):1856\u20131867. https:\/\/doi.org\/10.1109\/TMI.2019.2959609","journal-title":"IEEE Trans Med Imaging"},{"key":"5977_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/8867776","volume":"2021","author":"L Xiong","year":"2020","unstructured":"Xiong L, Zhang L, Huang X, Yang X, Huang W, Zeng H, Tang H (2020) DCAST: a spatiotemporal model with DenseNet and GRU based on attention mechanism. Math Probl Eng 2021:1\u201312. https:\/\/doi.org\/10.1155\/2021\/8867776","journal-title":"Math Probl Eng"},{"issue":"15","key":"5977_CR31","doi-asserted-by":"publisher","first-page":"3305","DOI":"10.3390\/math11153305","volume":"11","author":"SM Alsharee","year":"2023","unstructured":"Alsharee SM, Fathy A (2023) Efficient red kite optimization algorithm for integrating the renewable sources and electric vehicle fast charging stations in radial distribution networks. Mathematics 11(15):3305. https:\/\/doi.org\/10.3390\/math11153305","journal-title":"Mathematics"},{"issue":"4","key":"5977_CR32","doi-asserted-by":"publisher","first-page":"1482","DOI":"10.1109\/TCSII.2020.3039526A","volume":"68","author":"H Wu","year":"2021","unstructured":"Wu H, Ma X, Yang CH, Liu S (2021) Attention based bidirectional convolutional LSTM for high-resolution radio tomographic imaging. IEEE Trans Circ Syst II: Express Briefs 68(4):1482\u20131486. https:\/\/doi.org\/10.1109\/TCSII.2020.3039526A","journal-title":"IEEE Trans Circ Syst II: Express Briefs"},{"key":"5977_CR33","doi-asserted-by":"publisher","first-page":"158322","DOI":"10.1109\/ACCESS.2021.3128960","volume":"9","author":"K Li","year":"2021","unstructured":"Li K, Shen N, Kang Y, Chen H, Wang Y, He S (2021) Livestock product price forecasting method based on heterogeneous GRU neural network and energy decomposition. IEEE Access 9:158322\u2013158330. https:\/\/doi.org\/10.1109\/ACCESS.2021.3128960","journal-title":"IEEE Access"},{"key":"5977_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2023.3252048","volume":"20","author":"Y Wang","year":"2023","unstructured":"Wang Y, Gu L, Jiang T, Gao F (2023) MDE-UNet: A multitask deformable UNet combined enhancement network for farmland boundary segmentation. IEEE Geosci Remote Sens Lett 20:1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2023.3252048","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"5977_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2023.3288742","volume":"20","author":"N Ma","year":"2023","unstructured":"Ma N, Sun L, He Y, Zhou C, Dong C (2023) CNN-TransNet: A hybrid CNN-transformer network with differential feature enhancement for cloud detection. IEEE Geosci Remote Sens Lett 20:1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2023.3288742","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"5977_CR36","doi-asserted-by":"publisher","first-page":"5526","DOI":"10.1109\/JSTARS.2023.3278862","volume":"16","author":"K Zheng","year":"2023","unstructured":"Zheng K, Wang H, Qin F, Miao C, Han Z (2023) An improved land use classification method based on DeepLab V3+ under GauGAN data enhancement. IEEE J Sel Top Appl Earth Obs Remote Sens 16:5526\u20135537. https:\/\/doi.org\/10.1109\/JSTARS.2023.3278862","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"5977_CR37","doi-asserted-by":"crossref","unstructured":"Jing J, Wang Z, R\u00e4tsch M, Zhang H (2020) Mobile-Unet: An efficient convolutional neural network for fabric defect detection. Text Res J. https:\/\/doi.org\/10.1177\/0040517520928604","DOI":"10.1177\/0040517520928604"},{"key":"5977_CR38","doi-asserted-by":"publisher","unstructured":"Xie L, Han T, Zhou H, Zhang ZR, Han B, Tang A (2021) Tuna swarm optimization: a novel swarm-based metaheuristic algorithm for global optimization. Comput Intell Neurosci.\u00a0\u00a0https:\/\/doi.org\/10.1155\/2021\/9210050","DOI":"10.1155\/2021\/9210050"},{"key":"5977_CR39","doi-asserted-by":"publisher","first-page":"109215","DOI":"10.1016\/j.knosys.2022.109215","volume":"251","author":"C Zhong","year":"2022","unstructured":"Zhong C, Li G, Meng Z (2022) Beluga whale optimization: a novel nature-inspired metaheuristic algorithm. Knowl-Based Syst 251:109215. https:\/\/doi.org\/10.1016\/j.knosys.2022.109215","journal-title":"Knowl-Based Syst"},{"key":"5977_CR40","doi-asserted-by":"publisher","first-page":"10953","DOI":"10.1038\/s41598-022-14338-z","volume":"12","author":"MA Akbari","year":"2022","unstructured":"Akbari MA, Zare M, Abarghooee AR, Mirjalili S, Deriche M (2022) The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems. Sci Rep 12:10953. https:\/\/doi.org\/10.1038\/s41598-022-14338-z","journal-title":"Sci Rep"}],"container-title":["Clinical Oral Investigations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00784-024-05977-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00784-024-05977-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00784-024-05977-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T09:18:40Z","timestamp":1731662320000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00784-024-05977-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":40,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["5977"],"URL":"https:\/\/doi.org\/10.1007\/s00784-024-05977-9","relation":{},"ISSN":["1436-3771"],"issn-type":[{"value":"1436-3771","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,28]]},"assertion":[{"value":"28 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not Applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"614"}}