{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T03:18:48Z","timestamp":1783048728493,"version":"3.54.6"},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100009104","name":"Department of Health Research, India","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001411","name":"Indian Council of Medical Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001411","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004541","name":"Ministry of Education, India","doi-asserted-by":"publisher","award":["17-11\/2015-PN-1"],"award-info":[{"award-number":["17-11\/2015-PN-1"]}],"id":[{"id":"10.13039\/501100004541","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007334","name":"Ministry of Health and Family Welfare","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007334","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100016280","name":"Panjab University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100016280","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001409","name":"Department of Science and Technology, Ministry of Science and Technology, India","doi-asserted-by":"publisher","award":["SR\/FST\/ET-I\/2021\/878"],"award-info":[{"award-number":["SR\/FST\/ET-I\/2021\/878"]}],"id":[{"id":"10.13039\/501100001409","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.bspc.2026.110735","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T11:57:20Z","timestamp":1780487840000},"page":"110735","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["FD-CAD: An AI-based Computer-Aided Diagnosis System for Accurate Furcation Detection in Dental Radiographs"],"prefix":"10.1016","volume":"125","author":[{"family":"Priyanka","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sahil","family":"Pathak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2611-9005","authenticated-orcid":false,"given":"Mamta","family":"Juneja","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naveen","family":"Aggarwal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0285-403X","authenticated-orcid":false,"given":"Manojkumar","family":"Jaiswal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Priyanka","family":"Rana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.bspc.2026.110735_b1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1111\/prd.12447","article-title":"Risk factors for periodontitis & peri-implantitis","volume":"90","author":"Darby","year":"2022","journal-title":"Periodontol."},{"issue":"11","key":"10.1016\/j.bspc.2026.110735_b2","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1038\/s41415-022-5254-y","article-title":"Furcation-involved molar teeth-part 2: management and prognosis","volume":"233","author":"Gill","year":"2022","journal-title":"Br. Dent. J."},{"issue":"10","key":"10.1016\/j.bspc.2026.110735_b3","article-title":"Functional and structural aspects in periodontal furcation treatment: a novel approach","volume":"53","author":"Neumeyer","year":"2022","journal-title":"Quintessence Int."},{"issue":"4","key":"10.1016\/j.bspc.2026.110735_b4","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/0300-5712(84)90087-3","article-title":"Interpretation of intraoral periapical radiographs","volume":"12","author":"Wahab","year":"1984","journal-title":"J. Dent."},{"issue":"4","key":"10.1016\/j.bspc.2026.110735_b5","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1136\/ewjm.174.4.288","article-title":"Dental damage, sequelae, and prevention","volume":"174","author":"Holt","year":"2001","journal-title":"West. J. Med."},{"issue":"9","key":"10.1016\/j.bspc.2026.110735_b6","first-page":"223","article-title":"Clinical and radiographic characteristics of the primary teeth indicated for pulpectomy: a cross-sectional analysis","volume":"2","author":"Morankar","year":"2018","journal-title":"Int. Heal. Res. J."},{"issue":"4","key":"10.1016\/j.bspc.2026.110735_b7","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1007\/s12663-013-0499-2","article-title":"Intraoral periapical radiographs with grids for implant dentistry","volume":"13","author":"Deshpande","year":"2014","journal-title":"J. Maxillofacial Oral Surg."},{"issue":"3","key":"10.1016\/j.bspc.2026.110735_b8","doi-asserted-by":"crossref","first-page":"236","DOI":"10.4258\/hir.2018.24.3.236","article-title":"Application of convolutional neural network in the diagnosis of jaw tumors","volume":"24","author":"Poedjiastoeti","year":"2018","journal-title":"Heal. Informatics Res."},{"key":"10.1016\/j.bspc.2026.110735_b9","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compmedimag.2018.07.001","article-title":"An effective teeth recognition method using label tree with cascade network structure","volume":"68","author":"Zhang","year":"2018","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.bspc.2026.110735_b10","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jdent.2018.07.015","article-title":"Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm","volume":"77","author":"Lee","year":"2018","journal-title":"J. Dent."},{"issue":"2","key":"10.1016\/j.bspc.2026.110735_b11","doi-asserted-by":"crossref","first-page":"47","DOI":"10.3390\/bioengineering5020047","article-title":"Deep artificial neural networks for the diagnostic of caries using socioeconomic and nutritional features as determinants: Data from nhanes 2013\u20132014","volume":"5","author":"Zanella-Calzada","year":"2018","journal-title":"Bioengineering"},{"issue":"3","key":"10.1016\/j.bspc.2026.110735_b12","doi-asserted-by":"crossref","DOI":"10.1259\/dmfr.20180218","article-title":"A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography","volume":"48","author":"Hiraiwa","year":"2019","journal-title":"Dentomaxillofacial Radiol."},{"issue":"1","key":"10.1016\/j.bspc.2026.110735_b13","doi-asserted-by":"crossref","first-page":"3840","DOI":"10.1038\/s41598-019-40414-y","article-title":"A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films","volume":"9","author":"Chen","year":"2019","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.bspc.2026.110735_b14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5624\/isd.2019.49.1.1","article-title":"An overview of deep learning in the field of dentistry","volume":"49","author":"Hwang","year":"2019","journal-title":"Imaging Sci. Dent."},{"issue":"1","key":"10.1016\/j.bspc.2026.110735_b15","doi-asserted-by":"crossref","first-page":"9007","DOI":"10.1038\/s41598-019-45487-3","article-title":"Automated detection of third molars and mandibular nerve by deep learning","volume":"9","author":"Vinayahalingam","year":"2019","journal-title":"Sci. Rep."},{"issue":"7","key":"10.1016\/j.bspc.2026.110735_b16","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1016\/j.joen.2019.03.016","article-title":"Deep learning for the radiographic detection of apical lesions","volume":"45","author":"Ekert","year":"2019","journal-title":"J. Endod."},{"issue":"4","key":"10.1016\/j.bspc.2026.110735_b17","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.oooo.2019.05.014","article-title":"Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique","volume":"128","author":"Ariji","year":"2019","journal-title":"Oral Surg. Oral Med. Oral Pathol. Oral Radiol."},{"issue":"1","key":"10.1016\/j.bspc.2026.110735_b18","doi-asserted-by":"crossref","DOI":"10.1259\/dmfr.20170344","article-title":"Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study","volume":"48","author":"Lee","year":"2019","journal-title":"Dentomaxillofacial Radiol."},{"issue":"4","key":"10.1016\/j.bspc.2026.110735_b19","doi-asserted-by":"crossref","DOI":"10.1259\/dmfr.20180051","article-title":"Tooth detection and numbering in panoramic radiographs using convolutional neural networks","volume":"48","author":"Tuzoff","year":"2019","journal-title":"Dentomaxillofacial Radiol."},{"issue":"3","key":"10.1016\/j.bspc.2026.110735_b20","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s11282-018-0363-7","article-title":"Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography","volume":"35","author":"Murata","year":"2019","journal-title":"Oral Radiol."},{"issue":"4","key":"10.1016\/j.bspc.2026.110735_b21","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s11282-019-00409-x","article-title":"Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography","volume":"36","author":"Fukuda","year":"2020","journal-title":"Oral Radiol."},{"issue":"6","key":"10.1016\/j.bspc.2026.110735_b22","doi-asserted-by":"crossref","DOI":"10.1259\/dmfr.20200172","article-title":"Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs","volume":"50","author":"K\u0131l\u0131c","year":"2021","journal-title":"Dentomaxillofacial Radiol."},{"issue":"4","key":"10.1016\/j.bspc.2026.110735_b23","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1177\/0022034520972335","article-title":"Cost-effectiveness of artificial intelligence for proximal caries detection","volume":"100","author":"Schwendicke","year":"2021","journal-title":"J. Dent. Res."},{"issue":"3","key":"10.1016\/j.bspc.2026.110735_b24","doi-asserted-by":"crossref","first-page":"237","DOI":"10.5624\/isd.20210074","article-title":"Deep learning convolutional neural network algorithms for the early detection and diagnosis of dental caries on periapical radiographs: A systematic review","volume":"51","author":"Musri","year":"2021","journal-title":"Imaging Sci. Dent."},{"issue":"4","key":"10.1016\/j.bspc.2026.110735_b25","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1007\/s11282-021-00577-9","article-title":"Deep-learning approach for caries detection and segmentation on dental bitewing radiographs","volume":"38","author":"Bayrakdar","year":"2022","journal-title":"Oral Radiol."},{"issue":"7","key":"10.1016\/j.bspc.2026.110735_b26","doi-asserted-by":"crossref","first-page":"802","DOI":"10.3390\/bioengineering10070802","article-title":"Deep learning for dental diagnosis: a novel approach to furcation involvement detection on periapical radiographs","volume":"10","author":"Mao","year":"2023","journal-title":"Bioengineering"},{"issue":"3","key":"10.1016\/j.bspc.2026.110735_b27","doi-asserted-by":"crossref","first-page":"257","DOI":"10.5624\/isd.20240020","article-title":"Classification of mandibular molar furcation involvement in periapical radiographs by deep learning","volume":"54","author":"Vilkomir","year":"2024","journal-title":"Imaging Sci. Dent."},{"issue":"1","key":"10.1016\/j.bspc.2026.110735_b28","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1186\/s12903-024-03896-5","article-title":"Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study","volume":"24","author":"Kurt-Bayrakdar","year":"2024","journal-title":"BMC Oral. Health"},{"issue":"1","key":"10.1016\/j.bspc.2026.110735_b29","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1186\/s12903-024-05268-5","article-title":"Application of artificial intelligence-based detection of furcation involvement in mandibular first molar using cone beam tomography images-a preliminary study","volume":"24","author":"Shetty","year":"2024","journal-title":"BMC Oral. Health"},{"issue":"1","key":"10.1016\/j.bspc.2026.110735_b30","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1186\/s12903-024-03896-5","article-title":"Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study","volume":"24","author":"Kurt-Bayrakdar","year":"2024","journal-title":"BMC Oral. Health"},{"key":"10.1016\/j.bspc.2026.110735_b31","first-page":"1","article-title":"Segmentation of the nasopalatine canal and detection of canal furcation status with artificial intelligence on cone-beam computed tomography images","author":"Deniz","year":"2025","journal-title":"Oral Radiol."},{"issue":"3","key":"10.1016\/j.bspc.2026.110735_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.oooo.2024.11.036","article-title":"Detection of mandibular molar furcation involvement on intraoral radiograph by machine learning","volume":"139","author":"Baldwin","year":"2025","journal-title":"Oral Surg. Oral Med. Oral Pathol. Oral Radiol."},{"issue":"1","key":"10.1016\/j.bspc.2026.110735_b33","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1186\/s12903-025-05431-6","article-title":"Enhancing furcation involvement classification on panoramic radiographs with vision transformers","volume":"25","author":"Zhang","year":"2025","journal-title":"BMC Oral. Health"},{"issue":"11","key":"10.1016\/j.bspc.2026.110735_b34","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.3390\/children12111517","article-title":"Panoramic radiograph-based deep learning models for diagnosis and clinical decision support of furcation lesions in primary molars","volume":"12","author":"Karam\u00fcft\u00fco\u011flu","year":"2025","journal-title":"Children"},{"issue":"4","key":"10.1016\/j.bspc.2026.110735_b35","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"issue":"11","key":"10.1016\/j.bspc.2026.110735_b36","first-page":"73","article-title":"Yolo versions architecture","volume":"9","author":"Hasan","year":"2023","journal-title":"Int. J. Adv. Sci. Res. Eng."},{"issue":"35","key":"10.1016\/j.bspc.2026.110735_b37","doi-asserted-by":"crossref","first-page":"83535","DOI":"10.1007\/s11042-024-18872-y","article-title":"Yolo-based object detection models: A review and its applications","volume":"83","author":"Vijayakumar","year":"2024","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.bspc.2026.110735_b38","series-title":"International Conference on Data Intelligence and Cognitive Informatics","first-page":"529","article-title":"A review on yolov8 and its advancements","author":"Sohan","year":"2024"},{"key":"10.1016\/j.bspc.2026.110735_b39","series-title":"European Conference on Computer Vision","first-page":"1","article-title":"Yolov9: Learning what you want to learn using programmable gradient information","author":"Wang","year":"2024"},{"key":"10.1016\/j.bspc.2026.110735_b40","doi-asserted-by":"crossref","first-page":"107984","DOI":"10.52202\/079017-3429","article-title":"Yolov10: Real-time end-to-end object detection","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.110735_b41","series-title":"Yolov11: An overview of the key architectural enhancements","author":"Khanam","year":"2024"},{"key":"10.1016\/j.bspc.2026.110735_b42","doi-asserted-by":"crossref","unstructured":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.bspc.2026.110735_b43","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.110735_b44","doi-asserted-by":"crossref","unstructured":"Zhi Tian, Chunhua Shen, Hao Chen, Tong He, Fcos: Fully convolutional one-stage object detection, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 9627\u20139636.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"10.1016\/j.bspc.2026.110735_b45","unstructured":"Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Doll\u00e1r, Focal loss for dense object detection, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 2980\u20132988."},{"key":"10.1016\/j.bspc.2026.110735_b46","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9759","article-title":"Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection","author":"Zhang","year":"2020"},{"issue":"2","key":"10.1016\/j.bspc.2026.110735_b47","first-page":"1","article-title":"A review on evaluation metrics for data classification evaluations","volume":"5","author":"Hossin","year":"2015","journal-title":"Int. J. Data Min. Knowl. Manag. Process."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426012899?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426012899?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T02:53:58Z","timestamp":1783047238000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426012899"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":47,"alternative-id":["S1746809426012899"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110735","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FD-CAD: An AI-based Computer-Aided Diagnosis System for Accurate Furcation Detection in Dental Radiographs","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110735","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110735"}}