{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T09:35:59Z","timestamp":1793352959339,"version":"4.5.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T00:00:00Z","timestamp":1748217600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T00:00:00Z","timestamp":1748217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Jinan Medical and Health Science and Technology Big Data Development Plan","award":["2023-YBD-1-08"],"award-info":[{"award-number":["2023-YBD-1-08"]}]},{"name":"Jinan Medical and Health Science and Technology Big Data Development Plan","award":["2022-YBD-17"],"award-info":[{"award-number":["2022-YBD-17"]}]},{"name":"Jinan Municipal Science and Technology Plan Project","award":["202430028"],"award-info":[{"award-number":["202430028"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Oral Health"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Purpose<\/jats:title>\n            <jats:p>Numerous studies have investigated the use of convolutional neural network (CNN) models for detecting periapical lesions(PLs). However, limited research has focused on evaluating their potential in assisting clinicians with diagnosis. This study aims to utilize two deep learning(DL) models, ConvNeXt and ResNet34, to aid novice dentists in the detection of PLs on periapical radiographs (PRs). By assessing the diagnostic support provided by these models, this research seeks to promote the clinical application of DL in dentistry.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Materials and methods<\/jats:title>\n            <jats:p>In this study, 1,305 PRs were gathered and then split into a training set of 1,044 images and a validation set of 261 images, following an 80\/20 ratio. The model\u2019s effectiveness was assessed using various measures, including precision, sensitivity, F1 score, specificity, accuracy, and the area under the curve (AUC). To evaluate the impact of the model on diagnostic performance by novice dentists, we used an additional set of 800 individual teeth PRs, which were not included in the training or validation sets. The diagnostic performance and time of three novice dentists were measured both with and without model assistance.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The precision of ConvNeXt was 85.93%, with an F1 score of 0.92, accuracy of 91.25%, sensitivity of 98.49%, specificity of 84.11%, and an AUC of 0.9693, outperforming ResNet34 across all metrics. In comparison, ResNet34 achieved a precision of 83.08%, an F1 score of 0.84, accuracy of 81.63%, sensitivity of 84.38%, specificity of 78.13%, and an AUC of 0.8988. In the model-assisted diagnosis phase, both ConvNeXt and ResNet34 improved the diagnostic performance of novice dentists. With the help of ConvNeXt, the average AUC of three dentists increased from 0.88 to 0.94, while with ResNet34, the average AUC of the three dentists improved from 0.88 to 0.91. ConvNeXt performed better than ResNet34 (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05). Additionally, ConvNeXt reduced the average diagnostic time of the three dentists from 178.8\u00a0min to 141.9\u00a0min, while ResNet34 reduced the average diagnostic time from 178.8\u00a0min to 153.6\u00a0min.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>ConvNeXt significantly improved the diagnostic performance of novice dentists and reduced the time required for diagnosis, thereby enhancing clinical efficiency in both diagnosis and treatment. This model shows potential for application in dental clinics or educational institutions where experienced specialists are limited, but there is a large presence of novice, less-experienced dentists.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12903-025-06104-0","type":"journal-article","created":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T16:09:27Z","timestamp":1748275767000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A comparative analysis of deep learning models for assisting in the diagnosis of periapical lesions in periapical radiographs"],"prefix":"10.1186","volume":"25","author":[{"given":"Jian","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoran","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kexu","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuoyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinxuan","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xijiao","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,26]]},"reference":[{"issue":"3","key":"6104_CR1","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1111\/iej.12625","volume":"50","author":"S Huumonen","year":"2017","unstructured":"Huumonen S, Suominen AL, Vehkalahti MM. Prevalence of apical periodontitis in root filled teeth: findings from a nationwide survey in Finland. Int Endod J. 2017;50(3):229\u201336. https:\/\/doi.org\/10.1111\/iej.12625","journal-title":"Int Endod J"},{"issue":"8","key":"6104_CR2","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1016\/j.joen.2019.05.002","volume":"45","author":"T Chew","year":"2019","unstructured":"Chew T, Brennan D, Rossi-Fedele G. Comparative longitudinal study on the impact root Canal treatment and other dental services have on oral health-related quality of life using Self-reported health measures (Oral health impact Profile-14 and global health measures). J Endod. 2019;45(8):985\u2013e9931. https:\/\/doi.org\/10.1016\/j.joen.2019.05.002","journal-title":"J Endod"},{"issue":"10","key":"6104_CR3","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1111\/iej.12507","volume":"48","author":"JJ Segura-Egea","year":"2015","unstructured":"Segura-Egea JJ, Mart\u00edn-Gonz\u00e1lez J, Castellanos-Cosano L. Endodontic medicine: connections between apical periodontitis and systemic diseases. Int Endod J. 2015;48(10):933\u201351. https:\/\/doi.org\/10.1111\/iej.12507","journal-title":"Int Endod J"},{"issue":"5","key":"6104_CR4","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1111\/iej.12651","volume":"50","author":"S Kanagasingam","year":"2017","unstructured":"Kanagasingam S, Hussaini HM, Soo I, Baharin S, Ashar A, Patel S. Accuracy of single and parallax film and digital periapical radiographs in diagnosing apical periodontitis - a cadaver study. Int Endod J. 2017;50(5):427\u201336. https:\/\/doi.org\/10.1111\/iej.12651","journal-title":"Int Endod J"},{"issue":"9","key":"6104_CR5","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1038\/sj.bdj.2015.340","volume":"218","author":"SV van der Waal","year":"2015","unstructured":"van der Waal SV, Lappin DF, Crielaard W. Does apical periodontitis have systemic consequences? The need for well-planned and carefully conducted clinical studies. Br Dent J. 2015;218(9):513\u20136. https:\/\/doi.org\/10.1038\/sj.bdj.2015.340","journal-title":"Br Dent J"},{"issue":"12","key":"6104_CR6","doi-asserted-by":"publisher","first-page":"1675","DOI":"10.1111\/iej.13187","volume":"52","author":"S Patel","year":"2019","unstructured":"Patel S, Brown J, Semper M, Abella F, Mannocci F. European society of endodontology position statement: use of cone beam computed tomography in endodontics: European society of endodontology (ESE) developed by. Int Endod J. 2019;52(12):1675\u20138. https:\/\/doi.org\/10.1111\/iej.13187","journal-title":"Int Endod J"},{"issue":"7","key":"6104_CR7","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1016\/j.joen.2019.03.016","volume":"45","author":"T Ekert","year":"2019","unstructured":"Ekert T, Krois J, Meinhold L, et al. Deep learning for the radiographic detection of apical lesions. J Endod. 2019;45(7):917\u2013e9225. https:\/\/doi.org\/10.1016\/j.joen.2019.03.016","journal-title":"J Endod"},{"issue":"6","key":"6104_CR8","doi-asserted-by":"publisher","first-page":"20190290","DOI":"10.1259\/dmfr.20190290","volume":"49","author":"ZB Arslan","year":"2020","unstructured":"Arslan ZB, Demir H, Berker Y\u0131ld\u0131z D, Ya\u015far F. Diagnostic accuracy of panoramic radiography and ultrasonography in detecting periapical lesions using periapical radiography as a gold standard. Dentomaxillofac Radiol. 2020;49(6):20190290. https:\/\/doi.org\/10.1259\/dmfr.20190290","journal-title":"Dentomaxillofac Radiol"},{"issue":"6","key":"6104_CR9","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1067\/moe.2001.118904","volume":"92","author":"P Velvart","year":"2001","unstructured":"Velvart P, Hecker H, Tillinger G. Detection of the apical lesion and the mandibular Canal in conventional radiography and computed tomography. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2001;92(6):682\u20138. https:\/\/doi.org\/10.1067\/moe.2001.118904","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol Endod"},{"issue":"1","key":"6104_CR10","doi-asserted-by":"publisher","first-page":"43","DOI":"10.3978\/j.issn.2223-4292.2013.02.07","volume":"3","author":"A Sabarudin","year":"2013","unstructured":"Sabarudin A, Tiau YJ. Image quality assessment in panoramic dental radiography: a comparative study between conventional and digital systems. Quant Imaging Med Surg. 2013;3(1):43\u20138. https:\/\/doi.org\/10.3978\/j.issn.2223-4292.2013.02.07","journal-title":"Quant Imaging Med Surg"},{"key":"6104_CR11","first-page":"13","volume":"13","author":"M Hamid","year":"2017","unstructured":"Hamid M, Singer S. Challenges associated with digital radiology in dentistry. EC Dent Sci. 2017;13:13\u201323.","journal-title":"EC Dent Sci"},{"issue":"5","key":"6104_CR12","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany DS, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treatable diseases by Image-Based deep learning. Cell. 2018;172(5):1122\u2013e11319. https:\/\/doi.org\/10.1016\/j.cell.2018.02.010","journal-title":"Cell"},{"issue":"2","key":"6104_CR13","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1177\/00220345211032524","volume":"101","author":"J K\u00fchnisch","year":"2022","unstructured":"K\u00fchnisch J, Meyer O, Hesenius M, Hickel R, Gruhn V. Caries detection on intraoral images using artificial intelligence. J Dent Res. 2022;101(2):158\u201365. https:\/\/doi.org\/10.1177\/00220345211032524","journal-title":"J Dent Res"},{"issue":"4","key":"6104_CR14","doi-asserted-by":"publisher","first-page":"383","DOI":"10.5624\/isd.20220105","volume":"52","author":"R Widyaningrum","year":"2022","unstructured":"Widyaningrum R, Candradewi I, Aji NRAS, Aulianisa R. Comparison of Multi-Label U-Net and mask R-CNN for panoramic radiograph segmentation to detect periodontitis. Imaging Sci Dent. 2022;52(4):383\u201391. https:\/\/doi.org\/10.5624\/isd.20220105","journal-title":"Imaging Sci Dent"},{"issue":"1","key":"6104_CR15","doi-asserted-by":"publisher","first-page":"197","DOI":"10.3390\/jcm13010197","volume":"13","author":"A Hadzic","year":"2023","unstructured":"Hadzic A, Urschler M, Press JA, et al. Evaluating a periapical lesion detection CNN on a clinically representative CBCT Dataset-A validation study. J Clin Med. 2023;13(1):197. https:\/\/doi.org\/10.3390\/jcm13010197. Published 2023 Dec 29.","journal-title":"J Clin Med"},{"key":"6104_CR16","doi-asserted-by":"publisher","unstructured":"Guo J, Wu Y, Chen L et al. A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis. Biomed Eng Online. 2022;21(1):36. Published 2022 Jun 15. https:\/\/doi.org\/10.1186\/s12938-022-01008-4","DOI":"10.1186\/s12938-022-01008-4"},{"issue":"4","key":"6104_CR17","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1007\/s11548-021-02319-y","volume":"16","author":"H Chen","year":"2021","unstructured":"Chen H, Li H, Zhao Y, Zhao J, Wang Y. Dental disease detection on periapical radiographs based on deep convolutional neural networks. Int J Comput Assist Radiol Surg. 2021;16(4):649\u201361. https:\/\/doi.org\/10.1007\/s11548-021-02319-y","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"1","key":"6104_CR18","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1177\/00220345231201793","volume":"103","author":"WT Fu","year":"2024","unstructured":"Fu WT, Zhu QK, Li N, et al. Clinically oriented CBCT periapical lesion evaluation via 3D CNN algorithm. J Dent Res. 2024;103(1):5\u201312. https:\/\/doi.org\/10.1177\/00220345231201793","journal-title":"J Dent Res"},{"key":"6104_CR19","doi-asserted-by":"publisher","unstructured":"Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S, A ConvNet for the 2020s, 2022 IEEE\/CVF Conference on Computer Vision and, Recognition P. (CVPR), New Orleans, LA, USA. 2022:11966\u201311976. https:\/\/doi.org\/10.1109\/CVPR52688.2022.01167","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"6104_CR20","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer Vision\u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J. Identity mappings in deep residual networks. In: Leibe B, Matas J, Sebe N, Welling M, editors. Computer Vision\u2013 ECCV 2016. Cham: Springer International Publishing; 2016. pp. 630\u201345."},{"key":"6104_CR21","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J, Recognition P. Deep Residual Learning for Image Recognition, (CVPR), Las Vegas, NV, USA. 2016:770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"issue":"5S1","key":"6104_CR22","doi-asserted-by":"publisher","first-page":"101818","DOI":"10.1016\/j.jormas.2024.101818","volume":"125","author":"I Leblebicioglu Kurtulus","year":"2024","unstructured":"Leblebicioglu Kurtulus I, Lubbad M, Yilmaz OMD, et al. A robust deep learning model for the classification of dental implant brands. J Stomatol Oral Maxillofac Surg. 2024;125(5S1):101818. https:\/\/doi.org\/10.1016\/j.jormas.2024.101818","journal-title":"J Stomatol Oral Maxillofac Surg"},{"key":"6104_CR23","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile. 2015:1026\u20131034. https:\/\/doi.org\/10.1109\/ICCV.2015.123","DOI":"10.1109\/ICCV.2015.123"},{"issue":"6","key":"6104_CR24","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1016\/j.oooo.2020.08.024","volume":"131","author":"HA Khan","year":"2021","unstructured":"Khan HA, Haider MA, Ansari HA, et al. Automated feature detection in dental periapical radiographs by using deep learning. Oral Surg Oral Med Oral Pathol Oral Radiol. 2021;131(6):711\u201320. https:\/\/doi.org\/10.1016\/j.oooo.2020.08.024","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"issue":"3","key":"6104_CR25","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1007\/s00784-018-2566-z","volume":"23","author":"T Connert","year":"2019","unstructured":"Connert T, Truckenm\u00fcller M, ElAyouti A, et al. Changes in periapical status, quality of root fillings and estimated endodontic treatment need in a similar urban German population 20 years later. Clin Oral Investig. 2019;23(3):1373\u201382. https:\/\/doi.org\/10.1007\/s00784-018-2566-z","journal-title":"Clin Oral Investig"},{"issue":"4","key":"6104_CR26","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1111\/iej.13020","volume":"52","author":"C Kruse","year":"2019","unstructured":"Kruse C, Spin-Neto R, Evar Kraft DC, Vaeth M, Kirkevang LL. Diagnostic accuracy of cone beam computed tomography used for assessment of apical periodontitis: an ex vivo histopathological study on human cadavers. Int Endod J. 2019;52(4):439\u201350. https:\/\/doi.org\/10.1111\/iej.13020","journal-title":"Int Endod J"},{"key":"6104_CR27","doi-asserted-by":"publisher","unstructured":"Ozsari S, Kamburo\u011flu K, Tamse A, et al. Automatic vertical root fracture detection on intraoral periapical radiographs with artificial Intelligence-Based image enhancement. Dent Traumatol Published Online January. 2025;19. https:\/\/doi.org\/10.1111\/edt.13027","DOI":"10.1111\/edt.13027"},{"key":"6104_CR28","doi-asserted-by":"publisher","unstructured":"Cejudo JE, Chaurasia A, Feldberg B, Krois J, Schwendicke F. Classification of Dental Radiographs Using Deep Learning. J Clin Med. 2021;10(7):1496. Published 2021 Apr 3. https:\/\/doi.org\/10.3390\/jcm10071496","DOI":"10.3390\/jcm10071496"},{"key":"6104_CR29","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/7035367","author":"IS Bayrakdar","year":"2022","unstructured":"Bayrakdar IS, Orhan K, \u00c7elik \u00d6, et al. A U-Net approach to apical lesion segmentation on panoramic radiographs. Biomed Res Int. 2022. https:\/\/doi.org\/10.1155\/2022\/7035367. 2022:7035367. Published 2022 Jan 15.","journal-title":"Biomed Res Int"},{"issue":"4","key":"6104_CR30","doi-asserted-by":"publisher","first-page":"351","DOI":"10.5624\/isd.20220078","volume":"52","author":"IS Song","year":"2022","unstructured":"Song IS, Shin HK, Kang JH, et al. Deep learning-based apical lesion segmentation from panoramic radiographs. Imaging Sci Dent. 2022;52(4):351\u20137. https:\/\/doi.org\/10.5624\/isd.20220078","journal-title":"Imaging Sci Dent"}],"container-title":["BMC Oral Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12903-025-06104-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12903-025-06104-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12903-025-06104-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T16:09:29Z","timestamp":1748275769000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcoralhealth.biomedcentral.com\/articles\/10.1186\/s12903-025-06104-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,26]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["6104"],"URL":"https:\/\/doi.org\/10.1186\/s12903-025-06104-0","relation":{},"ISSN":["1472-6831"],"issn-type":[{"value":"1472-6831","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,26]]},"assertion":[{"value":"26 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2025","order":3,"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 approved by the Jinan Stomatological Hospital Ethics Committee for research involving human subjects (approval number: JNSKQYY-2022-023). All patients involved in the PRs provided informed consent, and the experimental procedures were strictly conducted in accordance with the principles and regulations outlined in the Declaration of Helsinki. Additionally, the three novice dentists provided their signed informed consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All participants involved in the periapical radiographs used in this study provided their informed consent. All images and data presented in the manuscript are anonymized to protect patient identities. All participants submitted consent for publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"801"}}