Volume 10, Issue 2 (6-2025)
J Res Dent Maxillofac Sci 2025, 10(2): 111-124 |
Back to browse issues page
Ethics code: IR.SBMU.DRC.REC.1402.102
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Vazirizadeh Y, Mirmohamadsadeghi H, Behnaz M, Kavousinejad S. Development and Evaluation of a Convolutional Neural Network for Automated Detection of Lip Separation on Profile and Frontal Photographs. J Res Dent Maxillofac Sci 2025; 10 (2) :111-124
URL: http://jrdms.dentaliau.ac.ir/article-1-751-en.html
URL: http://jrdms.dentaliau.ac.ir/article-1-751-en.html
1- 1-Department of Orthodontics, Faculty of Dentistry, Shahed University, Tehran, Iran. 2- Dentofacial Deformities Research Center, Research Institute for Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
2- Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
3- 1-Dentofacial Deformities Research Center, Research Institute for Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 2- Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran. ,dr.shahab.k93@gmail.com
2- Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
3- 1-Dentofacial Deformities Research Center, Research Institute for Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 2- Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran. ,
Abstract: (73 Views)
Background and Aim: Lip incompetence is defined as a habitual gap of more than 3-4 mm between the lips at rest, which can contribute to oral health issues and malocclusions. This study aimed to propose a deep learning-based model for automatic detection of lip separation on orthodontic photographs.
Materials and Methods: This retrospective observational study employed a balanced dataset of 800 clinical images, comprising 400 cases of lip incompetence and 400 cases of lip competence. An auto-cropping technique based on averaged manual cropping coordinates was used to isolate the lip region. The cropped images were resized to 70×70 pixels and normalized before feeding into a novel attention-based residual connection convolutional neural network (ARN-CNN). The model incorporated both residual connections and attention modules to enhance feature learning and training stability. Data augmentation (e.g., rotation and scaling) was applied to improve generalizability. Training was conducted using 5-fold cross-validation, with an external test set to evaluate performance and reduce overfitting. Metrics such as accuracy, precision, recall, F1 score, receiver-operating characteristic curve-area under the curve (ROC-AUC), and a confusion matrix were used for performance evaluation.
Results: The ARN-CNN achieved 95% accuracy on the test set. For the competent class, precision was 0.97, recall was 0.94, and F1 score was 0.96. These values were 0.94, 0.96, and 0.95, respectively, for the incompetent class with an AUC of 0.98.
Conclusion: The ARN-CNN model effectively identified lip incompetence, highlighting the potential of deep learning to support orthodontic diagnosis through image-based analysis.
Materials and Methods: This retrospective observational study employed a balanced dataset of 800 clinical images, comprising 400 cases of lip incompetence and 400 cases of lip competence. An auto-cropping technique based on averaged manual cropping coordinates was used to isolate the lip region. The cropped images were resized to 70×70 pixels and normalized before feeding into a novel attention-based residual connection convolutional neural network (ARN-CNN). The model incorporated both residual connections and attention modules to enhance feature learning and training stability. Data augmentation (e.g., rotation and scaling) was applied to improve generalizability. Training was conducted using 5-fold cross-validation, with an external test set to evaluate performance and reduce overfitting. Metrics such as accuracy, precision, recall, F1 score, receiver-operating characteristic curve-area under the curve (ROC-AUC), and a confusion matrix were used for performance evaluation.
Results: The ARN-CNN achieved 95% accuracy on the test set. For the competent class, precision was 0.97, recall was 0.94, and F1 score was 0.96. These values were 0.94, 0.96, and 0.95, respectively, for the incompetent class with an AUC of 0.98.
Conclusion: The ARN-CNN model effectively identified lip incompetence, highlighting the potential of deep learning to support orthodontic diagnosis through image-based analysis.
Keywords: Artificial Intelligence, Deep Learning, Malocclusion, Convolutional Neural Networks, Orthodontics
Type of Study: Original article |
Subject:
orthodontic
References
1. Kook MS, Jung S, Park HJ, Oh HK, Ryu SY, Cho JH, Lee JS, Yoon SJ, Kim MS, Shin HK. A comparison study of different facial soft tissue analysis methods. J Craniomaxillofac Surg. 2014 Jul;42(5):648-56. [DOI:10.1016/j.jcms.2013.09.010] [PMID]
2. Siécola GS, Capelozza L Filho, Lorenzoni DC, Janson G, Henriques JFC. Subjective facial analysis and its correlation with dental relationships. Dental Press J Orthod. 2017 Mar-Apr;22(2):87-94. [DOI:10.1590/2177-6709.22.2.087-094.oar] [PMID] []
3. Drevensek M, Stefanac-Papić J, Farcnik F. The influence of incompetent lip seal on the growth and development of craniofacial complex. Coll Antropol. 2005 Dec;29(2):429-34.
4. Leonardo SE, Sato Y, Kaneko T, Yamamoto T, Handa K, Iida J. Differences in dento-facial morphology in lip competence and lip incompetence. Orthod Waves. 2009 Mar;68(1):12-9. [DOI:10.1016/j.odw.2008.11.002]
5. Proffit WR, Fields H, Larson B, Sarver DM. Contemporary orthodontics. 5th ed. St. Louis: Elsevier; 2018.
6. Hassan AH, Turkistani AA, Hassan MH. Skeletal and dental characteristics of subjects with incompetent lips. Saudi Med J. 2014 Aug;35(8):849-54.
7. Siécola GS, Capelozza L Filho, Lorenzoni DC, Janson G, Henriques JFC. Subjective facial analysis and its correlation with dental relationships. Dental Press J Orthod. 2017 Mar-Apr;22(2):87-94. [DOI:10.1590/2177-6709.22.2.087-094.oar] [PMID] []
8. Zacharopoulos GV, Manios A, Kau CH, Velagrakis G, Tzanakakis GN, de Bree E. Anthropometric Analysis of the Face. J Craniofac Surg. 2016 Jan;27(1):e71-5. [DOI:10.1097/SCS.0000000000002231] [PMID]
9. Juerchott A, Saleem MA, Hilgenfeld T, Freudlsperger C, Zingler S, Lux CJ, Bendszus M, Heiland S. 3D cephalometric analysis using Magnetic Resonance Imaging: validation of accuracy and reproducibility. Sci Rep. 2018 Aug 29;8(1):13029. [DOI:10.1038/s41598-018-31384-8] [PMID] []
10. Ahmed M, Shaikh A, Fida M. Diagnostic validity of different cephalometric analyses for assessment of the sagittal skeletal pattern. Dental Press J Orthod. 2018 Sep-Oct;23(5):75-81. [DOI:10.1590/2177-6709.23.5.075-081.oar] [PMID] []
11. Sukhia RH, Nuruddin R, Azam SI, Fida M. Predicting the sagittal skeletal pattern using dental cast and facial profile photographs in children aged 9 to 14 years. J Pak Med Assoc. 2022 Nov;72(11):2198-203.
12. Monill-González A, Rovira-Calatayud L, d'Oliveira NG, Ustrell-Torrent JM. Artificial intelligence in orthodontics: Where are we now? A scoping review. Orthod Craniofac Res. 2021 Dec;24 Suppl 2:6-15. [DOI:10.1111/ocr.12517] [PMID]
13. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44. [DOI:10.1038/nature14539] [PMID]
14. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25.
15. Ryu J, Lee YS, Mo SP, Lim K, Jung SK, Kim TW. Application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos. BMC Oral Health. 2022 Oct 25;22(1):454. [DOI:10.1186/s12903-022-02466-x] [PMID] []
16. Ryu J, Kim YH, Kim TW, Jung SK. Evaluation of artificial intelligence model for crowding categorization and extraction diagnosis using intraoral photographs. Sci Rep. 2023 Mar 30;13(1):5177. [DOI:10.1038/s41598-023-32514-7] [PMID] []
17. Mohammad-Rahimi H, Nadimi M, Rohban MH, Shamsoddin E, Lee VY, Motamedian SR. Machine learning and orthodontics, current trends and the future opportunities: A scoping review. Am J Orthod Dentofacial Orthop. 2021 Aug;160(2):170-192.e4. [DOI:10.1016/j.ajodo.2021.02.013] [PMID]
18. Nordblom NF, Büttner M, Schwendicke F. Artificial Intelligence in Orthodontics: Critical Review. J Dent Res. 2024 Jun;103(6):577-84. [DOI:10.1177/00220345241235606] [PMID] []
19. Ahmad I. Digital dental photography. Part 7: extra-oral set-ups. Br Dent J. 2009 Aug 8;207(3):103-10. [DOI:10.1038/sj.bdj.2009.667] [PMID]
20. Samawi SS. Clinical Digital Photography in Orthodontics. Jordan Dental Journal. 2011;18(1).
21. Zuiderveld K. Contrast limited adaptive histogram equalization. In: Graphics Gems IV. 1994. p. 474-85. [DOI:10.1016/B978-0-12-336156-1.50061-6]
22. Alwakid G, Gouda W, Humayun M. Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement. Healthcare (Basel). 2023 Mar 15;11(6):863. [DOI:10.3390/healthcare11060863] [PMID] []
23. Chakraverti S, Agarwal P, Pattanayak HS, Chauhan SP, Chakraverti AK, Kumar M. De-noising the image using DBST-LCM-CLAHE: A deep learning approach. Multimed Tools Appl. 2024;83(4):11017-42. [DOI:10.1007/s11042-023-16016-2]
24. Hayati M, Muchtar K, Maulina N, Syamsuddin I, Elwirehardja GN, Pardamean B. Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning. Procedia Comput Sci. 2023;216:57-66. [DOI:10.1016/j.procs.2022.12.111]
25. Kavousinejad S. An Attention-Based Residual Connection Convolutional Neural Network for Classification Tasks in Computer Vision. Journal of Dental School, Shahid Beheshti University of Medical Sciences. 2024 Jan 1;42(1):14-25.
26. Narkhede S. Understanding AUC-ROC curve. Towards Data Science. 2018;26(1):220-7.
27. Vovk V. The fundamental nature of the log loss function. Fields of logic and computation II: Essays dedicated to Yuri Gurevich on the Occasion of His 75th Birthday. 2015:307-18. [DOI:10.1007/978-3-319-23534-9_20]
28. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-cam: Visual explanations from deep networks via gradient-based localization. InProceedings of the IEEE international conference on computer vision 2017 (pp. 618-626). [DOI:10.1109/ICCV.2017.74]
29. Wang J, Khan MA, Wang S, Zhang Y. SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis. Comput Mater Contin. 2023 Aug 30;76(2):2201-2216. [DOI:10.32604/cmc.2023.041191] [PMID] []
30. Wig M, Kumar A, Chaluvaiah MB, Yadav V, Shyam R. Lip incompetence and traumatic dental injuries: a systematic review and meta-analysis. Evid Based Dent. 2022 Jul 11. [DOI:10.1038/s41432-022-0258-7] [PMID]
31. Patil S, Albogami S, Hosmani J, Mujoo S, Kamil MA, Mansour MA, Abdul HN, Bhandi S, Ahmed SSSJ. Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls. Diagnostics (Basel). 2022 Apr 19;12(5):1029. [DOI:10.3390/diagnostics12051029] [PMID] []
32. Al-Mahadeen B, AlTarawneh M, AlTarawneh IH. Signature region of interest using auto cropping. arXiv preprint arXiv:10043549. 2010.
33. Lugaresi C, Tang J, Nash H, C Clanahan C, Uboweja E, Hays M, et al. Mediapipe: a framework for building perception pipelines. arXiv preprint arXiv:190608172. 2019.
34. Yan J, Lin S, Bing Kang S, Tang X. Learning the change for automatic image cropping. InProceedings of the IEEE conference on computer vision and pattern recognition 2013 (pp. 971-978).Chang Q, Bai Y, Wang S, Wang F, Wang Y, Zuo F, Xie X. Automatic soft-tissue analysis on orthodontic frontal and lateral facial photographs based on deep learning. Orthod Craniofac Res. 2024 Jul 5.
35. Chang Q, Bai Y, Wang S, Wang F, Wang Y, Zuo F, Xie X. Automatic soft-tissue analysis on orthodontic frontal and lateral facial photographs based on deep learning. Orthod Craniofac Res. 2024 Dec;27(6):893-902. [DOI:10.1111/ocr.12830] [PMID]
36. Surendran A, Daigavane P, Shrivastav S, Kamble R, Sanchla AD, Bharti L, Shinde M. The Future of Orthodontics: Deep Learning Technologies. Cureus. 2024 Jun 10;16(6):e62045. [DOI:10.7759/cureus.62045]
37. Yu HJ, Cho SR, Kim MJ, Kim WH, Kim JW, Choi J. Automated Skeletal Classification with Lateral Cephalometry Based on Artificial Intelligence. J Dent Res. 2020 Mar;99(3):249-56. [DOI:10.1177/0022034520901715] [PMID]
38. Nakornnoi T, Chanmanee P. Accuracy of Digital Imaging Software to Predict Soft Tissue Changes during Orthodontic Treatment. J Imaging. 2024 May 31;10(6):134. [DOI:10.3390/jimaging10060134] [PMID] []
39. Jeong SH, Yun JP, Yeom HG, Lim HJ, Lee J, Kim BC. Deep learning based discrimination of soft tissue profiles requiring orthognathic surgery by facial photographs. Sci Rep. 2020 Oct 1;10(1):16235. [DOI:10.1038/s41598-020-73287-7] [PMID] []
40. Tanikawa C, Yamashiro T. Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients. Sci Rep. 2021 Aug 4;11(1):15853. [DOI:10.1038/s41598-021-95002-w] [PMID] []
41. Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, et al. Human-level control through deep reinforcement learning. Nature. 2015 Feb 26;518(7540):529-33. [DOI:10.1038/nature14236] [PMID]
42. Rousseau M, Retrouvey JM. Machine learning in orthodontics: Automated facial analysis of vertical dimension for increased precision and efficiency. Am J Orthod Dentofacial Orthop. 2022 Mar;161(3):445-50. [DOI:10.1016/j.ajodo.2021.03.017] [PMID]
43. Kavousinejad S, Ameli-Mazandarani Z, Behnaz M, Ebadifar A. A Deep Learning Framework for Automated Classification and Archiving of Orthodontic Diagnostic Documents. Cureus. 2024 Dec 28;16(12):e76530. [DOI:10.7759/cureus.76530] [PMID] []
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
