Treffer: Comparative Analysis of AI-Generated and Manually Designed Approaches in Accuracy and Design Time for Surgical Path Planning of Dynamic Navigation-Aided Endodontic Microsurgery: A Retrospective Study.
Title:
Comparative Analysis of AI-Generated and Manually Designed Approaches in Accuracy and Design Time for Surgical Path Planning of Dynamic Navigation-Aided Endodontic Microsurgery: A Retrospective Study.
Authors:
Chen C; State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine, Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China.; Department of Cariology and Endodontics, School & Hospital of Stomatology, Wuhan University, Wuhan, China., Zhang X; State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine, Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China.; Department of Cariology and Endodontics, School & Hospital of Stomatology, Wuhan University, Wuhan, China., Qin L; State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine, Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China.; Department of Cariology and Endodontics, School & Hospital of Stomatology, Wuhan University, Wuhan, China., Meng L; State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine, Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China.; Department of Cariology and Endodontics, School & Hospital of Stomatology, Wuhan University, Wuhan, China.
Source:
International endodontic journal [Int Endod J] 2026 Feb; Vol. 59 (2), pp. 288-298. Date of Electronic Publication: 2025 Oct 01.
Publication Type:
Journal Article; Comparative Study
Language:
English
Journal Info:
Publisher: Blackwell Scientific Publications Country of Publication: England NLM ID: 8004996 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1365-2591 (Electronic) Linking ISSN: 01432885 NLM ISO Abbreviation: Int Endod J Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford, Blackwell Scientific Publications.
MeSH Terms:
References:
Alahmari, M., M. Alahmari, A. Almuaddi, et al. 2025. “Accuracy of Artificial Intelligence‐Based Segmentation in Maxillofacial Structures: A Systematic Review.” BMC Oral Health 25: 350.
Baldini, B., D. Papasratorn, F. B. Fagundes, R. C. Fontenele, and R. Jacobs. 2025. “Validation of a Novel Tool for Automated Tooth Modelling by Fusion of CBCT‐Derived Roots With the Respective IOS‐Derived Crowns.” Journal of Dentistry 153: 105546.
Chakravorty, S., B. K. Aulakh, M. Shil, M. Nepale, R. Puthenkandathil, and W. Syed. 2024. “Role of Artificial Intelligence (AI) in Dentistry: A Literature Review.” Journal of Pharmacy & Bioallied Sciences 16: S14–S16.
Chen, C., R. Zhang, W. Zhang, et al. 2023a. “Clinical and Radiological Outcomes of Dynamic Navigation in Endodontic Microsurgery: A Prospective Study.” Clinical Oral Investigations 27: 5317–5329.
Chen, C., R. Zhang, W. Zhang, et al. 2023b. “Analysis of the Accuracy of a Dynamic Navigation System in Endodontic Microsurgery: A Prospective Case Series Study.” Journal of Dentistry 134: 104534.
Chen, H., K. L. Zhang, P. J. Lyu, et al. 2019. “A Deep Learning Approach to Automatic Teeth Detection and Numbering Based on Object Detection in Dental Periapical Films.” Scientific Reports 9: 3840.
Cho, J. H., G. Çakmak, J. Choi, et al. 2024. “Deep Learning‐Designed Implant‐Supported Posterior Crowns: Assessing Time Efficiency, Tooth Morphology, Emergence Profile, Occlusion, and Proximal Contacts.” Journal of Dentistry 147: 105142.
Du, W., W. J. Bi, Y. Liu, Z. K. Zhu, Y. Tai, and E. Luo. 2024. “Machine Learning‐Based Decision Support System for Orthognathic Diagnosis and Treatment Planning.” BMC Oral Health 24: 286.
Duan, W., Y. F. Chen, Q. Zhang, X. Lin, and X. Y. Yang. 2021. “Refined Tooth and Pulp Segmentation Using U‐Net in CBCT Image.” Dento Maxillo Facial Radiology 50: 20200251.
Elgarba, B. M., R. C. Fontenele, F. Mangano, and R. Jacobs. 2024. “Novel AI‐Based Automated Virtual Implant Placement: Artificial Versus Human Intelligence.” Journal of Dentistry 147: 105146.
Elgarba, B. M., R. C. Fontenele, M. Tarce, and R. Jacobs. 2024. “Artificial Intelligence Serving Pre‐Surgical Digital Implant Planning: A Scoping Review.” Journal of Dentistry 143: 104862.
Elgarba, B. M., S. Van Aelst, A. Swaity, N. Morgan, S. Shujaat, and R. Jacobs. 2023. “Deep Learning‐Based Segmentation of Dental Implants on Cone‐Beam Computed Tomography Images: A Validation Study.” Journal of Dentistry 137: 104639.
Fontenele, R. C., M. D. Gerhardt, F. F. Picoli, et al. 2023. “Convolutional Neural Network‐Based Automated Maxillary Alveolar Bone Segmentation on Cone‐Beam Computed Tomography Images.” Clinical Oral Implants Research 34: 565–574.
Fu, W. T., Q. K. Zhu, N. Li, et al. 2024. “Clinically Oriented CBCT Periapical Lesion Evaluation via 3D CNN Algorithm.” Journal of Dental Research 103: 5–12.
Hadj Saïd, M., M.‐K. Le Roux, J.‐H. Catherine, and R. Lan. 2020. “Development of an Artificial Intelligence Model to Identify a Dental Implant From a Radiograph.” International Journal of Oral & Maxillofacial Implants 35: 1077–1082.
Hiraiwa, T., Y. Ariji, M. Fukuda, et al. 2019. “A Deep‐Learning Artificial Intelligence System for Assessment of Root Morphology of the Mandibular First Molar on Panoramic Radiography.” Dento Maxillo Facial Radiology 48: 20180218.
Huang, X. X., J. Xu, B. X. Hou, and Y. Wang. 2025. “Proximity of Maxillary Molar Palatal Roots to Adjacent Structures for Endodontic Microsurgery: A Cone‐Beam Computed Tomography Study.” BMC Oral Health 25: 21.
Iqbal, A., T. AL Sharari, O. Khattak, et al. 2023. “Guided Endodontic Surgery: A Narrative Review.” Medicina 59: 678.
Kim, S., and S. Kratchman. 2006. “Modern Endodontic Surgery Concepts and Practice: A Review.” Journal of Endodontics 32: 601–623.
Lahoud, P., M. Ezeldeen, T. Beznik, et al. 2021. “Artificial Intelligence for Fast and Accurate 3‐Dimensional Tooth Segmentation on Cone‐Beam Computed Tomography.” Journal of Endodontics 47: 827–835.
Lee, J., H. Seo, Y. J. Choi, et al. 2023. “An Endodontic Forecasting Model Based on the Analysis of Preoperative Dental Radiographs: A Pilot Study on an Endodontic Predictive Deep Neural Network.” Journal of Endodontics 49: 710–719.
Li, Y. H., Y. L. Li, M. Y. Wei, and G. Y. Li. 2024. “Innovation and Challenges of Artificial Intelligence Technology in Personalized Healthcare.” Scientific Reports 14: 18994.
Liu, Z. K., C. W. Xu, Z. K. Zhu, Y. Liu, and E. Luo. 2024. “Design and Accuracy Evaluation of AI Orthognathic Surgery Plates.” Journal of Oral and Maxillofacial Surgery 34: 115–121.
Mangano, F. G., O. Admakin, H. Lerner, and C. Mangano. 2023. “Artificial Intelligence and Augmented Reality for Guided Implant Surgery Planning: A Proof of Concept.” Journal of Dentistry 133: 104485.
Martinho, F. C., S. A. Aldahmash, T. Y. Cahill, et al. 2022. “Comparison of the Accuracy and Efficiency of a 3‐Dimensional Dynamic Navigation System for Osteotomy and Root‐End Resection Performed by Novice and Experienced Endodontists.” Journal of Endodontics 48: 1327–1333. e1.
Mohammad‐Rahimi, H., O. Dianat, R. Abbasi, et al. 2024. “Artificial Intelligence for Detection of External Cervical Resorption Using Label‐Efficient Self‐Supervised Learning Method.” Journal of Endodontics 50: 144–153. e2.
Nogueira‐Reis, F., N. Morgan, I. R. Suryani, C. P. M. Tabchoury, and R. Jacobs. 2024. “Full Virtual Patient Generated by Artificial Intelligence‐Driven Integrated Segmentation of Craniomaxillofacial Structures From CBCT Images.” Journal of Dentistry 141: 104829.
Preda, F., F. Nogueira‐Reis, E. M. Stanciu, A. Smolders, R. Jacobs, and E. Shaheen. 2024. “Validation of Automated Registration of Intraoral Scan Onto Cone Beam Computed Tomography for an Efficient Digital Dental Workflow.” Journal of Dentistry 149: 105282.
Santos, A. J. R., R. C. Fontenele, F. S. Neves, S. Ali, R. Jacobs, and M. Tanomaru. 2025. “A Unique AI‐Based Tool for Automated Segmentation of Pulp Cavity Structures in Maxillary Premolars on CBCT.” Scientific Reports 15: 5509.
Setzer, F. C., and S. Kratchman. 2022. “Present Status and Future Directions: Surgical Endodontics.” International Endodontic Journal 55: 1020–1058.
Shi, C., Q. Yang, Y. T. Wang, et al. 2025. “Automatic Path Planning for Pelvic Fracture Reduction With Multi‐Degree‐Of‐Freedom.” Computer Methods and Programs in Biomedicine 261: 108591.
Sreedevi, P., N. Varghese, and J. Varugheese. 2011. “Prognosis of Periapical Surgery Using Bonegrafts: A Clinical Study.” Journal of Conservative Dentistry 14: 68–72.
Theodosiou, A. A., and R. C. Read. 2023. “Artificial Intelligence, Machine Learning and Deep Learning: Potential Resources for the Infection Clinician.” Journal of Infection 87: 287–294.
Too, C. W., K. Y. Fong, G. Hang, et al. 2024. “Artificial Intelligence‐Guided Segmentation and Path Planning Software for Transthoracic Lung Biopsy.” Journal of Vascular and Interventional Radiology 35: 780–789.e1.
Vasudevan, A., S. S. Santosh, R. J. Selvakumar, D. T. Sampath, and V. Natanasabapathy. 2022. “Dynamic Navigation in Guided Endodontics – A Systematic Review.” European Endodontic Journal 7: 81–91.
Vicory, J., R. Chandradevan, P. Hernandez‐Cerdan, et al. 2021. “Dental Microfracture Detection Using Wavelet Features and Machine Learning.” Proceedings of SPIE: The International Society for Optical Engineering 11596: 115961R.
Wang, C., S. Jiang, Z. Li, et al. 2024. “Accuracy and Safety Evaluation of a Novel Artificial Intelligence‐Based Robotic System for Autonomous Spinal Posterior Decompression.” Neurosurgical Focus 57: E16.
Wang, H. G., X. Xu, Z. Bian, et al. 2025. “Expert Consensus on Apical Microsurgery.” International Journal of Oral Science 17: 2.
Wang, L., J. P. Li, Z. P. Ge, and G. Li. 2019. “CBCT Image Based Segmentation Method for Tooth Pulp Cavity Region Extraction.” Dento Maxillo Facial Radiology 48: 20180236.
Wang, Y. W., W. J. Xia, Z. N. Yan, et al. 2023. “Root Canal Treatment Planning by Automatic Tooth and Root Canal Segmentation in Dental CBCT With Deep Multi‐Task Feature Learning.” Medical Image Analysis 85: 102750.
Wu, Z., X. B. Yu, F. Wang, and C. Xu. 2024. “Application of Artificial Intelligence in Dental Implant Prognosis: A Scoping Review.” Journal of Dentistry 144: 104924.
Yu, Z. C., Q. F. Zhao, Z. S. Tang, and W. J. Xia. 2020. “CBCT Image Segmentation of Tooth‐Root Canal Based on Improved Level Set Algorithm.” Proceedings of the 2020 International Conference on Computers, Information Processing and Advanced Education.
Baldini, B., D. Papasratorn, F. B. Fagundes, R. C. Fontenele, and R. Jacobs. 2025. “Validation of a Novel Tool for Automated Tooth Modelling by Fusion of CBCT‐Derived Roots With the Respective IOS‐Derived Crowns.” Journal of Dentistry 153: 105546.
Chakravorty, S., B. K. Aulakh, M. Shil, M. Nepale, R. Puthenkandathil, and W. Syed. 2024. “Role of Artificial Intelligence (AI) in Dentistry: A Literature Review.” Journal of Pharmacy & Bioallied Sciences 16: S14–S16.
Chen, C., R. Zhang, W. Zhang, et al. 2023a. “Clinical and Radiological Outcomes of Dynamic Navigation in Endodontic Microsurgery: A Prospective Study.” Clinical Oral Investigations 27: 5317–5329.
Chen, C., R. Zhang, W. Zhang, et al. 2023b. “Analysis of the Accuracy of a Dynamic Navigation System in Endodontic Microsurgery: A Prospective Case Series Study.” Journal of Dentistry 134: 104534.
Chen, H., K. L. Zhang, P. J. Lyu, et al. 2019. “A Deep Learning Approach to Automatic Teeth Detection and Numbering Based on Object Detection in Dental Periapical Films.” Scientific Reports 9: 3840.
Cho, J. H., G. Çakmak, J. Choi, et al. 2024. “Deep Learning‐Designed Implant‐Supported Posterior Crowns: Assessing Time Efficiency, Tooth Morphology, Emergence Profile, Occlusion, and Proximal Contacts.” Journal of Dentistry 147: 105142.
Du, W., W. J. Bi, Y. Liu, Z. K. Zhu, Y. Tai, and E. Luo. 2024. “Machine Learning‐Based Decision Support System for Orthognathic Diagnosis and Treatment Planning.” BMC Oral Health 24: 286.
Duan, W., Y. F. Chen, Q. Zhang, X. Lin, and X. Y. Yang. 2021. “Refined Tooth and Pulp Segmentation Using U‐Net in CBCT Image.” Dento Maxillo Facial Radiology 50: 20200251.
Elgarba, B. M., R. C. Fontenele, F. Mangano, and R. Jacobs. 2024. “Novel AI‐Based Automated Virtual Implant Placement: Artificial Versus Human Intelligence.” Journal of Dentistry 147: 105146.
Elgarba, B. M., R. C. Fontenele, M. Tarce, and R. Jacobs. 2024. “Artificial Intelligence Serving Pre‐Surgical Digital Implant Planning: A Scoping Review.” Journal of Dentistry 143: 104862.
Elgarba, B. M., S. Van Aelst, A. Swaity, N. Morgan, S. Shujaat, and R. Jacobs. 2023. “Deep Learning‐Based Segmentation of Dental Implants on Cone‐Beam Computed Tomography Images: A Validation Study.” Journal of Dentistry 137: 104639.
Fontenele, R. C., M. D. Gerhardt, F. F. Picoli, et al. 2023. “Convolutional Neural Network‐Based Automated Maxillary Alveolar Bone Segmentation on Cone‐Beam Computed Tomography Images.” Clinical Oral Implants Research 34: 565–574.
Fu, W. T., Q. K. Zhu, N. Li, et al. 2024. “Clinically Oriented CBCT Periapical Lesion Evaluation via 3D CNN Algorithm.” Journal of Dental Research 103: 5–12.
Hadj Saïd, M., M.‐K. Le Roux, J.‐H. Catherine, and R. Lan. 2020. “Development of an Artificial Intelligence Model to Identify a Dental Implant From a Radiograph.” International Journal of Oral & Maxillofacial Implants 35: 1077–1082.
Hiraiwa, T., Y. Ariji, M. Fukuda, et al. 2019. “A Deep‐Learning Artificial Intelligence System for Assessment of Root Morphology of the Mandibular First Molar on Panoramic Radiography.” Dento Maxillo Facial Radiology 48: 20180218.
Huang, X. X., J. Xu, B. X. Hou, and Y. Wang. 2025. “Proximity of Maxillary Molar Palatal Roots to Adjacent Structures for Endodontic Microsurgery: A Cone‐Beam Computed Tomography Study.” BMC Oral Health 25: 21.
Iqbal, A., T. AL Sharari, O. Khattak, et al. 2023. “Guided Endodontic Surgery: A Narrative Review.” Medicina 59: 678.
Kim, S., and S. Kratchman. 2006. “Modern Endodontic Surgery Concepts and Practice: A Review.” Journal of Endodontics 32: 601–623.
Lahoud, P., M. Ezeldeen, T. Beznik, et al. 2021. “Artificial Intelligence for Fast and Accurate 3‐Dimensional Tooth Segmentation on Cone‐Beam Computed Tomography.” Journal of Endodontics 47: 827–835.
Lee, J., H. Seo, Y. J. Choi, et al. 2023. “An Endodontic Forecasting Model Based on the Analysis of Preoperative Dental Radiographs: A Pilot Study on an Endodontic Predictive Deep Neural Network.” Journal of Endodontics 49: 710–719.
Li, Y. H., Y. L. Li, M. Y. Wei, and G. Y. Li. 2024. “Innovation and Challenges of Artificial Intelligence Technology in Personalized Healthcare.” Scientific Reports 14: 18994.
Liu, Z. K., C. W. Xu, Z. K. Zhu, Y. Liu, and E. Luo. 2024. “Design and Accuracy Evaluation of AI Orthognathic Surgery Plates.” Journal of Oral and Maxillofacial Surgery 34: 115–121.
Mangano, F. G., O. Admakin, H. Lerner, and C. Mangano. 2023. “Artificial Intelligence and Augmented Reality for Guided Implant Surgery Planning: A Proof of Concept.” Journal of Dentistry 133: 104485.
Martinho, F. C., S. A. Aldahmash, T. Y. Cahill, et al. 2022. “Comparison of the Accuracy and Efficiency of a 3‐Dimensional Dynamic Navigation System for Osteotomy and Root‐End Resection Performed by Novice and Experienced Endodontists.” Journal of Endodontics 48: 1327–1333. e1.
Mohammad‐Rahimi, H., O. Dianat, R. Abbasi, et al. 2024. “Artificial Intelligence for Detection of External Cervical Resorption Using Label‐Efficient Self‐Supervised Learning Method.” Journal of Endodontics 50: 144–153. e2.
Nogueira‐Reis, F., N. Morgan, I. R. Suryani, C. P. M. Tabchoury, and R. Jacobs. 2024. “Full Virtual Patient Generated by Artificial Intelligence‐Driven Integrated Segmentation of Craniomaxillofacial Structures From CBCT Images.” Journal of Dentistry 141: 104829.
Preda, F., F. Nogueira‐Reis, E. M. Stanciu, A. Smolders, R. Jacobs, and E. Shaheen. 2024. “Validation of Automated Registration of Intraoral Scan Onto Cone Beam Computed Tomography for an Efficient Digital Dental Workflow.” Journal of Dentistry 149: 105282.
Santos, A. J. R., R. C. Fontenele, F. S. Neves, S. Ali, R. Jacobs, and M. Tanomaru. 2025. “A Unique AI‐Based Tool for Automated Segmentation of Pulp Cavity Structures in Maxillary Premolars on CBCT.” Scientific Reports 15: 5509.
Setzer, F. C., and S. Kratchman. 2022. “Present Status and Future Directions: Surgical Endodontics.” International Endodontic Journal 55: 1020–1058.
Shi, C., Q. Yang, Y. T. Wang, et al. 2025. “Automatic Path Planning for Pelvic Fracture Reduction With Multi‐Degree‐Of‐Freedom.” Computer Methods and Programs in Biomedicine 261: 108591.
Sreedevi, P., N. Varghese, and J. Varugheese. 2011. “Prognosis of Periapical Surgery Using Bonegrafts: A Clinical Study.” Journal of Conservative Dentistry 14: 68–72.
Theodosiou, A. A., and R. C. Read. 2023. “Artificial Intelligence, Machine Learning and Deep Learning: Potential Resources for the Infection Clinician.” Journal of Infection 87: 287–294.
Too, C. W., K. Y. Fong, G. Hang, et al. 2024. “Artificial Intelligence‐Guided Segmentation and Path Planning Software for Transthoracic Lung Biopsy.” Journal of Vascular and Interventional Radiology 35: 780–789.e1.
Vasudevan, A., S. S. Santosh, R. J. Selvakumar, D. T. Sampath, and V. Natanasabapathy. 2022. “Dynamic Navigation in Guided Endodontics – A Systematic Review.” European Endodontic Journal 7: 81–91.
Vicory, J., R. Chandradevan, P. Hernandez‐Cerdan, et al. 2021. “Dental Microfracture Detection Using Wavelet Features and Machine Learning.” Proceedings of SPIE: The International Society for Optical Engineering 11596: 115961R.
Wang, C., S. Jiang, Z. Li, et al. 2024. “Accuracy and Safety Evaluation of a Novel Artificial Intelligence‐Based Robotic System for Autonomous Spinal Posterior Decompression.” Neurosurgical Focus 57: E16.
Wang, H. G., X. Xu, Z. Bian, et al. 2025. “Expert Consensus on Apical Microsurgery.” International Journal of Oral Science 17: 2.
Wang, L., J. P. Li, Z. P. Ge, and G. Li. 2019. “CBCT Image Based Segmentation Method for Tooth Pulp Cavity Region Extraction.” Dento Maxillo Facial Radiology 48: 20180236.
Wang, Y. W., W. J. Xia, Z. N. Yan, et al. 2023. “Root Canal Treatment Planning by Automatic Tooth and Root Canal Segmentation in Dental CBCT With Deep Multi‐Task Feature Learning.” Medical Image Analysis 85: 102750.
Wu, Z., X. B. Yu, F. Wang, and C. Xu. 2024. “Application of Artificial Intelligence in Dental Implant Prognosis: A Scoping Review.” Journal of Dentistry 144: 104924.
Yu, Z. C., Q. F. Zhao, Z. S. Tang, and W. J. Xia. 2020. “CBCT Image Segmentation of Tooth‐Root Canal Based on Improved Level Set Algorithm.” Proceedings of the 2020 International Conference on Computers, Information Processing and Advanced Education.
Grant Information:
82571079 the General Program of National Natural Science Foundation of China; 2042024YXA009 Fundamental Research Funds for the Central Universities; 2022020801010498 Wuhan Special Project on Knowledge Innovation; 2023BCB134 Key R&D projects of Hubei Provincial Science and Technology Plan; ZW202404 Research Project of School and Hospital of Stomatology Wuhan University
Contributed Indexing:
Keywords: accuracy; artificial intelligence; design time; dynamic navigation; endodontic microsurgery; guided endodontics
Entry Date(s):
Date Created: 20251002 Date Completed: 20260112 Latest Revision: 20260112
Update Code:
20260112
DOI:
10.1111/iej.70045
PMID:
41035305
Database:
MEDLINE
Weitere Informationen
Aim: To compare the accuracy and design time of artificial intelligence (AI)-generated and manually designed (MD) surgical pathways for osteotomies and root-end resections in dynamic navigation (DN)-aided endodontic microsurgery (EMS).
Methodology: Fifty-one surgical pathways were analysed, each planned using both AI and MD methodologies. Accuracy was assessed using the DCARE Navigation System (v3.2, MedNav Ltd) and AutoCAD (2023, Autodesk Inc.), evaluating five parameters: start deviation, end deviation, angular deviation, root-end resection length deviation, and root-end resection angulation deviation. Design time was measured from the point of CBCT dataset import to the finalisation of the surgical pathway design. Mann-Whitney U test was used to compare the accuracy and design time of the AI and MD groups, whereas the rank-based ANCOVA was used to assess deviations according to tooth type, jaw type, and root number. Statistical significance was set at p < 0.05.
Results: Compared with the MD group, the AI group exhibited significantly smaller root-end resection length deviations (AI: 0.01 [0.01, 0.02] mm; MD: 0.02 [0.01, 0.03] mm; p = 0.029) but significantly larger root-end resection angulation deviations (AI: 3.48 [1.01, 7.48]; MD: 0.35 [0.16, 0.73]; p < 0.001). There were no significant differences in the start deviation, end deviation, angular deviation, root-end resection length deviation, or root-end resection angulation deviation across tooth type, jaw type, or root number. The design time was significantly shorter in the AI group than in the MD group (55 [21, 74] s vs. 379 [215, 553] s; p < 0.001).
Conclusions: A clinically operational AI-based surgical path design approach is capable of minimising manual interventions and delivering time-efficient, accurate results for clinical use. The integration of AI with DN-aided EMS may contribute to the development of increasingly autonomous surgical procedures.
(© 2025 British Endodontic Society. Published by John Wiley & Sons Ltd.)