Previously submitted to: JMIR AI (no longer under consideration since Oct 02, 2025)
Date Submitted: Sep 27, 2025
Open Peer Review Period: Oct 1, 2025 - Oct 2, 2025
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Risk Prediction of Root Resorption Induced by Impacted Canine based on Deep Learning
ABSTRACT
Root resorption (RR) of adjacent teeth secondary to impacted maxillary canines represents a significant orthodontic challenge, characterized by high morbidity and compromised treatment outcomes. This study introduced the Mask RCNN-SA network, which integrating volumetric features, to overcome traditional algorithmic limitations in anatomical modeling and establish an interpretable RR risk prediction model. Using 4200 CBCT slices augmented 42 CBCT patients, we trained the model for multiclass segmentation of the maxilla, lateral incisors, canines, first premolars, and background. The CBCT scans segmented by the Mask RCNN-SA network demonstrated a large overlap with the gold standard segmentations. The Mask RCNN-SA network–based 3D models of the maxilla and the teeth showed minor surface deviations when compared with the corresponding gold standard 3D models. The Mask RCNN-SA framework reduced segmentation time compared with manual segmentation, confirming volumetric parameters as key biomechanical RR indicators for quantitative preventive strategies.
Citation
The author of this paper has made a PDF available, but requires the user to login, or create an account.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.