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Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction
Elham Tahsin Yasin, Mediha Erturk, Melek Taşsöker, Murat Köklü. Clinical Oral Investigations, 2025-03-25
Topic: Brain proteins, microglia and lab models
MobileNet achieved the highest overall performance, with an accuracy of 99.44%. Xception and DenseNet201 also demonstrated strong classification capabilities, with accuracies of 98.74% and 98.73%, respectively. These results highlight the potential of deep learning models to automate and improve the accuracy and consistency of mandibular third molars and the mandibular canal relationship classifications. CLINICAL RELEVANCE: The integration of such systems into clinical workflows could enhance surgical risk assessments, streamline diagnostics, and reduce reliance on manual analysis, particularly in resource-constrained settings. This study contributes to advancing the use of artificial intelligence in dental imaging, offering a promising avenue for safer and more efficient surgical planning.
Key findings
- MobileNet achieved the highest overall performance, with an accuracy of 99.44%.
- Xception and DenseNet201 also demonstrated strong classification capabilities, with accuracies of 98.74% and 98.73%, respectively.
- These results highlight the potential of deep learning models to automate and improve the accuracy and consistency of mandibular third molars and the mandibular canal relationship classifications.
- CLINICAL RELEVANCE: The integration of such systems into clinical workflows could enhance surgical risk assessments, streamline diagnostics, and reduce reliance on manual analysis, particularly in resource-constrained settings.
- This study contributes to advancing the use of artificial intelligence in dental imaging, offering a promising avenue for safer and more efficient surgical planning.
Why it matters
This study is about using deep learning to classify the relationship between wisdom teeth and the mandibular canal before surgery, not about Alzheimer's disease, so it has no relevance to Alzheimer's treatment, cure or prevention. It reported high accuracy for the deep learning models tested.
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Study details
- Effect size99.44%; 98.74%; 98.73%
- Evidence strengthLow
How this study scored 41/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Clinical Oral Investigations) |
| Journal's citation record | 1 / 4 | Clinical Oral Investigations: journal h-index 139 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 14 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 4.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 12.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 14 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Murat Köklü: h-index 27 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Selçuk University: institution h-index 237 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40128451) |
| Free to read | 2 / 2 | Free to read |
| Total | 41 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1007/s00784-025-06285-6
- PubMed: 40128451
- Original source →
- Free full text: Clinical Oral Investigations →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Yasin, E. T., Erturk, M., Taşsöker, M., & Köklü, M. (2025). Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction. Clinical Oral Investigations. https://doi.org/10.1007/s00784-025-06285-6
Vancouver
Yasin ET, Erturk M, Taşsöker M, Köklü M. Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction. Clinical Oral Investigations. 2025. doi:10.1007/s00784-025-06285-6. PMID: 40128451.
BibTeX
@article{yasin2025-40128451,
author = {Yasin, Elham Tahsin and Erturk, Mediha and Taşsöker, Melek and Köklü, Murat},
title = {{Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction}},
journal = {Clinical Oral Investigations},
year = {2025},
doi = {10.1007/s00784-025-06285-6},
pmid = {40128451},
url = {https://doi.org/10.1007/s00784-025-06285-6}
}