SynthesisBMC oral health2026
Artificial intelligence for assessment of the relationship between the mandibular canal and third molars using CBCT and panoramic radiography: a systematic review.
Synthesis in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate assessment of the relationship between mandibular third molars and the mandibular canal is essential to reduce the risk of inferior alveolar nerve injury. Artificial intelligence (AI), including deep learning and convolutional neural network-based models, has been increasingly investigated using panoramic radiography and cone-beam computed tomography (CBCT) for automated diagnostic support in third molar assessment.
methodsA systematic review (SR) was conducted following the PECOS framework to answer the research question: "Can artificial intelligence determine the relationship between the mandibular canal and mandibular third molars?" Searches were performed in the PubMed, Scopus, CAPES Periodicals, LILACS, and Cochrane Library databases up to February 2026. Studies evaluating AI models for identifying or classifying the relationship between the mandibular canal and mandibular third molars were included. Due to methodological heterogeneity among studies, a qualitative synthesis was performed. After screening titles, abstracts, and full texts, 29 studies met the eligibility criteria, including 15 using panoramic radiographs, 9 using CBCT, and 5 evaluating both imaging modalities. Protocol registration was not performed.
resultsAI-based models demonstrated favourable diagnostic performance for identifying and classifying the spatial relationship between the mandibular canal and mandibular third molars. Reported accuracy values frequently ranged from 80% to 97%, while sensitivity and specificity commonly exceeded 85%. Area under the ROC curve (AUC) values ranged from 0.84 to 0.98 across studies, indicating strong discriminatory capacity of CNN-based models for canal segmentation, spatial classification, and risk assessment tasks using panoramic radiographs and CBCT images.
conclusionCurrent evidence suggests that AI may support radiographic interpretation and pre-surgical assessment of the relationship between the mandibular canal and mandibular third molars. However, methodological heterogeneity and limited external validation highlight the need for further standardized multicenter studies before broader clinical implementation.
Indexed as
Identifiers
42436515What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.