ReviewEuropean journal of medical research2026
Artificial intelligence performance in maxillary canine impaction: a systematic review.
Review in European journal of medical research, 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
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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.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
objectivesThis systematic review evaluates the literature to determine the usefulness and rules of artificial intelligence (AI) in the diagnosis and prediction of maxillary canine impaction (MCI). MATERIALS AND
methodsThe search was performed through four electronic databases (PubMed, Scopus, and Web of Science, with Google Scholar). The protocol was registered at the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY202550102). Original English-language research articles utilizing AI in MCI were included. The diagnostic accuracy studies were evaluated using the QUADAS-2 tool, while the predictive studies were evaluated using the PROBASST tool.
resultsThe research strategy yielded seven articles deemed suitable for data extraction and quality assessment. Of these, five were diagnostic accuracy studies, and two were predictive studies. Overall, the studies demonstrated a low risk of bias. AI demonstrated high diagnostic performance, including improved detection accuracy (up to 98.3%), efficient segmentation (Dice similarity coefficient 0.99), and moderate-to-high predictive performance for three-dimensional (3D) reconstruction or root resorption classification. These findings highlight the potential of AI to enhance localization, diagnosis, and treatment planning of impacted maxillary canines.
conclusionsAcross the seven included studies, AI demonstrated high accuracy in localizing impacted maxillary canines and showed predictive capability for eruption outcomes. These findings suggest that AI can meaningfully support clinicians in diagnosis, treatment planning, and management of MCI. Further research is necessary to elucidate the clinical applicability of AI by standardizing methodologies, including larger samples for better validity and generalizability.
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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.