SynthesisClinical and experimental dental research2026
Role of Artificial Intelligence in Cleft Lip and/or Cleft Palate in Diagnosis and Detection-An Umbrella Review.
Synthesis in Clinical and experimental dental 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
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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeEarly diagnosis and treatment are essential in managing congenital cleft conditions. Use of artificial intelligence (AI) in routine diagnosis can substantially improve management of chronic conditions; however, in the context of CL/P, evidence remains fragmented. Thus, an umbrella review was planned to explore the applications of AI-based diagnostic systems in managing orofacial clefts.
methodsPriori protocol was registered with PROSPERO. Five electronic databases were thoroughly searched (PubMed/MEDLINE, Scopus, Embase, Google Scholar, ScienceDirect), based on the pre-defined PICO framework. The data extraction form was designed in accordance with the Joanna Briggs Institute (JBI) guidelines and analyzed. Results were presented in the form of tables, supported by narrative summaries. Overlap assessment was conducted to avoid overemphasis and duplication of the overall results. Methodological robustness of the included studies was assessed using AMSTAR 2.0 tool.
resultsOf 395 initially retrieved articles, only three systematic reviews were included for this study. Overlap assessment indicated a high percentage of overlap among studies, with the corrected covered area to be 13.89%. Overall analysis revealed that AI models-DCNN (97% to 96%), RF (99% to 96%), and SVM (94% to 93%) demonstrated high accuracy in diagnosing orofacial clefts. Deep learning models were extensively used in diagnosing and predicting the orofacial cleft with accuracies across models ranging over 90%. Machine learning models also demonstrated good performance in identifying genetic risk. However, the lowest accuracies were demonstrated by the DesNet model (nearly 73% accuracy). Also, methodological robustness indicated a moderate level of confidence, suggesting limitations in the generalizability of the findings.
conclusionsFindings present the potential of AI models in diagnosing and detecting orofacial clefts. AI models can support the early diagnosis of orofacial clefts; however, this study highlights the need for more comprehensive research to determine how different AI models can improve early diagnosis and treatment outcomes. PROSPERO Registration Number: CRD420251125934.
Indexed as
Identifiers
What Socratic 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 Socratic graph.