ArticleJournal of oral biology and craniofacial research
Computerised assessment-a novel approach for calculation of percentage of hypomineralized lesion on incisors and its correlation with aesthetic concern.
Article in Journal of oral biology and craniofacial research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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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.
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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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Which diagnostic tools and criteria to use for diagnosis of HSPM and MIH? A systematic review.European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry · 2026Pooled it
- Artificial Intelligence Applications in Pediatric Dentistry: A Comprehensive Review.Biomimetics (Basel, Switzerland) · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Molar-incisor hypomineralization (MIH) is a localized, qualitative, demarcated enamel defect that affects first permanent molars (FPMs) and/or permanent incisors. The aim of present study was to introduce a novel computerised assessment process to detect and quantify the percentage opacity associated with MIH affected maxillary central incisors. Methodology: Children (8-16 years) enrolled in the primary study having mild (white/cream or yellow/brown) MIH lesion on fully erupted maxillary permanent central incisor. 50 standardised images of MIH lesions were captured in an artificially lit room with fixed parameters and were anonymized and securely stored. Images were analysed by AI-driven computerised software and generates output classifications via a sophisticated algorithm crafted using a meticulously annotated image dataset as reference through supervised machine learning (SML). For the validation of computerised assessment of MIH lesions, the percentage of demarked opacity was calculated using Results: The percentage of MIH lesion was calculated through histogram plotting with the maxima ranging from 7.29 % to 71.21 % with the mean value of 34.51 %. The validation score ranged from 10.29 % to 67.27 % with the mean value of 35.32 %. The difference between the two was statistically not significant. Out of 50 patients; 11 patients had 1-30 % of surface affected with MIH and 2 had aesthetic concern; 24 had 30-60 % of surface affected and 13 had aesthetic concern; 15 had >60 % of surface affected and 12 had aesthetic concerns. Conclusions: The proposed approach exhibit sufficient quality to be integrated into a dental software addressing practical challenges encountered in daily clinical settings.
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