Computerised assessment-a novel approach for calculation of percentage of hypomineralized lesion on incisors and its correlation with aesthetic concern

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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 ADOBE PHOTOSHOP CS7.

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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计算机评估--计算门牙低矿化病变百分比及其与美观相关性的新方法
导言臼齿-切牙低矿化(MIH)是一种影响第一恒磨牙(FPM)和/或恒切牙的局部、定性、分界釉质缺损。本研究旨在引入一种新的计算机化评估程序,以检测和量化受MIH影响的上颌中切牙的相关不透明百分比。在人工照明的房间内,以固定参数拍摄 50 张标准化的 MIH 病变图像,并进行匿名和安全存储。图像由人工智能驱动的计算机软件进行分析,并通过监督机器学习(SML),利用精心注释的图像数据集作为参考,通过复杂的算法生成输出分类。结果通过直方图计算出 MIH 病变的百分比,最大值为 7.29 % 至 71.21 %,平均值为 34.51 %。验证得分范围为 10.29 % 至 67.27 %,平均值为 35.32 %。两者之间的差异在统计学上并不显著。在 50 名患者中,11 名患者的牙面 1-30% 受 MIH 影响,2 名患者有美学问题;24 名患者的牙面 30-60% 受 MIH 影响,13 名患者有美学问题;15 名患者的牙面 60% 受 MIH 影响,12 名患者有美学问题。
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来源期刊
CiteScore
4.90
自引率
0.00%
发文量
133
审稿时长
167 days
期刊介绍: Journal of Oral Biology and Craniofacial Research (JOBCR)is the official journal of the Craniofacial Research Foundation (CRF). The journal aims to provide a common platform for both clinical and translational research and to promote interdisciplinary sciences in craniofacial region. JOBCR publishes content that includes diseases, injuries and defects in the head, neck, face, jaws and the hard and soft tissues of the mouth and jaws and face region; diagnosis and medical management of diseases specific to the orofacial tissues and of oral manifestations of systemic diseases; studies on identifying populations at risk of oral disease or in need of specific care, and comparing regional, environmental, social, and access similarities and differences in dental care between populations; diseases of the mouth and related structures like salivary glands, temporomandibular joints, facial muscles and perioral skin; biomedical engineering, tissue engineering and stem cells. The journal publishes reviews, commentaries, peer-reviewed original research articles, short communication, and case reports.
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