Jaiswal Manojkumar, Mukhtar Umer, Shakya Kaushlesh Singh, Laddi Amit, Singha L Akash
A Unit of Pediatric and Preventive Dentistry, Oral Health Sciences Center, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, India.
J Oral Biol Craniofac Res. 2024 Sep-Oct;14(5):570-577. doi: 10.1016/j.jobcr.2024.07.004. Epub 2024 Jul 23.
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.
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 .
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.
The proposed approach exhibit sufficient quality to be integrated into a dental software addressing practical challenges encountered in daily clinical settings.
磨牙-切牙矿化不全(MIH)是一种局限性、定性的、边界清晰的釉质缺陷,影响第一恒磨牙(FPMs)和/或恒切牙。本研究的目的是引入一种新的计算机化评估方法,以检测和量化与MIH相关的上颌中切牙的不透明度百分比。
纳入初级研究的儿童(8-16岁),其完全萌出的上颌恒中切牙有轻度(白色/米色或黄色/棕色)MIH病变。在人工照明的房间里,以固定参数拍摄50张MIH病变的标准化图像,并进行匿名处理并安全存储。图像由人工智能驱动的计算机软件进行分析,并通过使用经过精心注释的图像数据集作为参考,通过监督机器学习(SML)精心设计的复杂算法生成输出分类。为了验证MIH病变的计算机化评估,使用……计算划定不透明度的百分比。
通过直方图绘制计算出MIH病变的百分比,最大值范围为7.29%至71.21%,平均值为34.51%。验证分数范围为10.29%至67.27%,平均值为35.32%。两者之间的差异在统计学上不显著。在50名患者中,11名患者有1-30%的表面受MIH影响,2名患者有美学方面的担忧;24名患者有30-60%的表面受影响,13名患者有美学方面的担忧;15名患者有>60%的表面受影响,12名患者有美学方面的担忧。
所提出的方法具有足够的质量,可以集成到牙科软件中,以应对日常临床环境中遇到的实际挑战。