Mouncif Hamza, Kassimi Amine, Bertin Gardelle Thierry, Tairi Hamid, Riffi Jamal
LISAC Laboratory, Department of Informatics, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
3D Smart Factory, Mohamadia, Morocco.
BMC Oral Health. 2025 Apr 30;25(1):665. doi: 10.1186/s12903-025-06017-y.
The classification of intraoral teeth structures is a critical component in modern dental analysis and forensic dentistry. Traditional methods, relying on 2D imaging, often suffer from limitations in accuracy and comprehensiveness due to the complex three-dimensional (3D) nature of dental anatomy. Although 3D imaging introduces the third dimension, offering a more comprehensive view, it also introduces additional challenges due to the irregular nature of the data. Our proposed approach addresses these issues with a novel method that extracts critical representative features from 3D tooth models and transforms them into a 2D image format suitable for detailed analysis. The 2D images are subsequently processed using a recurrent neural network (RNN) architecture, which effectively detects complex patterns essential for accurate classification, while its capability to manage sequential data is further augmented by fully connected layers specifically designed for this purpose. This innovative approach improves accuracy and diagnostic efficiency by reducing manual analysis and speeding up processing time, overcoming the challenges of 3D data irregularity and leveraging its detailed representation, thereby setting a new standard in dental identification.
口腔内牙齿结构的分类是现代牙科分析和法医牙科学的关键组成部分。传统方法依赖二维成像，由于牙齿解剖结构复杂的三维性质，在准确性和全面性方面常常存在局限性。尽管三维成像引入了第三维度，能提供更全面的视图，但由于数据的不规则性，也带来了额外的挑战。我们提出的方法通过一种新颖的方式解决了这些问题，该方法从三维牙齿模型中提取关键的代表性特征，并将其转换为适合详细分析的二维图像格式。随后，使用循环神经网络（RNN）架构对二维图像进行处理，该架构能有效检测出准确分类所需的复杂模式，而专门为此设计的全连接层进一步增强了其处理序列数据（的能力）。这种创新方法通过减少人工分析并加快处理时间，提高了准确性和诊断效率，克服了三维数据不规则性的挑战，并利用了其详细的表示，从而在牙齿识别方面树立了新的标准。
