Issa Julien, Chidiac Alexandre, Mozdziak Paul, Kempisty Bartosz, Dorocka-Bobkowska Barbara, Mehr Katarzyna, Dyszkiewicz-Konwińska Marta
Chair of Practical Clinical Dentistry, Department of Diagnostics, Poznan University of Medical Sciences, Bukowska 70, 60-812 Poznan, Poland.
Doctoral School, Poznań University of Medical Sciences, Bukowska 70, 60-812 Poznan, Poland.
Medicina (Kaunas). 2024 Dec 12;60(12):2048. doi: 10.3390/medicina60122048.
Intracranial calcifications, particularly within the falx cerebri, serve as crucial diagnostic markers ranging from benign accumulations to signs of severe pathologies. The falx cerebri, a dural fold that separates the cerebral hemispheres, presents challenges in visualization due to its low contrast in standard imaging techniques. Recent advancements in artificial intelligence (AI), particularly in machine learning and deep learning, have significantly transformed radiological diagnostics. This study aims to explore the application of AI in the segmentation and detection of falx cerebri calcifications using Cone-Beam Computed Tomography (CBCT) images through a comprehensive literature review and a detailed case report. The case report presents a 59-year-old patient diagnosed with falx cerebri calcifications whose CBCT images were analyzed using a cloud-based AI platform, demonstrating effectiveness in segmenting these calcifications, although challenges persist in distinguishing these from other cranial structures. A specific search strategy was employed to search electronic databases, yielding four studies exploring AI-based segmentation of the falx cerebri. The review detailed various AI models and their accuracy across different imaging modalities in identifying and segmenting falx cerebri calcifications, also highlighting the gap in publications in this area. In conclusion, further research is needed to improve AI-driven methods for accurately identifying and measuring intracranial calcifications. Advancing AI applications in radiology, particularly for detecting falx cerebri calcifications, could significantly enhance diagnostic precision, support disease monitoring, and inform treatment planning.
颅内钙化，尤其是大脑镰内的钙化，是重要的诊断标志物，范围从良性沉积到严重病变的征象。大脑镰是分隔大脑半球的硬脑膜皱襞，由于在标准成像技术中对比度较低，在可视化方面存在挑战。人工智能（AI）的最新进展，特别是机器学习和深度学习，显著改变了放射诊断学。本研究旨在通过全面的文献综述和详细的病例报告，探讨AI在使用锥形束计算机断层扫描（CBCT）图像对大脑镰钙化进行分割和检测中的应用。病例报告介绍了一名59岁被诊断为大脑镰钙化的患者，其CBCT图像使用基于云的AI平台进行分析，结果表明在分割这些钙化方面有效，尽管在将其与其他颅骨结构区分开来时仍存在挑战。采用了特定的搜索策略来检索电子数据库，产生了四项探索基于AI的大脑镰分割的研究。该综述详细介绍了各种AI模型及其在不同成像模式下识别和分割大脑镰钙化的准确性，同时也强调了该领域出版物中的差距。总之，需要进一步研究以改进AI驱动的方法，用于准确识别和测量颅内钙化。推进AI在放射学中的应用，特别是用于检测大脑镰钙化，可显著提高诊断精度，支持疾病监测并为治疗计划提供信息。
