Michelutti Luca, Tel Alessandro, Robiony Massimo, Marini Lorenzo, Tognetto Daniele, Agosti Edoardo, Ius Tamara, Gagliano Caterina, Zeppieri Marco
Clinic of Maxillofacial Surgery, Head-Neck and NeuroScience Department, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy.
Department of Medicine, Surgery and Health Sciences, University of Trieste, 34127 Trieste, Italy.
Bioengineering (Basel). 2025 May 29;12(6):585. doi: 10.3390/bioengineering12060585.
The entry of artificial intelligence, in particular deep learning models, into the study of medical-clinical processes is revolutionizing the way of conceiving and seeing the future of medicine, offering new and promising perspectives in patient management. These models are proving to be excellent tools for the clinician through their great potential and capacity for processing clinical data, in particular radiological images. The processing and analysis of imaging data, such as CT scans or histological images, by these algorithms offers aid to clinicians for image segmentation and classification and to surgeons in the surgical planning of a delicate and complex operation. This study aims to analyze what the most frequently used models in the segmentation and classification of medical images are, to evaluate what the applications of these algorithms in maxillo-facial surgery are, and to explore what the future perspectives of the use of artificial intelligence in the processing of radiological data are, particularly in oncological fields. Future prospects are promising. Further development of deep learning algorithms capable of analyzing image sequences, integrating multimodal data, i.e., combining information from different sources, and developing human-machine interfaces to facilitate the integration of these tools with clinical reality are expected. In conclusion, these models have proven to be versatile and potentially effective tools on different types of data, from photographs of intraoral lesions to histopathological slides via MRI scans.
人工智能，尤其是深度学习模型，进入医学临床过程研究领域，正在彻底改变人们构想和看待医学未来的方式，为患者管理提供了新的、充满希望的视角。这些模型凭借其巨大潜力和处理临床数据（尤其是放射影像数据）的能力，正被证明是临床医生的优秀工具。这些算法对成像数据（如CT扫描或组织学图像）进行处理和分析，有助于临床医生进行图像分割和分类，也有助于外科医生进行精细复杂手术的手术规划。本研究旨在分析医学图像分割和分类中最常用的模型有哪些，评估这些算法在颌面外科中的应用，以及探索人工智能在放射学数据处理中的未来前景，尤其是在肿瘤学领域。未来前景广阔。预计能够分析图像序列、整合多模态数据（即结合来自不同来源的信息）以及开发人机界面以促进这些工具与临床实际相结合的深度学习算法将得到进一步发展。总之，这些模型已被证明是适用于不同类型数据的通用且潜在有效的工具，从口腔内病变照片到组织病理学切片，再到MRI扫描数据。
