Updates, Applications and Future Directions of Deep Learning for the Images Processing in the Field of Cranio-Maxillo-Facial Surgery
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 t...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/12/6/585 |
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| Summary: | 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. |
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| ISSN: | 2306-5354 |