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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MDPI AG
2025-05-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/6/585 |
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| author | Luca Michelutti Alessandro Tel Massimo Robiony Lorenzo Marini Daniele Tognetto Edoardo Agosti Tamara Ius Caterina Gagliano Marco Zeppieri |
| author_facet | Luca Michelutti Alessandro Tel Massimo Robiony Lorenzo Marini Daniele Tognetto Edoardo Agosti Tamara Ius Caterina Gagliano Marco Zeppieri |
| author_sort | Luca Michelutti |
| collection | DOAJ |
| description | 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. |
| format | Article |
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| institution | Matheson Library |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Bioengineering |
| spelling | doaj-art-83d7cd946b4040eba3d0639e6b5b02e72025-06-25T13:29:46ZengMDPI AGBioengineering2306-53542025-05-0112658510.3390/bioengineering12060585Updates, Applications and Future Directions of Deep Learning for the Images Processing in the Field of Cranio-Maxillo-Facial SurgeryLuca Michelutti0Alessandro Tel1Massimo Robiony2Lorenzo Marini3Daniele Tognetto4Edoardo Agosti5Tamara Ius6Caterina Gagliano7Marco Zeppieri8Clinic of Maxillofacial Surgery, Head-Neck and NeuroScience Department, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, ItalyClinic of Maxillofacial Surgery, Head-Neck and NeuroScience Department, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, ItalyClinic of Maxillofacial Surgery, Head-Neck and NeuroScience Department, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, ItalyClinic of Maxillofacial Surgery, Head-Neck and NeuroScience Department, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, ItalyDepartment of Medicine, Surgery and Health Sciences, University of Trieste, 34127 Trieste, ItalyDivision of Neurosurgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Piazza Spedali Civili 1, 25123 Brescia, ItalyAcademic Neurosurgery, Department of Neurosciences, University of Padova, 35121 Padova, ItalyDepartment of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, ItalyDepartment of Medicine, Surgery and Health Sciences, University of Trieste, 34127 Trieste, ItalyThe 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.https://www.mdpi.com/2306-5354/12/6/585deep learningcranio-maxillo-facial surgeryimages processingclassificationsegmentationoral cancer |
| spellingShingle | Luca Michelutti Alessandro Tel Massimo Robiony Lorenzo Marini Daniele Tognetto Edoardo Agosti Tamara Ius Caterina Gagliano Marco Zeppieri Updates, Applications and Future Directions of Deep Learning for the Images Processing in the Field of Cranio-Maxillo-Facial Surgery Bioengineering deep learning cranio-maxillo-facial surgery images processing classification segmentation oral cancer |
| title | Updates, Applications and Future Directions of Deep Learning for the Images Processing in the Field of Cranio-Maxillo-Facial Surgery |
| title_full | Updates, Applications and Future Directions of Deep Learning for the Images Processing in the Field of Cranio-Maxillo-Facial Surgery |
| title_fullStr | Updates, Applications and Future Directions of Deep Learning for the Images Processing in the Field of Cranio-Maxillo-Facial Surgery |
| title_full_unstemmed | Updates, Applications and Future Directions of Deep Learning for the Images Processing in the Field of Cranio-Maxillo-Facial Surgery |
| title_short | Updates, Applications and Future Directions of Deep Learning for the Images Processing in the Field of Cranio-Maxillo-Facial Surgery |
| title_sort | updates applications and future directions of deep learning for the images processing in the field of cranio maxillo facial surgery |
| topic | deep learning cranio-maxillo-facial surgery images processing classification segmentation oral cancer |
| url | https://www.mdpi.com/2306-5354/12/6/585 |
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