Deep learning in oral surgery for third molar extraction: empirical evidence and original model
Preemptive analgesia is an analgesic intervention to influence postoperative pain sensation. Control of postoperative pain is a major challenge for any surgeon. Adequate control of postoperative pain continues to be a challenge for modern medicine. The advent of artificial intelligence (AI) in all s...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Biotechnology & Biotechnological Equipment |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/13102818.2024.2349564 |
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| author | Deyan Neychev Ralitsa Raycheva Nadezhda Kafadarova |
| author_facet | Deyan Neychev Ralitsa Raycheva Nadezhda Kafadarova |
| author_sort | Deyan Neychev |
| collection | DOAJ |
| description | Preemptive analgesia is an analgesic intervention to influence postoperative pain sensation. Control of postoperative pain is a major challenge for any surgeon. Adequate control of postoperative pain continues to be a challenge for modern medicine. The advent of artificial intelligence (AI) in all spheres of life, including medicine, has created the technical ability to process a variety of types and characteristics of data related to many diseases. The application of artificial neural networks in medical science has made it possible to obtain an independent, objective assessment as a consequence of the application of preemptive analgesia. The data analysis by our original model, compared with the routinely used statistical methods, show the presence of a tendency for a positive effect of preemptive analgesia. In order to obtain an efficient self-learning neural network, it is necessary to use large arrays of properly selected data that fulfill the role of input parameters for the neural network. The results obtained from the original model used are comparable to the traditionally used statistical methods. This model objectifies to a certain extent the preemptive analgesia in the surgery of third mandibular molars. |
| format | Article |
| id | doaj-art-4ba39225162e4f239e8b4a5b4ea0f63b |
| institution | OA Journals |
| issn | 1310-2818 1314-3530 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Biotechnology & Biotechnological Equipment |
| spelling | doaj-art-4ba39225162e4f239e8b4a5b4ea0f63b2025-08-20T01:59:17ZengTaylor & Francis GroupBiotechnology & Biotechnological Equipment1310-28181314-35302024-12-0138110.1080/13102818.2024.2349564Deep learning in oral surgery for third molar extraction: empirical evidence and original modelDeyan Neychev0Ralitsa Raycheva1Nadezhda Kafadarova2Department of Oral Surgery, Medical University of Plovdiv, Plovdiv, BulgariaDepartment of Social Medicine and Public Health, Medical University of Plovdiv, Plovdiv, BulgariaECIT Department, Plovdiv University “Paisii Hilendarski”, Plovdiv, BulgariaPreemptive analgesia is an analgesic intervention to influence postoperative pain sensation. Control of postoperative pain is a major challenge for any surgeon. Adequate control of postoperative pain continues to be a challenge for modern medicine. The advent of artificial intelligence (AI) in all spheres of life, including medicine, has created the technical ability to process a variety of types and characteristics of data related to many diseases. The application of artificial neural networks in medical science has made it possible to obtain an independent, objective assessment as a consequence of the application of preemptive analgesia. The data analysis by our original model, compared with the routinely used statistical methods, show the presence of a tendency for a positive effect of preemptive analgesia. In order to obtain an efficient self-learning neural network, it is necessary to use large arrays of properly selected data that fulfill the role of input parameters for the neural network. The results obtained from the original model used are comparable to the traditionally used statistical methods. This model objectifies to a certain extent the preemptive analgesia in the surgery of third mandibular molars.https://www.tandfonline.com/doi/10.1080/13102818.2024.2349564Preemptive analgesiadeep learningpostoperative painthird molar extraction |
| spellingShingle | Deyan Neychev Ralitsa Raycheva Nadezhda Kafadarova Deep learning in oral surgery for third molar extraction: empirical evidence and original model Biotechnology & Biotechnological Equipment Preemptive analgesia deep learning postoperative pain third molar extraction |
| title | Deep learning in oral surgery for third molar extraction: empirical evidence and original model |
| title_full | Deep learning in oral surgery for third molar extraction: empirical evidence and original model |
| title_fullStr | Deep learning in oral surgery for third molar extraction: empirical evidence and original model |
| title_full_unstemmed | Deep learning in oral surgery for third molar extraction: empirical evidence and original model |
| title_short | Deep learning in oral surgery for third molar extraction: empirical evidence and original model |
| title_sort | deep learning in oral surgery for third molar extraction empirical evidence and original model |
| topic | Preemptive analgesia deep learning postoperative pain third molar extraction |
| url | https://www.tandfonline.com/doi/10.1080/13102818.2024.2349564 |
| work_keys_str_mv | AT deyanneychev deeplearninginoralsurgeryforthirdmolarextractionempiricalevidenceandoriginalmodel AT ralitsaraycheva deeplearninginoralsurgeryforthirdmolarextractionempiricalevidenceandoriginalmodel AT nadezhdakafadarova deeplearninginoralsurgeryforthirdmolarextractionempiricalevidenceandoriginalmodel |