Using neural network for restoring the lost surface of skull bones
Objective: To assess the sensitivity, specificity and accuracy of a digital algorithm based on convolutional neural networks used for restoring the lost surface of the skull bones. Materials and methods. The neural network was trained over 6,000 epochs on 78,000 variants of skull models with artif...
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| Format: | Article |
| Language: | English |
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Saratov State Medical University
2023-03-01
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| Series: | Saratov Medical Journal |
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| Online Access: | https://sarmj.org/sites/default/files/pdf/2023/Saratov_Med_J-2023-0102.pdf |
| _version_ | 1827298911915606016 |
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| author | Mishinov, Sergey V. |
| author_facet | Mishinov, Sergey V. |
| author_sort | Mishinov, Sergey V. |
| collection | DOAJ |
| description | Objective: To assess the sensitivity, specificity and accuracy of a digital algorithm based on convolutional neural networks used
for restoring the lost surface of the skull bones.
Materials and methods. The neural network was trained over 6,000 epochs on 78,000 variants of skull models with artificially
generated skull injuries. The key parameters of the algorithm were assessed on 222 series of multislice computed tomography
(MSCT) of patients with defects of the skull bones, presented in DICOM format.
Results. For the group as a whole, the sensitivity, specificity, and accuracy rates were 95.3%, 85.5%, and 79.4%, respectively.
Multiple experiments were conducted with a step-by-step elimination of 3D models in order to find the underlying cause of
unsatisfactory outcomes of the skull lost surface restoration. Incorrect identification of the defect zone most often occurred in
the area of the facial skeleton. After excluding series with the presence of artifacts, the mean increase in metrics was 2.6%.
Conclusion. The accuracy of identifying the reference points (specificity) on a 3D model of the skull by the algorithm had the
greatest impact on the ultimate accuracy of repairing the lost surface. The maximum accuracy of the algorithm allowing the use
of the resulting surfaces without additional processing in a 3D modeling environment was achieved in series without the
presence of artifacts in computed tomography (83.5%), as well as with defects that did not extend to the base of the skull
(79.5%). |
| first_indexed | 2024-04-24T15:24:08Z |
| format | Article |
| id | doaj.art-0d93b1e1876f4bb7aeb5e58e2487b102 |
| institution | Directory Open Access Journal |
| issn | 2712-8253 |
| language | English |
| last_indexed | 2024-04-24T15:24:08Z |
| publishDate | 2023-03-01 |
| publisher | Saratov State Medical University |
| record_format | Article |
| series | Saratov Medical Journal |
| spelling | doaj.art-0d93b1e1876f4bb7aeb5e58e2487b1022024-04-02T06:52:28ZengSaratov State Medical UniversitySaratov Medical Journal2712-82532023-03-01411510.15275/sarmj.2023.0102Using neural network for restoring the lost surface of skull bonesMishinov, Sergey V. 0Ya.L. Tsivyan Research Institute of Traumatology and Orthopedics of Novosibirsk, Novosibirsk, RussiaObjective: To assess the sensitivity, specificity and accuracy of a digital algorithm based on convolutional neural networks used for restoring the lost surface of the skull bones. Materials and methods. The neural network was trained over 6,000 epochs on 78,000 variants of skull models with artificially generated skull injuries. The key parameters of the algorithm were assessed on 222 series of multislice computed tomography (MSCT) of patients with defects of the skull bones, presented in DICOM format. Results. For the group as a whole, the sensitivity, specificity, and accuracy rates were 95.3%, 85.5%, and 79.4%, respectively. Multiple experiments were conducted with a step-by-step elimination of 3D models in order to find the underlying cause of unsatisfactory outcomes of the skull lost surface restoration. Incorrect identification of the defect zone most often occurred in the area of the facial skeleton. After excluding series with the presence of artifacts, the mean increase in metrics was 2.6%. Conclusion. The accuracy of identifying the reference points (specificity) on a 3D model of the skull by the algorithm had the greatest impact on the ultimate accuracy of repairing the lost surface. The maximum accuracy of the algorithm allowing the use of the resulting surfaces without additional processing in a 3D modeling environment was achieved in series without the presence of artifacts in computed tomography (83.5%), as well as with defects that did not extend to the base of the skull (79.5%).https://sarmj.org/sites/default/files/pdf/2023/Saratov_Med_J-2023-0102.pdfneurosurgerycranioplasty3d modeling |
| spellingShingle | Mishinov, Sergey V. Using neural network for restoring the lost surface of skull bones Saratov Medical Journal neurosurgery cranioplasty 3d modeling |
| title | Using neural network for restoring the lost surface of skull bones |
| title_full | Using neural network for restoring the lost surface of skull bones |
| title_fullStr | Using neural network for restoring the lost surface of skull bones |
| title_full_unstemmed | Using neural network for restoring the lost surface of skull bones |
| title_short | Using neural network for restoring the lost surface of skull bones |
| title_sort | using neural network for restoring the lost surface of skull bones |
| topic | neurosurgery cranioplasty 3d modeling |
| url | https://sarmj.org/sites/default/files/pdf/2023/Saratov_Med_J-2023-0102.pdf |
| work_keys_str_mv | AT mishinovsergeyv usingneuralnetworkforrestoringthelostsurfaceofskullbones |