Features of modeling a polymer implant for closing a defect after decompressive craniotomy

Okishev, D N; Cherebylo, S. A.; Konovalov, A N; Chelushkin, D M; Shekhtman, O D; Konovalov, N A; Окишева, Е. А.; Kravchuk, A D; Eliava, Shalva

doi:10.17116/neiro20228601117

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…The algorithm can be used in medical production for the initial modeling of the lost surface of the skull, as well as in medical 3D printing laboratories at universities and national medical research centers for developing skills of modeling custom implants. Our findings will also help resolving the issue of developing specialized medical software for 3D modeling of custom implants used for repairing defects in the skull bones [2].…”

Section: Discussionmentioning

confidence: 89%

“…A specialized software environment for threedimensional (3D) modeling is required for designing such products. These activities are performed by medical engineers with competence in the fields of technology, medicine, and anatomy [1], or by physicians with skills and experience in such programs [2]. Despite the more than thirty-year history of custom modeling in neurosurgery, specialists employ utilitarian software products that are widely used in technical industries: MeshMixer, Blender, Autodesk 3ds Max, InVesalius, Geomagic Design X, Materialize Mimics and 3 Matic, etc.…”

Section: Introductionmentioning

confidence: 99%

“…642 by the Russian Federation President of 1 December 2016 commands the need for transition to personalized medicine, high-tech health care system and technology, advanced digital intelligent manufacturing technologies, and the development of big data processing systems, machine learning and artificial intelligence. The first part of this strategy has already become firmly established in practical medicine, and the geography of the use of custom implants for repairing defects in the skull bones gradually expands [2][3][4]. At the same time, its second aspect related to the medical industry is in most cases at the stage of conceptual development.…”

Section: Introductionmentioning

confidence: 99%

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Using neural network for restoring the lost surface of skull bones

Mishinov

2023

Saratov Med J

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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%).

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Section: Discussionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using neural network for restoring the lost surface of skull bones

Mishinov

2023

Saratov Med J

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Application of a neural network to restore the lost surface of skull bones

Мишинов

2023

Saratov J Med Sci Res

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Objective: to evaluate the sensitivity, specificity and accuracy of a digital algorithm based on convo-lutional neural networks to restore of bones of cranium defects. Material and methods. Neural network training was carried out as a result of 6,000 epochs on 78,000 variants of skull models with artificially generated skull injuries. The evaluation was performed on 222 DICOM series of patients computerized tomography with bones of cranium defects. Results. The indicators of sensitivity, specificity and accuracy were 95.3%, 85.5% and 79.4% respectively. A number of experiments were carried out with step-by-step sorting of three-dimensional models in order to find the reasons for the unsatisfactory skull reconstructing results. Incorrect detection of the skull defect most often occurred in the area of the facial skeleton. After excluding the series with artifacts, the average increase in metrics was 2.6%. Conclusion. Correct determination of the bone defect at the scull model (specificity) by the algorithm had the greatest impact on the surface accuracy. The maximum accuracy of the algorithm, which allows using the obtained surfaces without additional processing in a three-dimensional modeling environment, was achieved on series without the presence of artifacts during computed tomography (83.5%), as well as with defects that do not extend to the skull base (79.5%).

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Modern Neurosurgery — the Multidisciplinary Integration of Competences an Technologies

et al. 2022

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Modern neurosurgery is based on the complex integration of basic and applied neurosciences and technologies. A significant number of different diagnostic and therapeutic approaches are used to prolong a patients life and preserve its quality to the maximum. At the same time, neurosurgery is the only clinical discipline providing direct access to the living human brain. A large body of information on the nervous system state and pathology, routinely generated in neurosurgery, attract specialists from the related medical and biological sciences. Such close cooperation of physicians, biologists, chemists, physicists, mathematicians, engineers, and other specialists ensures rapid and continuous improvement of neurosurgical treatment. This article highlights the spectrum of technologies in modern neurosurgery, the research tasks facing this specialty, and briefly the activities of the National Medical Research Center for Neurosurgery named after academician N.N. Burdenko the leading neurosurgical institution of the Russian Federation in priority clinical and scientific areas.

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Features of modeling a polymer implant for closing a defect after decompressive craniotomy

Cited by 4 publications

References 30 publications

Using neural network for restoring the lost surface of skull bones

Using neural network for restoring the lost surface of skull bones

Application of a neural network to restore the lost surface of skull bones

Modern Neurosurgery — the Multidisciplinary Integration of Competences an Technologies

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