Augmented Reality in Surgery for Recurrent Pelvic Tumors: Clinical Case ; Применение дополненной реальности в хирургии рецидивной опухоли малого таза: клинический случай

Συγγραφείς: V. G. Grebenkov, V. V. Von, O. V. Balyura, A. L. Gavrilova, V. N. Rumyantsev, D. A. Rudakov, M. D. Grebenkova, A. S. Ponomarev, I. А. Savchenko, E. G. Verdiev, В. Г. Гребеньков, В. В. Вон, О. В. Балюра, А. Л. Гаврилова, В. Н. Румянцев, Д. А. Рудаков, М. Д. Гребенькова, А. С. Пономарев, И. А. Савченко, Э. Г. Вердиев

Πηγή: Creative surgery and oncology; Том 15, № 1 (2025); 85-91 ; Креативная хирургия и онкология; Том 15, № 1 (2025); 85-91 ; 2307-0501 ; 2076-3093

Θεματικοί όροι: интраоперационная навигация, rectal cancer, augmented reality, image-guided biopsy, intraoperative navigation, рак прямой кишки, дополненная реальность, биопсия под визуальным контролем

Περιγραφή αρχείου: application/pdf

Relation: https://www.surgonco.ru/jour/article/view/1057/646; World Health Organization. Colorectal cancer. Geneva: WHO (cited 2024 Nov 29). Available from: https://www.who.int/ru/news-room/fact-sheets/detail/colorectal-cancer; Seetohul J., Shafiee M., Sirlantzis K. Augmented Reality (AR) for surgical robotic and autonomous systems: state of the art, challenges, and solutions. Sensors. 2023;23(13):6202. DOI:10.3390/s23136202; Xiao S.-X., Wu W.-T., Yu T.-C., Chen I.-H., Yeh K.-T. Augmenting reality in spinal surgery: a narrative review of augmented reality applications in pedicle screw instrumentation. Medicina. 2024;60(9):1485. DOI:10.3390/medicina60091485; Shen Y., Wang S., Shen Y., Hu J. The application of augmented reality technology in perioperative visual guidance: technological advances and innovation challenges. Sensors. 2024;24(22):7363. DOI:10.3390/s24227363; Heining S.M., Raykov V., Wolff O., Alkadhi H., Pape H.C., Wanner G.A. Augmented reality-based surgical navigation of pelvic screw placement: an ex-vivo experimental feasibility study. Patient Saf Surg. 2024;18:3. DOI:10.1186/s13037-023-00385-6; De Jesus Encarnacion Ramirez M., Chmutin G., Nurmukhametov R., Soto G.R., Kannan S., Piavchenko, et al. Integrating augmented reality in spine surgery: redefining precision with new technologies. Brain Sci. 2024;14(7):645. DOI:10.3390/brainsci14070645; Sifted. How augmented reality is revolutionizing surgery. 2021 Mar 31 (cited 2024 Nov 29). Available from: https://sifted.eu/articles/augmented-reality-ar-surgery; Суров Д.А., Дымников Д.А., Соловьев И.А., Уточкин А.П., Габриелян М.А., Коржук М.С. и др. Госпитальная хирургия. СПб.: МОРСАР АВ; 2024.; Wang M., Wei M., Qin H., Wang B. The malignant solitary fibrous tumor in pelvic cavity: A case report and literature review. Clin Surg. 2022;7:3573.; Гребеньков В.Г., Румянцев В.Н., Иванов В.М., Балюра О.В., Дымников Д.А., Маркевич В.Ю. и др. Периоперационное применение технологии дополненной реальности при хирургическом лечении пациента с местнораспространенным локорегиональным рецидивом рака прямой кишки. Хирургия. Журнал им. Н.И. Пирогова. 2022;12-2:44–53. DOI:10.17116/hirurgia202212244; Schoeb D.S., Rassweiler J., Sigle A., Miernik A., Engels C., Goezen A.S., et al. Robotics and intraoperative navigation. Urologe A. 2021;60(1):27–38. DOI:10.1007/s00120-020-01405-4; Alessa F.M., Alhaag M.H., Al-Harkan I.M., Ramadan M.Z., Alqahtani F.M. A Neurophysiological evaluation of cognitive load during augmented reality interactions in various industrial maintenance and assembly tasks. Sensors. 2023;23(18):7698. DOI:10.3390/s23187698; Chen X., Li K. Robotic arm control system based on augmented reality brain-computer interface and computer vision. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2021;38(3):483–91. Chinese. DOI:10.7507/1001-5515.202011039; González Izard S., Sánchez Torres R., Alonso Plaza Ó., Juanes J.A. Nextmed: automatic imaging segmentation, 3D reconstruction, and 3D model visualization platform using augmented and virtual reality. Sensors (Basel). 2020;20(10):2962. DOI:10.3390/s20102962; Ivanov V.M., Krivtsov A.M., Strelkov S.V., Smirnov A.Y., Shipov R.Y., Grebenkov V.G., et al. Practical application of augmented/mixed reality technologies in surgery of abdominal cancer patients. J Imaging. 2022;8(7):183. DOI:10.3390/jimaging8070183; Котив Б.Н., Будько И.А., Иванов И.А., Тросько И.У. Использование искусственного интеллекта для медицинской диагностики с помощью реализации экспертной системы. Вестник Российской военно-медицинской академии. 2021;23(1):215–24. doi:10.17816/brmma63657.; Zawy Alsofy S., Nakamura M., Suleiman A., Sakellaropoulou I., Welzel Saravia H., Shalamberidze D., et al. Cerebral anatomy detection and surgical planning in patients with anterior skull base meningiomas using a virtual reality technique. J Clin Med. 2021;10(4):681. DOI:10.3390/jcm10040681; Thomas D.J. Augmented reality in surgery: the computer-aided medicine revolution. Int J Surg. 2016;(36):25. DOI:10.1016/j.ijsu.2016.10.003; Leuze C., Zoellner A., Schmidt A.R., Cushing R.E., Fischer M.J., Joltes K., et al. Augmented reality visualization tool for the future of tactical combat casualty care. J Trauma Acute Care Surg. 2021;(91):40–5. DOI:10.1097/TA.0000000000003263; Ivanov V.M., Krivtsov A.M., Strelkov S.V., Kalakutskiy N.V., Yaremenko A.I., Petropavlovskaya M.Yu., et al. Intraoperative use of mixed reality technology in median neck and branchial cyst excision. Future Internet. 2021;(13):214. DOI:10.3390/fi13080214; https://www.surgonco.ru/jour/article/view/1057

Διαθεσιμότητα: https://www.surgonco.ru/jour/article/view/1057
https://doi.org/10.24060/2076-3093-2025-15-1-85-91

Machine learning methods for spectrally-resolved imaging analysis in neuro-oncology ; Методы машинного обучения для анализа спектрально-разрешенных изображений в нейроонкологии

Συγγραφείς: T. A. Savelieva, I. D. Romanishkin, A. Ospanov, K. G. Linkov, S. A. Goryajnov, G. V. Pavlova, I. N. Pronin, V. B. Loschenov, Т. А. Савельева, И. Д. Романишкин, А. Оспанов, К. Г. Линьков, С. А. Горяйнов, Г. В. Павлова, И. Н. Пронин, В. Б. Лощенов

Πηγή: Biomedical Photonics; Том 13, № 4 (2024); 40-54 ; 2413-9432

Θεματικοί όροι: гиперспектральные изображения, spectrally-resolved imaging, intracranial tumor, machine learning, fluorescence intraoperative navigation, fluorescence endomicroscopy, Raman microscopy, CARS, Stimulated Raman Histology, hyperspectral images, спектрально-разрешенная визуализация, внутричерепные опухоли, машинное обучение, флуоресцентная интраоперационная навигация, флуоресцентная эндомикроскопия, микроскопия комбинационного рассеяния, стимулированная Рамановская гистология

Περιγραφή αρχείου: application/pdf

Relation: https://www.pdt-journal.com/jour/article/view/680/477; Aboras M., Amasha H., Ibraheem I. Early detection of melanoma using multispectral imaging and artifcial intelligence techniques // American Journal of Biomedical and Life Sciences. – 2015. – Vol. 3. – № 2–3. – P. 29–33. doi:10.11648/j.ajbls.s.2015030203.16.; Kotwal A., Saragadam V., Bernstock J. D. et al. Hyperspectral imaging in neurosurgery: a review of systems, computational methods, and clinical applications // Journal of Biomedical Optics. – 2024. – Vol. 30. – № 02. doi:10.1117/1.JBO.30.2.023512.; Stummer W., Stocker S., Wagner S. et al. Intraoperative Detection of Malignant Gliomas by 5-Aminolevulinic Acid-induced Porphyrin Fluorescence // Neurosurgery. – 1998. – Vol. 42. – № 3. – P. 518–526. doi:10.1097/00006123-199803000-00017.; Traylor J. I., Pernik M. N., Sternisha A. C. et al. Molecular and Metabolic Mechanisms Underlying Selective 5-Aminolevulinic Acid-Induced Fluorescence in Gliomas // Cancers. – 2021. – Vol. 13. – № 3. – P. 580. doi:10.3390/cancers13030580.; Ivanova-Radkevich V. I., Kuznetsova O. M., Filonenko E. V. The role of membrane transport proteins in 5-ALA-induced accumulation of protoporphyrin iX in tumor cells // Biomedical Photonics. – 2024. – Vol. 13. – № 2. – P. 43–48. doi:10.24931/2413-9432-2024-13-2-43-48.; Nasir-Moin M., Wadiura L. I., Sacalean V. et al. Localization of protoporphyrin IX during glioma-resection surgery via paired stimulated Raman histology and fuorescence microscopy // Nature Biomedical Engineering. – 2024. – Vol. 8. – № 6. – P. 672–688. doi:10.1038/s41551-024-01217-3.; Matsumura H., Akimoto J., Haraoka J. et al. Uptake and retention of the photosensitizer mono-l-asparthyl chlorine e6 in experimental malignant glioma // Lasers in Medical Science. – 2008. – Vol. 23. – № 3. – P. 237–245. doi:10.1007/s10103-007-0469-3.; Valdés P. A., Jacobs V., Harris B. T. et al. Quantitative fuorescence using 5-aminolevulinic acid-induced protoporphyrin IX biomarker as a surgical adjunct in low-grade glioma surgery // Journal of Neurosurgery. – 2015. – Vol. 123. – № 3. – P. 771–780. doi:10.3171/2014.12.JNS14391.; Rynda A. Yu., Olyushin V. E., Rostovtsev D. M. et al. Results of microsurgical resection of glioblastomas under endoscopic and fuorescent control // Biomedical Photonics. – 2024. – Vol. 13. – № 3. – P. 20–30. doi:10.24931/2413-9432-2024-13-3-20-30.; Горяйнов С. А., Потапов А. А., Лощенов В. Б. и др. Флуоресценция в нейрохирургии / С. А. Горяйнов, А. А. Потапов, В. Б. Лощенов и др., под ред. Д. Ю. Усачева, Москва: ТПС Принт, 2024.; Loshchenov M., Zelenkov P., Potapov A. et al. Endoscopic fuorescence visualization of 5-ALA photosensitized central nervous system tumors in the neural tissue transparency spectral range // Photonics & Lasers in Medicine. – 2014. – Vol. 3. – № 2. doi:10.1515/plm-2013-0017.; Savelieva T. A., Loshchenov M. V., Borodkin A. V. et al. Combined spectroscopic and video fuorescent instrument for intraoperative navigation when removing a glial tumor ed. Z. Zalevsky, V. V. Tuchin, W. C. Blondel, Online Only, France: SPIE, 2020. C. 35. doi:10.1117/12.2556064.; Udeneev A. M., Kalyagina N. A., Reps V. F. et al. Photo and spectral fuorescence analysis of the spinal cord injury area in animal models // Biomedical Photonics. – 2023. – Vol. 12. – № 3. – P. 15–20. doi:10.24931/2413-9432-2023-12-3-16-20.; Wainwright J. V., Endo T., Cooper J. B. et al. The role of 5-aminolevulinic acid in spinal tumor surgery: a review // Journal of Neuro-Oncology. – 2019. – Vol. 141. – № 3. – P. 575–584. doi:10.1007/s11060-018-03080-0.; Valdés P. A., Jacobs V. L., Leblond F. et al. Quantitative spectrally resolved intraoperative fuorescence imaging for neurosurgical guidance in brain tumor surgery: pre-clinical and clinical results ed. H. Hirschberg, S. J. Madsen, E. D. Jansen et al., San Francisco, California, United States, 2014. C. 892809. doi:10.1117/12.2039090.; Picart T., Gautheron A., Caredda C. et al. Fluorescence-Guided Surgical Techniques in Adult Difuse Low-Grade Gliomas: State-of-the-Art and Emerging Techniques: A Systematic Review // Cancers. – 2024. – Vol. 16. – № 15. – P. 2698. doi:10.3390/cancers16152698.; Maragkou T., Quint K., Pollo B. et al. Intraoperative confocal laser endomicroscopy for brain tumors - potential and challenges from a neuropathological perspective // Free Neuropathology. – 2022. – P. 24 Pages. doi:10.17879/FREENEUROPATHOLOGY-2022-4369.; Sankar T., Delaney P. M., Ryan R. W. et al. Miniaturized Handheld Confocal Microscopy for Neurosurgery: Results in an Experimental Glioblastoma Model // Neurosurgery. – 2010. – Vol. 66. – № 2. – P. 410–418. doi:10.1227/01.NEU.0000365772.66324.6F.; Foersch S., Heimann A., Ayyad A. et al. Confocal Laser Endomicroscopy for Diagnosis and Histomorphologic Imaging of Brain Tumors In Vivo // PLoS ONE. – 2012. – Vol. 7. – № 7. – P. e41760. doi:10.1371/journal.pone.0041760.; Martirosyan N. L., Cavalcanti D. D., Eschbacher J. M. et al. Use of in vivo near-infrared laser confocal endomicroscopy with indocyanine green to detect the boundary of infltrative tumor: Laboratory investigation // Journal of Neurosurgery. – 2011. – Vol. 115. – № 6. – P. 1131–1138. doi:10.3171/2011.8.JNS11559.; Sanai N., Eschbacher J., Hattendorf G. et al. Intraoperative Confocal Microscopy for Brain Tumors: A Feasibility Analysis in Humans // Operative Neurosurgery. – 2011. – Vol. 68. – P. ons282–ons290. doi:10.1227/NEU.0b013e318212464e.; Sanai N., Snyder L. A., Honea N. J. et al. Intraoperative confocal microscopy in the visualization of 5-aminolevulinic acid fuorescence in low-grade gliomas: Clinical article // Journal of Neurosurgery. – 2011. – Vol. 115. – № 4. – P. 740–748. doi:10.3171/2011.6.JNS11252.; Eschbacher J., Martirosyan N. L., Nakaji P. et al. In vivo intraoperative confocal microscopy for real-time histopathological imaging of brain tumors: Clinical article // Journal of Neurosurgery. – 2012. – Vol. 116. – № 4. – P. 854–860. doi:10.3171/2011.12.JNS11696.; Pavlov V., Meyronet D., Meyer-Bisch V. et al. Intraoperative Probe-Based Confocal Laser Endomicroscopy in Surgery and Stereotactic Biopsy of Low-Grade and High-Grade Gliomas: A Feasibility Study in Humans // Neurosurgery. – 2016. – Vol. 79. – № 4. – P. 604–612. doi:10.1227/NEU.0000000000001365.; Liu J. T. C., Meza D., Sanai N. Trends in Fluorescence Image-Guided Surgery for Gliomas // Neurosurgery. – 2014. – Vol. 75. – № 1. – P. 61–71. doi:10.1227/NEU.0000000000000344.; Sibai M., Veilleux I., Elliott J. T. et al. Quantitative spatial frequency fuorescence imaging in the sub-difusive domain for image-guided glioma resection // Biomedical Optics Express. – 2015. – Vol. 6. – № 12. – P. 4923. doi:10.1364/BOE.6.004923.; Wirth D., Kolste K., Kanick S. et al. Fluorescence depth estimation from wide-feld optical imaging data for guiding brain tumor resection: a multi-inclusion phantom study // Biomedical Optics Express. – 2017. – Vol. 8. – № 8. – P. 3656. doi:10.1364/BOE.8.003656.; Belykh E., Miller E. J., Carotenuto A. et al. Progress in Confocal Laser Endomicroscopy for Neurosurgery and Technical Nuances for Brain Tumor Imaging With Fluorescein // Frontiers in Oncology. – 2019. – Vol. 9. – P. 554. doi:10.3389/fonc.2019.00554.; Abramov I., Park M. T., Belykh E. et al. Intraoperative confocal laser endomicroscopy: prospective in vivo feasibility study of a clinical-grade system for brain tumors // Journal of Neurosurgery. – 2023. – Vol. 138. – № 3. – P. 587–597. doi:10.3171/2022.5.JNS2282.; Belykh E., Miller E. J., Patel A. A. et al. Diagnostic Accuracy of a Confocal Laser Endomicroscope for In Vivo Diferentiation Between Normal Injured And Tumor Tissue During Fluorescein-Guided Glioma Resection: Laboratory Investigation // World Neurosurgery. – 2018. – Vol. 115. – P. e337–e348. doi:10.1016/j.wneu.2018.04.048.; Acerbi F., Pollo B., De Laurentis C. et al. Ex Vivo Fluorescein-Assisted Confocal Laser Endomicroscopy (CONVIVO® System) in Patients With Glioblastoma: Results From a Prospective Study // Frontiers in Oncology. – 2020. – Vol. 10. – P. 606574. doi:10.3389/fonc.2020.606574.; Giannoni L., Bonaudo C., Marradi M. et al. Optical characterisation and study of ex vivo glioma tissue for hyperspectral imaging during neurosurgery ed. D. Contini, Y. Hoshi, T. D. O’Sullivan, Munich, Germany: SPIE, 2023. C. 81. doi:10.1117/12.2670854.; Fabelo H., Ortega S., Kabwama S. et al. HELICoiD project: a new use of hyperspectral imaging for brain cancer detection in real-time during neurosurgical operations ed. D. P. Bannon, Baltimore, Maryland, United States:, 2016. C. 986002. doi:10.1117/12.2223075.; Fabelo H., Ortega S., Lazcano R. et al. An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation // Sensors. – 2018. – Vol. 18. – № 2. – P. 430. doi:10.3390/s18020430.; Ezhov I., Giannoni L., Shit S. et al. Identifying chromophore fngerprints of brain tumor tissue on hyperspectral imaging using principal component analysis ed. D. Contini, Y. Hoshi, T. D. O’Sullivan, Munich, Germany: SPIE, 2023. C. 78. doi:10.1117/12.2670775.; Martinez B., Leon R., Fabelo H. et al. Most Relevant Spectral Bands Identifcation for Brain Cancer Detection Using Hyperspectral Imaging // Sensors. – 2019. – Vol. 19. – № 24. – P. 5481. doi:10.3390/s19245481.; DePaoli D., Lemoine É., Ember K. et al. Rise of Raman spectroscopy in neurosurgery: a review // Journal of Biomedical Optics. – 2020. – Vol. 25. – № 05. – P. 1. doi:10.1117/1.JBO.25.5.050901.; Romanishkin I. D., Savelieva T. A., Ospanov A. et al. Classifcation of intracranial tumors based on optical-spectral analysis // Biomedical Photonics. – 2023. – Vol. 12. – № 3. – P. 4–10. doi:10.24931/2413-9432-2023-12-3-4-10.; Romanishkin I., Savelieva T., Kosyrkova A. et al. Diferentiation of glioblastoma tissues using spontaneous Raman scattering with dimensionality reduction and data classifcation // Frontiers in Oncology. – 2022. – Vol. 12. – P. 944210. doi:10.3389/fonc.2022.944210.; Hollon T., Orringer D. A. Label-free brain tumor imaging using Raman-based methods // Journal of Neuro-Oncology. – 2021. – Vol. 151. – № 3. – P. 393–402. doi:10.1007/s11060-019-03380-z.; Kast R., Auner G., Yurgelevic S. et al. Identifcation of regions of normal grey matter and white matter from pathologic glioblastoma and necrosis in frozen sections using Raman imaging // Journal of Neuro-Oncology. – 2015. – Vol. 125. – № 2. – P. 287–295. doi:10.1007/s11060-015-1929-4.; Hollon T., Jiang C., Chowdury A. et al. Artifcial-intelligence-based molecular classifcation of difuse gliomas using rapid, label-free optical imaging // Nature Medicine. – 2023. – Vol. 29. – № 4. – P. 828–832. doi:10.1038/s41591-023-02252-4.; Hollon T. C., Pandian B., Adapa A. R. et al. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks // Nature Medicine. – 2020. – Vol. 26. – № 1. – P. 52–58. doi:10.1038/s41591-019-0715-9.; Evans C. L., Xu X., Kesari S. et al. Chemically-selective imaging of brain structures with CARS microscopy // Optics Express. – 2007. – Vol. 15. – № 19. – P. 12076. doi:10.1364/OE.15.012076.; Uckermann O., Galli R., Tamosaityte S. et al. Label-Free Delineation of Brain Tumors by Coherent Anti-Stokes Raman Scattering Microscopy in an Orthotopic Mouse Model and Human Glioblastoma // PLoS ONE. – 2014. – Vol. 9. – № 9. – P. e107115. doi:10.1371/journal.pone.0107115.; Mascagni P., Alapatt D., Sestini L. et al. Computer vision in surgery: from potential to clinical value // npj Digital Medicine. – 2022. – Vol. 5. – № 1. – P. 163. doi:10.1038/s41746-022-00707-5.; Rau A., Edwards P. J. E., Ahmad O. F. et al. Implicit domain adaptation with conditional generative adversarial networks for depth prediction in endoscopy // International Journal of Computer Assisted Radiology and Surgery. – 2019. – Vol. 14. – № 7. – P. 1167–1176. doi:10.1007/s11548-019-01962-w.; Kassem H., Alapatt D., Mascagni P. et al. Federated Cycling (FedCy): Semi-Supervised Federated Learning of Surgical Phases // IEEE Transactions on Medical Imaging. – 2023. – Vol. 42. – № 7. – P. 1920–1931. doi:10.1109/TMI.2022.3222126.; Orringer D. A., Pandian B., Niknafs Y. S. et al. Rapid intraoperative histology of unprocessed surgical specimens via fbre-laser-based stimulated Raman scattering microscopy // Nature Biomedical Engineering. – 2017. – Vol. 1. – № 2. – P. 0027. doi:10.1038/s41551-016-0027.; Leon R., Fabelo H., Ortega S. et al. Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection // npj Precision Oncology. – 2023. – Vol. 7. – № 1. – P. 119. doi:10.1038/s41698-023-00475-9.; Fabelo H., Halicek M., Ortega S. et al. Deep Learning-Based Framework for In Vivo Identifcation of Glioblastoma Tumor using Hyperspectral Images of Human Brain // Sensors. – 2019. – Vol. 19. – № 4. – P. 920. doi:10.3390/s19040920.; Ravi D., Fabelo H., Callic G. M. et al. Manifold Embedding and Semantic Segmentation for Intraoperative Guidance With Hyperspectral Brain Imaging // IEEE Transactions on Medical Imaging. – 2017. – Vol. 36. – № 9. – P. 1845–1857. doi:10.1109/TMI.2017.2695523.; Fabelo H., Ortega S., Ravi D. et al. Spatio-spectral classifcation of hyperspectral images for brain cancer detection during surgical operations // PLOS ONE. – 2018. – Vol. 13. – № 3. – P. e0193721. doi:10.1371/journal.pone.0193721.; Florimbi G., Fabelo H., Torti E. et al. Accelerating the K-Nearest Neighbors Filtering Algorithm to Optimize the Real-Time Classifcation of Human Brain Tumor in Hyperspectral Images // Sensors. – 2018. – Vol. 18. – № 7. – P. 2314. doi:10.3390/s18072314.; Zhang Y., Cao G., Li X. et al. Cascaded Random Forest for Hyperspectral Image Classifcation // IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. – 2018. – Vol. 11. – № 4. – P. 1082–1094. doi:10.1109/JSTARS.2018.2809781.; Xia J., Ghamisi P., Yokoya N. et al. Random Forest Ensembles and Extended Multiextinction Profles for Hyperspectral Image Classifcation // IEEE Transactions on Geoscience and Remote Sensing. – 2018. – Vol. 56. – № 1. – P. 202–216. doi:10.1109/TGRS.2017.2744662.; Ayaz H., Tormey D., McLoughlin I. et al. Hyperspectral Brain Tissue Classifcation using a Fast and Compact 3D CNN Approach Genova, Italy: IEEE, 2022. C. 1–4. doi:10.1109/IPAS55744.2022.10053044.; Baig N., Fabelo H., Ortega S. et al. Empirical Mode Decomposition Based Hyperspectral Data Analysis for Brain Tumor Classifcation Mexico: IEEE, 2021. C. 2274–2277. doi:10.1109/EMBC46164.2021.9629676.; Cruz-Guerrero I. A., Leon R., Campos-Delgado D. U. et al. Classifcation of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing // Applied Sciences. – 2020. – Vol. 10. – № 16. – P. 5686. doi:10.3390/app10165686.; Ruiz L., Martin A., Urbanos G. et al. Multiclass Brain Tumor Classifcation Using Hyperspectral Imaging and Supervised Machine Learning Segovia, Spain: IEEE, 2020. C. 1–6. doi:10.1109/DCIS51330.2020.9268650.; Salvador R., Fabelo H., Lazcano R. et al. Demo: HELICoiD tool demonstrator for real-time brain cancer detection Rennes, France: IEEE, 2016. C. 237–238. doi:10.1109/DASIP.2016.7853831.; Sutradhar P., Sancho J., Villa M. et al. Exploration of Realtime Brain tumor classifcation from Hyperspectral Images in Heterogeneous Embedded MPSoC Pamplona, Spain: IEEE, 2022. C. 01–06. doi:10.1109/DCIS55711.2022.9970064.; Torti E., Florimbi G., Castelli F. et al. Parallel K-Means Clustering for Brain Cancer Detection Using Hyperspectral Images // Electronics. – 2018. – Vol. 7. – № 11. – P. 283. doi:10.3390/electronics7110283.; Urbanos G., Martín A., Vázquez G. et al. Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classifcation // Sensors. – 2021. – Vol. 21. – № 11. – P. 3827. doi:10.3390/s21113827.; Manni F., Van Der Sommen F., Fabelo H. et al. Hyperspectral Imaging for Glioblastoma Surgery: Improving Tumor Identifcation Using a Deep Spectral-Spatial Approach // Sensors. – 2020. – Vol. 20. – № 23. – P. 6955. doi:10.3390/s20236955.; Fürtjes G., Reinecke D., Von Spreckelsen N. et al. Intraoperative microscopic autofuorescence detection and characterization in brain tumors using stimulated Raman histology and two-photon fuorescence // Frontiers in Oncology. – 2023. – Vol. 13. – P. 1146031. doi:10.3389/fonc.2023.1146031.; Reinecke D., Von Spreckelsen N., Mawrin C. et al. Novel rapid intraoperative qualitative tumor detection by a residual convolutional neural network using label-free stimulated Raman scattering microscopy // Acta Neuropathologica Communications. – 2022. – Vol. 10. – № 1. – P. 109. doi:10.1186/s40478-022-01411-x.; Shirshin E. A., Yakimov B. P., Darvin M. E. et al. Label-Free Multiphoton Microscopy: The Origin of Fluorophores and Capabilities for Analyzing Biochemical Processes // Biochemistry (Moscow). – 2019. – Vol. 84. – № S1. – P. 69–88. doi:10.1134/S0006297919140050.; Uckermann O., Galli R., Mark G. et al. Label-free multiphoton imaging allows brain tumor recognition based on texture analysis—a study of 382 tumor patients // Neuro-Oncology Advances. – 2020. – Vol. 2. – № 1. – P. vdaa035. doi:10.1093/noajnl/vdaa035.; Lita A., Sjöberg J., Păcioianu D. et al. Raman-based machine-learning platform reveals unique metabolic diferences between IDHmut and IDHwt glioma // Neuro-Oncology. – 2024. – Vol. 26. – № 11. – P. 1994–2009. doi:10.1093/neuonc/noae101.; Morais C. L. M., Lilo T., Ashton K. M. et al. Determination of meningioma brain tumour grades using Raman microspectroscopy imaging // The Analyst. – 2019. – Vol. 144. – № 23. – P. 7024–7031. doi:10.1039/C9AN01551E.

Διαθεσιμότητα: https://www.pdt-journal.com/jour/article/view/680
https://doi.org/10.24931/2413-9432-2024-13-4-40-54

Интраоперационная навигация при выполнении катетерной радиочастотной симпатической денервации почечной артерии у больных при рефрактерной артериальной гипертензии ; Intraoperative navigation while performance of catheteric radio—frequency sympathetic denervation of renal artery in patients with refractory arterial hypertension

Συγγραφείς: Карпенко, Ю. И., Гармази Сабер, Karpenko, Yu. I., Garmazi Saber

Θεματικοί όροι: рефрактерная артериальная гипертензия, интраоперационная навигация, катетерная радиочастотная симпатическая денервация почечной артерии, refractory arterial hypertension, intraoperative navigation, the catheter radiofrequency sympathetic denervation of a renal artery

Περιγραφή αρχείου: application/pdf

Relation: Карпенко Ю. И. Интраоперационная навигация при выполнении катетерной радиочастотной симпатической денервации почечной артерии у больных при рефрактерной артериальной гипертензии / Ю. И. Карпенко, Сабер Гармази // Клінічна хірургія. – 2015. – № 9. – С. 43–45.; http://repo.odmu.edu.ua:80/xmlui/handle/123456789/4072

Διαθεσιμότητα: http://repo.odmu.edu.ua:80/xmlui/handle/123456789/4072

Интраоперационная навигация при выполнении катетерной радиочастотной симпатической денервации почечной артерии у больных при рефрактерной артериальной гипертензии ; Intraoperative navigation while performance of catheteric radio—frequency sympathetic denervation of renal artery in patients with refractory arterial hypertension

Συγγραφείς: Карпенко, Ю. И., Гармази Сабер, Karpenko, Yu. I., Garmazi Saber

Θεματικοί όροι: рефрактерная артериальная гипертензия, интраоперационная навигация, катетерная радиочастотная симпатическая денервация почечной артерии, refractory arterial hypertension, intraoperative navigation, the catheter radiofrequency sympathetic denervation of a renal artery

Περιγραφή αρχείου: application/pdf

Relation: Карпенко Ю. И. Интраоперационная навигация при выполнении катетерной радиочастотной симпатической денервации почечной артерии у больных при рефрактерной артериальной гипертензии / Ю. И. Карпенко, Сабер Гармази // Клінічна хірургія. – 2015. – № 9. – С. 43–45.; http://repo.odmu.edu.ua:80/xmlui/handle/123456789/4072

Διαθεσιμότητα: http://repo.odmu.edu.ua:80/xmlui/handle/123456789/4072

Удаление слюнных конкрементов поднижнечелюстной железы с применением ультразвуковой интраоперационной навигации

Συγγραφείς: Ластовка, А. С., Ядченко, В. Н.

Θεματικοί όροι: слюнные конкременты, поднижнечелюстная железа, ультразвуковая интраоперационная навигация

Relation: Ластовка, А. С. Удаление слюнных конкрементов поднижнечелюстной железы с применением ультразвуковой интраоперационной навигации / А. С. Ластовка, В. Н. Ядченко // Экстренная медицина. - 2013. - № 2 (06). - 24-30.; http://elib.gsmu.by/handle/GomSMU/1770

Διαθεσιμότητα: http://elib.gsmu.by/handle/GomSMU/1770

Оценка эффективности использования электромагнитной навигации при эндоскопических эндоназальных операциях на околоносовых пазухах и основании черепа

Συγγραφείς: Нерсесян М.В., Попадюк В.И., Гукасян М., Зинкевич Д.Н., Черникова Н.А., Доронина В.А., Лубнин А.Ю.

Πηγή: Folia Otorhinolaryngologiae et Pathologiae Respiratoriae

Θεματικοί όροι: Image-guided surgery, intraoperation navigation, electromagnetic navigation, functional endoscopic sinus surgery (FESS), Endoscopic sinus surgery, endoscopic skull base surgery, complications of endoscopic transnasal surgery, интраоперационная навигация, электромагнитная навигация, эндоназальная эндоскопическая хирургия околоносовых пазух, эндоскопическая санация пазух носа, синусотомия, эндоскопическая трансназальная хирургия основания черепа, осложнения эндоскопических эндоназальных операций

Relation: https://repository.rudn.ru/records/article/record/99201/

Διαθεσιμότητα: https://repository.rudn.ru/records/article/record/99201/