IMPACT OF AUGMENTED REALITY-BASED NEURONAVIGATION ON NEUROSURGICAL EDUCATION AND TRAINING

Öz

Objective: Integrating technology into surgical training enhances learning experiences. Affordable extended reality (XR) equipment, like the Illumetry XR screen with ArSurgeon software, allows segmentation of preoperative MRI and CT scans in a digital environment, presenting 3D visualisations of tumours and surrounding anatomy. This improves the spatial orientation and understanding of surgical cases, benefiting neurosurgery residents. Material and Methods: This study utilized preoperative MRI and CT scans of two patients, one with a right frontal mass and the other with a pituitary adenoma. The scans were segmented to isolate the brain, mass, and vascular structures. The 3D models were integrated with MRI and CT data and examined on the Illumetry XR screen with ArSurgeon software. Surgical procedures were recorded and edited into 5-minute videos. Forty neurosurgery residents, split into two groups based on the training year, were provided with the scans and 3D models. After viewing the surgical video, the participants completed a 20-item survey. The survey results were analysed using IBM SPSS Statistics version 29.0. Result: Among the 40 participants (28 male, 12 female), half were in the first three years of training, and half had 3-5 years of experience. The AR-based neuronavigation system received an average motivation score of 8.4/10, an ease of use rating of 7.6/10, and an ergonomic design rating of 7.9/10. Participants also rated the system’s contribution to anatomical understanding and mastery at 8.3/10. Conclusion: The study showed that AR-based neuronavigation systems effectively enhance surgical education by motivating learners and improving anatomical knowledge. However, further improvements in ergonomics and design could enhance their utility in medical training.

Anahtar Kelimeler

• Augmented reality
• neurosurgery
• neuronavigation
• education

ARTIRILMIŞ GERÇEKLİK TABANLI NÖRONAVİGASYON SİSTEMİNİN NÖROŞİRÜRJİ EĞİTİMİNE ETKİSİ

Öz

Amaç: Teknolojiyi cerrahi eğitime entegre etmek öğrenme deneyimlerini geliştirir. ArSurgeon yazılımına sahip Illumetry XR ekranı gibi uygun fiyatlı genişletilmiş gerçeklik (XR) ekipmanı, ameliyat öncesi MR ve BT taramalarının dijital bir ortamda segmentasyonuna olanak tanıyarak tümörlerin ve çevresindeki anatominin 3D görselleştirmelerini sunar. Bu, uzamsal oryantasyonu ve cerrahi vakaların anlaşılmasını geliştirerek nöroşirürji asistanlarına fayda sağlar. Gereç ve Yöntem: Bu çalışmada sağ frontal kitle ve hipofiz adenomu olan iki hastanın preoperatif MR ve BT taramaları kullanıldı. Taramalar beyin, kitle ve vasküler yapıları izole etmek için segmentlere ayrıldı. 3D modeller MR ve BT verileriyle entegre edildi ve ArSurgeon yazılımı ile Illumetry XR ekranında incelendi. Cerrahi prosedürler kaydedildi ve beş dakikalık videolar halinde düzenlendi. Eğitim yılına göre iki gruba ayrılan 40 beyin cerrahisi asistanına taramalar ve 3D modeller verildi. Cerrahi videoyu izledikten sonra katılımcılar 20 maddelik bir anket doldurdu. Anket sonuçları IBM SPSS Statistics version 29.0. kullanılarak analiz edilmiştir. Bulgular: Kırk katılımcının (28 erkek, 12 kadın) yarısı eğitimlerinin ilk üç yılında, yarısı ise 3-5 yıllık deneyime sahipti. AR tabanlı nöronavigasyon sistemi ortalama 8,4/10 motivasyon puanı, 7,6/10 kullanım kolaylığı puanı ve 7,9/10 ergonomik tasarım puanı almıştır. Katılımcılar ayrıca sistemin anatomik anlayış ve ustalığa katkısını 8,3/10 olarak değerlendirmiştir. Sonuç: Çalışma, AR tabanlı nöronavigasyon sistemlerinin öğrencileri motive ederek ve anatomik bilgiyi geliştirerek cerrahi eğitimi etkili bir şekilde geliştirdiğini göstermektedir. Bununla birlikte, ergonomi ve tasarımdaki daha fazla iyileştirme, tıp eğitimindeki faydalarını artırabilir

Anahtar Kelimeler

• Artırılmış gerçeklik
• beyin cerrahisi
• nöronavigasyon
• eğitim

Kaynakça

1. Davids J, Manivannan S, Darzi A, Giannarou S, Ashrafian H, Marcus HJ. Simulation for skills training in neurosurgery: a systematic review, meta-analysis, and analysis of progressive scholarly acceptance. Neurosurg Rev 2021;44(4):1853-67. [CrossRef] google scholar
2. Hey G, Guyot M, Carter A, Lucke-Wold B. Augmented Reality in Neurosurgery: A New Paradigm for Training. Medicina (Kaunas) 2023;59(10). [CrossRef] google scholar
3. Eckert M, Volmerg JS, Friedrich CM. Augmented reality in medicine: systematic and bibliographic review. JMIR Mhealth Uhealth 2019;7(4):e10967. [CrossRef] google scholar
4. Dhar P, Rocks T, Samarasinghe RM, Stephenson G, Smith C. Augmented reality in medical education: students’ experiences and learning outcomes. Med Educ Online 2021;26(1):1953953. [CrossRef] google scholar
5. Curran VR, Xu X, Aydin MY, Meruvia-Pastor O. Use of Extended Reality in Medical Education: An Integrative Review. Med Sci Educ 2023;33(1):275-86. [CrossRef] google scholar
6. Barteit S, Lanfermann L, Bârnighausen T, Neuhann F, Beiersmann C. Augmented, Mixed, and Virtual Reality-Based Head-Mounted Devices for Medical Education: Systematic Review. JMIR Serious Games 2021;9(3):e29080. [CrossRef] google scholar
7. Iop A, El-Hajj VG, Gharios M, de Giorgio A, Monetti FM, Edström E, et al. Extended Reality in Neurosurgical Education: A Systematic Review. Sensors (Basel) 2022;22(16):60-7. [CrossRef] google scholar
8. Sawaya R, Bugdadi A, Azarnoush H, Winkler-Schwartz A, Alotaibi FE, Bajunaid K, et al. Virtual reality tumor resection: the force pyramid approach. Oper Neurosurg (Hagerstown) 2018;14(6):686-96. [CrossRef] google scholar

9. Sawaya R, Alsideiri G, Bugdadi A, Winkler-Schwartz A, Azarnoush H, Bajunaid K, et al. Development of a performance model for virtual reality tumor resections. J Neurosurg 2018;131(1):192-200. [CrossRef] google scholar
10. Wise J. Life as a neurosurgeon. Bmj 2020;368:m395. [CrossRef] google scholar
11. Kazemzadeh K, Akhlaghdoust M, Zali A. Advances in artificial intelligence, robotics, augmented and virtual reality in neurosurgery. Front Surg 2023;10:1241923. [CrossRef] google scholar
12. Meola A, Cutolo F, Carbone M, Cagnazzo F, Ferrari M, Ferrari V. Augmented reality in neurosurgery: a systematic review. Neurosurg Rev 2017;40(4):537-48. [CrossRef] google scholar
13. Satoh M, Nakajima T, Watanabe E, Kawai K. Augmented reality in stereotactic neurosurgery: Current Status and Issues. Neurol Med Chir (Tokyo) 2023;63(4):137-40. [CrossRef] google scholar
14. Aydoseli A, Unal TC, Kardes O, Doguc O, Dolas I, Adiyaman AE, et al. An Early Warning System Using Machine Learning for the Detection of Intracranial Hematomas in the Emergency Trauma Setting. Turk Neurosurg 2022;32(3):459-65. [CrossRef] google scholar
15. Murray NM, Unberath M, Hager GD, Hui FK. Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review. J Neurointerv Surg 2020;12(2):156-64. [CrossRef] google scholar
16. Moro C, Stromberga Z, Raikos A, Stirling A. The effectiveness of virtual and augmented reality in health sciences and medical anatomy. Anat Sci Educ 2017;10(6):549-59. [CrossRef] google scholar
17. Aasekjær K, Bj0rnâs B, Skivenes HK, Vik ES. Immersive Virtual Reality (VR) when learning anatomy in midwifery education: A pre-post pilot study. Eur J Midwifery 2024;8(August):48. [CrossRef] google scholar
18. Sinou N, Sinou N, Filippou D. Virtual Reality and Augmented Reality in Anatomy Education During COVID-19 Pandemic. Cureus 2023;15(2):e35170. [CrossRef] google scholar