Kwoh, YS, Hou, J, Jonckheere, EA, Hayati, S. A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery. IEEE Transactions on Biomedical Engineering [Internet]. 1988 Feb;35(2):153–60.10.1109/10.1354CrossRefGoogle ScholarPubMed

Clark, MC, Hall, LO, Goldgof, DB, Velthuizen, R, Murtagh, FR, Silbiger, MS. Automatic tumor segmentation using knowledge-based techniques. IEEE Transactions on Medical Imaging. 1998 Apr;17(2):187–201.10.1109/42.700731CrossRefGoogle ScholarPubMed

Prastawa, M, Bullitt, E, Ho, S, Gerig, G. A brain tumor segmentation framework based on outlier detection*1. Medical Image Analysis. 2004 Sep;8(3):275–83.10.1016/j.media.2004.06.007CrossRefGoogle Scholar

Havaei, M, Dutil, F, Pal, C, Larochelle, H, Jodoin, PM. A convolutional neural network approach to brain tumor segmentation. Lecture Notes in Computer Science. 2016 Jan; 195–208.CrossRefGoogle Scholar

Moses, DA, Metzger, SL, Liu, JR, Anumanchipalli, GK, Makin, JG, Sun, PF, et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. New England Journal of Medicine. 2021 Jul;385(3):217–27.10.1056/NEJMoa2027540CrossRefGoogle Scholar

Dundar, TT, Yurtsever, I, Pehlivanoglu, MK, Yildiz, U, Eker, A, Demir, MA, et al. Machine learning-based surgical planning for neurosurgery: Artificial intelligent approaches to the cranium. Frontiers in Surgery. 2022 Apr;9.10.3389/fsurg.2022.863633CrossRefGoogle Scholar

Karabacak, M, Margetis, K. Machine learning-based prediction of short-term adverse postoperative outcomes in cervical disc arthroplasty patients. World Neurosurgery [Internet]. 2023 Jun 15 [cited 2023 Dec 10];S1878-8750(23)007969. Available from: https://pubmed.ncbi.nlm.nih.gov/37330003/Google Scholar

McAvoy, M, Prieto, PC, Kaczmarzyk, JR, Fernández, IS, McNulty, J, Smith, T, et al. Classification of glioblastoma versus primary central nervous system lymphoma using convolutional neural networks. Scientific Reports. 2021 Jul;11(1).10.1038/s41598-021-94733-0CrossRefGoogle ScholarPubMed

Liew, SL, Lo, B, Donnelly, MR, Zavaliangos-Petropulu, Artemis, Jeong, JN, Barisano, G, et al. A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms. Scientific Data. 2022 Jun;9(1).10.1038/s41597-022-01401-7CrossRefGoogle ScholarPubMed

Boaro, A, Kaczmarzyk, JR, Kavouridis, VK, Harary, M, Mammi, M, Dawood, H, et al. Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice. Scientific Reports [Internet]. 2022 Sep 14 [cited 2023 Apr 10];12(1):15462.10.1038/s41598-022-19356-5CrossRefGoogle ScholarPubMed

Su, R, Zhang, D, Liu, J, Cheng, C. MSU-Net: Multi-Scale U-Net for 2D Medical image segmentation. Frontiers in Genetics. 2021 Feb; 12.10.3389/fgene.2021.639930CrossRefGoogle Scholar

Yap, MH, Pons, G, Marti, J, Ganau, S, Sentis, M, Zwiggelaar, R, et al. Automated breast ultrasound lesions detection using convolutional neural networks. IEEE Journal of Biomedical and Health Informatics. 2017 Jul;22(4):1218–26.Google ScholarPubMed

Candemir, S, Jaeger, S, Palaniappan, K, Musco, JP, Singh, RK, Xue, Zhiyun, et al. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Transactions on Medical Imaging. 2013;33(2):577–90.Google ScholarPubMed

Jaeger, S, Karargyris, A, Candemir, S, Folio, L, Siegelman, J, Callaghan, F, et al. Automatic tuberculosis screening using chest radiographs. IEEE Transactions on Medical Imaging. 2013;33(2):233–45.Google ScholarPubMed

Tschandl, P, Rosendahl, C, Kittler, H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data [Internet]. 2018 Aug;5(1).10.1038/sdata.2018.161CrossRefGoogle ScholarPubMed

Codella, N, Rotemberg, V, Tschandl, P, Celebi, ME, Dusza, S, Gutman, D, et al. Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the International Skin Imaging Collaboration (ISIC). arXiv:190203368 [cs] [Internet]. 2019 Mar 29.Google Scholar

Walsh, J, Othmani, A, Jain, M, Dev, S. Using U-Net Network for efficient brain tumor segmentation in MRI images. Healthcare Analytics. 2022 Aug;100098.10.1016/j.health.2022.100098CrossRefGoogle Scholar

Trebeschi, S, Drago, SG, Birkbak, NJ, Kurilova, I, Cǎlin, AM, Delli Pizzi, A, et al. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Annals of Oncology [Internet]. 2019 Jun;30(6):998–1004.10.1093/annonc/mdz108CrossRefGoogle ScholarPubMed

Jiang, LY, Liu, XC, Nejatian, NP, Nasir-Moin, M, Wang, D, Abidin, A, et al. Health system-scale language models are all-purpose prediction engines. Nature [Internet]. 2023 Jun;1–6. Available from: https://www.nature.com/articles/s41586-023-06160-y.Google Scholar

Mezger, U., Jendrewski, C., Bartels, M.. Navigation in surgery. Langenbecks Arch Surg. 2013; 398(4): 501–14.10.1007/s00423-013-1059-4CrossRefGoogle ScholarPubMed

Ma, L. and Baowei, F.. Comprehensive review of surgical microscopes: technology development and medical applications. J Biomed Opt. 2021; 26 (1): 010901.10.1117/1.JBO.26.1.010901CrossRefGoogle ScholarPubMed

Michalak, S. M., Rolston, J. D., Lawton, M. T.. Incidence and predictors of complications and mortality in cerebrovascular surgery: national trends from 2007 to 2012. Neurosurgery. 2016; 79 (2): 182–93.10.1227/NEU.0000000000001251CrossRefGoogle ScholarPubMed

Lakomkin, N., Stannard, B., Fogelson, J. L., et al. Comparison of surgical invasiveness and morbidity of adult spinal deformity surgery to other major operations. Spine J. 2021; 21 (11): 1784–92.10.1016/j.spinee.2021.07.013CrossRefGoogle ScholarPubMed

How many years of postgraduate training do surgical residents undergo? https://www.facs.org/for-medical-professionals/education/online-guide-to-choosing-a-surgical-residency/guide-to-choosing-a-surgical-residency-for-medical-students/faqs/training/. (Accessed June 11, 2023.)Google Scholar

Kelly, P. J.. Stereotactic imaging, surgical planning and computer-assisted resection of intracranial lesions: methods and results. Adv Tech Stand Neurosurg. 1990; 17: 77–118.10.1007/978-3-7091-6925-4_3CrossRefGoogle ScholarPubMed

In: Furht, B. (eds). Augmented Reality. Encyclopedia of Multimedia. Springer, Boston, MA (2006).10.1007/0-387-30038-4CrossRefGoogle Scholar

Hyman, I.. Filippo Brunelleschi. Encyclopedia Britannica, April 11, 2023. https://www.britannica.com/biography/Filippo-Brunelleschi (Accessed June 12, 2023).Google Scholar

Steinhaus, H.. Sur la Localisation au Moyen des Rayons X. Comptes Rendus de L’Acad. des Sci. 1938; 206: 1473–5.Google Scholar

Roberts, D. W., Strohbehn, J. W., Hatch, J. F., et al. A frameless stereotaxic integration of computerized tomographic imaging and the operating microscope. J Neurosurg. 1986; 65 (4): 545–9.10.3171/jns.1986.65.4.0545CrossRefGoogle ScholarPubMed

Edwards, P. J., Hawkes, D. J., Hill, D. L., et al. Augmentation of reality using an operating microscope for otolaryngology and neurosurgical guidance. J Image Guid Surg. 1995; 1 (3): 172–8.10.1002/(SICI)1522-712X(1995)1:3<172::AID-IGS7>3.0.CO;2-73.0.CO;2-7>CrossRefGoogle ScholarPubMed

Kawamata, T., Iseki, H., Shibasaki, T., et al. Endoscopic augmented reality navigation system for endonasal transsphenoidal surgery to treat pituitary tumors: technical note. Neurosurgery. 2002; 50 (6): 1393–7.Google ScholarPubMed

Zamorano, L., Vinas, F. C., Buciuc, R., et al. Advanced neurosurgical navigation using a robotic microscope integrated with an infrared-based system. computer-assisted neurosurgery. Springer, Tokyo (1997).Google Scholar

Mascitelli, J. R., Schlachter, L., Chartrain, A. G., et al. Navigation-linked heads-up display in intracranial surgery: early experience. Oper Neurosurg (Hagerstown). 2018; 15 (2): 184–93.Google ScholarPubMed

Fiani, B., Jarrah, R., Griepp, D. W., et al. The role of 3D exoscope systems in neurosurgery: an optical innovation. Cureus. 2021; 13 (6): e15878.Google ScholarPubMed

Guha, D., Alotaibi, N. M., Nguyen, N., et al. Augmented reality in neurosurgery: a review of current concepts and emerging applications. Can J Neurol Sci. 2017; 44 (3): 235–45.10.1017/cjn.2016.443CrossRefGoogle ScholarPubMed

Gerhardt, J., Sollmann, N., Hiepe, P., et al. Retrospective distortion correction of diffusion tensor imaging data by semi-elastic image fusion – evaluation by means of anatomical landmarks. Clin Neurol Neurosurg. 2019; 183: 105387.10.1016/j.clineuro.2019.105387CrossRefGoogle ScholarPubMed

Milgram, P., Takemura, H., Utsumi, A., et al. Augmented reality: a class of displays on the reality-virtuality continuum. Proc. SPIE 2351, Telemanipulator and Telepresence Technologies, (21 December 1995).Google Scholar

Keil, H. and Trapp, O.. Fluoroscopic imaging: new advances. Injury. 2022; 53 Suppl. 3: S8–15.10.1016/j.injury.2022.05.035CrossRefGoogle ScholarPubMed

Microsoft HoloLens 2. https://www.microsoft.com/en-us/hololens. (Accessed June 11, 2023.)Google Scholar

Magic Leap 2. https://www.magicleap.com/magic-leap-2. (Accessed June 11, 2023.)Google Scholar

Nabiyouni, M., Scerbo, S., Bowman, D. A., et al. Relative effects of real-world and virtual-world latency on an augmented reality training task: an AR simulation experiment. Front. ICT 2017; 3: 34.10.3389/fict.2016.00034CrossRefGoogle Scholar

Negwer, C., Hiepe, P., Meyer, B., et al. Elastic fusion enables fusion of intraoperative magnetic resonance imaging data with preoperative neuronavigation data. World Neurosurg. 2020; 142: e223–8.10.1016/j.wneu.2020.06.166CrossRefGoogle ScholarPubMed

Barak, T., Vetsa, S., Nadar, A., et al. Surgical strategies for older patients with glioblastoma. J Neurooncol. 2021; 155 (3): 255–64.Google ScholarPubMed

Bailey, T. and Durrant-Whyte, H.. Simultaneous Localisation and Mapping (SLAM): part II state of the art. IEEE Robotics & Automation Magazine 2006; 13 (3): 108–17.10.1109/MRA.2006.1678144CrossRefGoogle Scholar

Röntgen, W. C.. On a new kind or rays. Journal of the Franklin Institute 1896; 141; 3: 183–91.10.1016/S0016-0032(96)90066-6CrossRefGoogle Scholar

Why has France lost 80,000 hospital beds in 20 years? https://www.connexionfrance.com/article/French-news/Why-has-France-lost-80-000-hospital-beds-in-20-years. (Accessed June 12, 2023.)Google Scholar

Jean, W. C.. Virtual and augmented reality in neurosurgery: the evolution of its application and study designs. World Neurosurg. 2022; 161: 459–64.10.1016/j.wneu.2021.08.150CrossRefGoogle ScholarPubMed

Amorim-Leite, R, Remick, M, Welch, W, Abel, TJ. History of the network approach in epilepsy surgery. Neurosurg Clin N Am. 2020;31(3):301–8.10.1016/j.nec.2020.03.011CrossRefGoogle Scholar

Hanjani, K, Fatehi, M, Schmidt, N, Aghakhani, Y, Redekop, GJ. A history of diagnostic investigations in epilepsy surgery. Can J Neurol Sci. 2021;48(6):845–51.Google ScholarPubMed

Reif, PS, Strzelczyk, A, Rosenow, F. The history of invasive EEG evaluation in epilepsy patients. Seizure. 2016;41:191–5.10.1016/j.seizure.2016.04.006CrossRefGoogle ScholarPubMed

Akhoon, N. Precision medicine: a new paradigm in therapeutics. Int J Prev Med. 2021;12:12.10.4103/ijpvm.IJPVM_375_19CrossRefGoogle ScholarPubMed

Brew-Sam, N, Parkinson, A, Lueck, C, et al. The current understanding of precision medicine and personalised medicine in selected research disciplines: study protocol of a systematic concept analysis. BMJ Open. 2022;12(9):e060326.10.1136/bmjopen-2021-060326CrossRefGoogle ScholarPubMed

Feindel, W, Leblanc, R, De Almeida, AN. Epilepsy surgery: historical highlights 1909–2009. Epilepsia. 2009;50(s3):131–51.10.1111/j.1528-1167.2009.02043.xCrossRefGoogle ScholarPubMed

Stone, JL, Hughes, JR. Early history of electroencephalography and establishment of the American Clinical Neurophysiology Society. J Clin Neurophysiol. 2013;30(1):28–44.10.1097/WNP.0b013e31827edb2dCrossRefGoogle ScholarPubMed

Yamamoto, T. Recent advancement of technologies and the transition to new concepts in epilepsy surgery. Neurol Med Chir (Tokyo). 2020;60(12):581–93.10.2176/nmc.ra.2020-0197CrossRefGoogle ScholarPubMed

Meador, KJ, Loring, DW, Flanigin, HF. History of epilepsy surgery. J Epilepsy. 1989;2(1):21–5.10.1016/0896-6974(89)90054-6CrossRefGoogle Scholar

Miller, KJ, Fine, AL. Decision-making in stereotactic epilepsy surgery. Epilepsia. 2022;63(11):2782–801.10.1111/epi.17381CrossRefGoogle ScholarPubMed

Mohamed, A, Wyllie, E, Ruggieri, P, et al. Temporal lobe epilepsy due to hippocampal sclerosis in pediatric candidates for epilepsy surgery. Neurology. 2001;56(12):1643–9.10.1212/WNL.56.12.1643CrossRefGoogle ScholarPubMed

Morrison, G, Duchowny, M, Resnick, T, et al. Epilepsy surgery in childhood: a report of 79 patients. Pediatr Neurosurg. 2008;18(5–6):291–7.Google Scholar

Paolicchi, JM, Jayakar, P, Dean, P, et al. Predictors of outcome in pediatric epilepsy surgery. Neurology. 2000;54(3):642.10.1212/WNL.54.3.642CrossRefGoogle ScholarPubMed

Prats, AR, Morrison, G, Wolf, AL. Focal cortical resections for the treatment of extratemporal epilepsy in children. Neurosurg Clin N Am. 1995;6(3):533–40.10.1016/S1042-3680(18)30447-9CrossRefGoogle ScholarPubMed

Adelson, PD, Black, PM, Madsen, JR, et al. Use of subdural grids and strip electrodes to identify a seizure focus in children. Pediatr Neurosurg. 1995;22(4):174–80.10.1159/000120898CrossRefGoogle ScholarPubMed

Gonzalez-Martinez, J, Bulacio, J, Alexopoulos, A, Jehi, L, Bingaman, W, Najm, I. Stereoelectroencephalography in the “difficult to localize” refractory focal epilepsy: early experience from a North American epilepsy center. Epilepsia. 2013;54(2):323–30.10.1111/j.1528-1167.2012.03672.xCrossRefGoogle ScholarPubMed

Gonzalez-Martinez, J, Vadera, S, Mullin, J, et al. Robot-assisted stereotactic laser ablation in medically intractable epilepsy: operative technique. Neurosurgery. 2014;10 Suppl 2:167–72; discussion 172–3.Google Scholar

Gonzalez-Martinez, JA. The stereo-electroencephalography: the epileptogenic zone. Journal of Clinical Neurophysiology. 2016;33(6):522–9.10.1097/WNP.0000000000000327CrossRefGoogle ScholarPubMed

Nelson, JH, Brackett, SL, Oluigbo, CO, Reddy, SK. Robotic stereotactic assistance (ROSA) for pediatric epilepsy: a single-center experience of 23 consecutive cases. Children (Basel). 2020;7(8).Google ScholarPubMed

Guerrini, R, Scerrati, M, Rubboli, G, et al. Overview of presurgical assessment and surgical treatment of epilepsy from the Italian League Against Epilepsy. Epilepsia. 2013;54 Suppl 7:35–48.10.1111/epi.12308CrossRefGoogle ScholarPubMed

Stone, SSD, Park, EH, Bolton, J, et al. Interictal connectivity revealed by Granger Analysis of stereoelectroencephalography: association with ictal onset zone, resection, and outcome. Neurosurgery. 2022;91(4):583–9.10.1227/neu.0000000000002079CrossRefGoogle ScholarPubMed

Park, EH, Madsen, JR. Granger Causality analysis of interictal iEEG predicts seizure focus and ultimate resection. Neurosurgery. 2018;82(1):99–109.10.1093/neuros/nyx195CrossRefGoogle ScholarPubMed

Belohlavkova, A, Jezdik, P, Jahodova, A, et al. Evolution of pediatric epilepsy surgery program over 2000–2017: improvement of care? Eur J Paediatr Neurol. 2019;23(3):456–65.10.1016/j.ejpn.2019.04.002CrossRefGoogle ScholarPubMed

Simon, SL, Telfeian, A, Duhaime, AC. Complications of invasive monitoring used in intractable pediatric epilepsy. Pediatr Neurosurg. 2003;38(1):47–52.10.1159/000067555CrossRefGoogle ScholarPubMed

Hader, WJ, Tellez-Zenteno, J, Metcalfe, A, et al. Complications of epilepsy surgery: a systematic review of focal surgical resections and invasive EEG monitoring. Epilepsia. 2013;54(5):840–7.10.1111/epi.12161CrossRefGoogle ScholarPubMed

Rotenberg, K. Bayesian Localization of the Seizure Onset Zone: A Theoretical Framework for Surgical Decision Making. Bachelor’s thesis, Harvard University; 2023.Google Scholar

Hoogteijling, S, Zijlmans, M. Deep learning for epileptogenic zone delineation from the invasive EEG: challenges and lookouts. Brain Comm. 2021;4(1).Google ScholarPubMed

Mirchi, N, Warsi, NM, Zhang, F, et al. Decoding intracranial EEG with machine learning: a systematic review. Front Hum Neurosci. 2022;16:913777.10.3389/fnhum.2022.913777CrossRefGoogle ScholarPubMed

Andrzejak, RG, Schindler, KA, Rummel, C. Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys Rev E Stat Nonlin Soft Matter Phys. 2012;86 4 Pt 2:046206.10.1103/PhysRevE.86.046206CrossRefGoogle ScholarPubMed

Andrzejak, RG, Lehnertz, K, Mormann, F, Rieke, C, David, P, Elger, CE. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys Rev E Stat Nonlin Soft Matter Phys. 2001;64(6 Pt 1):061907.10.1103/PhysRevE.64.061907CrossRefGoogle ScholarPubMed

Jehi, L. Machine learning for precision epilepsy surgery. Epilepsy Currents. 2023;23(2):78–83.10.1177/15357597221150055CrossRefGoogle ScholarPubMed

Jacobs, J, Staba, R, Asano, E, et al. High-frequency oscillations (HFOs) in clinical epilepsy. Prog Neurobiol. 2012;98(3):302–15.10.1016/j.pneurobio.2012.03.001CrossRefGoogle ScholarPubMed

Munia, TTK, Aviyente, S. Time-frequency based phase-amplitude coupling measure for neuronal oscillations. Sci Rep. 2019;9(1):12441.10.1038/s41598-019-48870-2CrossRefGoogle ScholarPubMed

Yuste, R, Goering, S, Arcas, BAY, et al. Four ethical priorities for neurotechnologies and AI. Nature. 2017;551(7679):159–63.10.1038/551159aCrossRefGoogle ScholarPubMed

Asplund, M. Accessing the brain with soft deployable electrocorticography arrays. Sci Robot. 2023;8(78):eadg2785.10.1126/scirobotics.adg2785CrossRefGoogle ScholarPubMed

Song, S, Fallegger, F, Trouillet, A, Kim, K, Lacour, SP Deployment of an electrocorticography system with a soft robotic actuator. Sci Robot. 2023;8(78):eadd1002.10.1126/scirobotics.add1002CrossRefGoogle ScholarPubMed