Artificial Intelligence for neurosurgery in Spain

• A Data-Centric Approach to Deep Learning for Brain Metastasis Analysis at MRI
• Current Concepts on Imaging and Artificial Intelligence of Osteosarcopenia in the Aging Spine - A Review for Spinal Surgeons by the SRS Adult Spinal Deformity Task Force on Senescence
• Evaluation of techniques for automated classification and artery quantification of the circle of Willis on TOF-MRA images: The CROWN challenge
• Multinational Attitudes Toward AI in Health Care and Diagnostics Among Hospital Patients
• Use of artificial intelligence in the management of stroke: scoping review
• AI-Driven Advances in Parkinson's Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes
• Global, Regional, and National Burden of Nontraumatic Subarachnoid Hemorrhage: The Global Burden of Disease Study 2021
• SLIMBRAIN database: A multimodal image database of in vivo human brains for tumour detection

Here are some notable institutions and projects in Spain that are integrating AI into neurosurgery and related fields:

Hospital Clínic de Barcelona:

Known for its advanced research in neurosurgery and the use of AI in various aspects of patient care. The hospital has been involved in projects focusing on AI for imaging and predictive analytics in neurosurgery. Hospital Universitario La Paz (Madrid):

This hospital is part of various research initiatives exploring the use of AI in neurosurgery, including improving surgical planning and postoperative monitoring. Barcelona Supercomputing Center (BSC):

BSC collaborates with medical institutions to apply AI and high-performance computing in healthcare. Their projects include using AI for analyzing complex medical images and enhancing diagnostic tools. Instituto de Salud Carlos III (ISCIII):

A major research institution in Spain that supports various projects involving AI in healthcare. They are involved in funding and coordinating research that includes AI applications in neurosurgery. Hospital Universitario de la Ribera (Alzira):

This hospital has been working on AI projects related to improving surgical precision and patient outcomes through the use of advanced imaging and data analysis tools. Fundación Teófilo Hernando:

Focuses on innovation in medical technology, including AI applications in neurosurgery. They support research and development in this field to advance surgical techniques and patient care. These institutions and projects highlight Spain's active role in integrating AI into neurosurgery and related fields, contributing to advancements in medical technology and patient care.

Department of Neurosurgery, Río Hortega University Hospital, Spain.

In this work, a method was developed and evaluated that classifies the OS into three classes - “short”, “medium” and “long”. For this purpose, the four MRI sequences of a person were corrected using bias-field correction and merged into one image. The pipeline was realized by a bagging model using 5-fold cross-validation and the ResNet50 architecture.

Results: The best model was able to achieve an F1-score of 0.51 and an accuracy of 0.67. In addition, this work enabled a largely clear differentiation of the “short” and “long” classes, which offers high clinical significance as decision support.

Conclusion: Automated analysis of MRI scans using deep learning-based image classification has the potential to enable accurate OS prediction in glioblastomas. ¹⁾

The study represents a promising step towards using AI for predicting glioblastoma patient survival based on MRI data. While the approach is innovative and the clinical relevance is high, the moderate performance metrics, lack of detailed analysis on class imbalance, and model interpretability issues highlight areas for further development. Future work should focus on improving model accuracy, addressing class imbalance, and enhancing the interpretability of predictions to facilitate clinical integration.

5: García-García S, Cepeda S, Arrese I, Sarabia R. A Fully Automated Pipeline Using Swin Transformers for Deep Learning-Based Blood Segmentation on Head Computed Tomography Scans After Aneurysmal Subarachnoid Hemorrhage. World Neurosurg. 2024 Aug 5:S1878-8750(24)01357-3. doi: 10.1016/j.wneu.2024.07.216. Epub ahead of print. PMID: 39111661.

The study presents a promising fully automated tool for blood segmentation in SAH patients, utilizing advanced transformer-based deep learning architecture. The high accuracy, robustness, and potential for real-time application are significant strengths. However, considerations regarding computational demands, clinical integration, and comparative analysis with existing methods would enhance the understanding of the model's practical utility and limitations. Further research and validation across diverse clinical settings are necessary to fully establish the tool's reliability and effectiveness in routine practice.

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10: Ben Pazi H, Jahashan S, Har Nof S, Zibman S, Yanai-Kohelet O, Prigan L, Intrator N, Bornstein NM, Ribo M. Pre-hospital stroke monitoring past, present, and future: a perspective. Front Neurol. 2024 Mar 22;15:1341170. doi: 10.3389/fneur.2024.1341170. PMID: 38585364; PMCID: PMC10995241.

11: Giannoukakos S, D'Ambrosi S, Koppers-Lalic D, Gómez-Martín C, Fernandez A, Hackenberg M. Assessing the complementary information from an increased number of biologically relevant features in liquid biopsy-derived RNA-Seq data. Heliyon. 2024 Mar 12;10(6):e27360. doi: 10.1016/j.heliyon.2024.e27360. PMID: 38515664; PMCID: PMC10955244.

12: Stengel FC, Stienen MN, Ivanov M, Gandía-González ML, Raffa G, Ganau M, Whitfield P, Motov S. Can AI pass the written European Board Examination in Neurological Surgery? - Ethical and practical issues. Brain Spine. 2024 Feb 13;4:102765. doi: 10.1016/j.bas.2024.102765. PMID: 38510593; PMCID: PMC10951784.

13: Tabouret E, Furtner J, Graillon T, Silvani A, Le Rhun E, Soffietti R, Lombardi G, Sepúlveda-Sánchez JM, Brandal P, Bendszus M, Golfinopoulos V, Gorlia T, Weller M, Sahm F, Wick W, Preusser M. 3D volume growth rate evaluation in the EORTC-BTG-1320 clinical trial for recurrent WHO grade 2 and 3 meningiomas. Neuro Oncol. 2024 Jul 5;26(7):1302-1309. doi: 10.1093/neuonc/noae037. PMID: 38452246; PMCID: PMC11226865.

14: González-González MA, Conde SV, Latorre R, Thébault SC, Pratelli M, Spitzer NC, Verkhratsky A, Tremblay MÈ, Akcora CG, Hernández-Reynoso AG, Ecker M, Coates J, Vincent KL, Ma B. Bioelectronic Medicine: a multidisciplinary roadmap from biophysics to precision therapies. Front Integr Neurosci. 2024 Feb 19;18:1321872. doi: 10.3389/fnint.2024.1321872. PMID: 38440417; PMCID: PMC10911101.

15: Thenier-Villa JL, Martínez-Ricarte FR, Figueroa-Vezirian M, Arikan-Abelló F. Glioblastoma Pseudoprogression Discrimination Using Multiparametric Magnetic Resonance Imaging, Principal Component Analysis, and Supervised and Unsupervised Machine Learning. World Neurosurg. 2024 Mar;183:e953-e962. doi: 10.1016/j.wneu.2024.01.074. Epub 2024 Jan 20. PMID: 38253179.

16: García-García S, Cepeda S, Müller D, Mosteiro A, Torné R, Agudo S, de la Torre N, Arrese I, Sarabia R. Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan. Brain Sci. 2023 Dec 22;14(1):10. doi: 10.3390/brainsci14010010. PMID: 38248225; PMCID: PMC10812955.

17: Yang K, Musio F, Ma Y, Juchler N, Paetzold JC, Al-Maskari R, Höher L, Li HB, Hamamci IE, Sekuboyina A, Shit S, Huang H, Prabhakar C, de la Rosa E, Waldmannstetter D, Kofler F, Navarro F, Menten M, Ezhov I, Rueckert D, Vos I, Ruigrok Y, Velthuis B, Kuijf H, Hämmerli J, Wurster C, Bijlenga P, Westphal L, Bisschop J, Colombo E, Baazaoui H, Makmur A, Hallinan J, Wiestler B, Kirschke JS, Wiest R, Montagnon E, Letourneau-Guillon L, Galdran A, Galati F, Falcetta D, Zuluaga MA, Lin C, Zhao H, Zhang Z, Ra S, Hwang J, Park H, Chen J, Wodzinski M, Müller H, Shi P, Liu W, Ma T, Yalçin C, Hamadache RE, Salvi J, Llado X, Lal- Trehan Estrada UM, Abramova V, Giancardo L, Oliver A, Liu J, Huang H, Cui Y, Lin Z, Liu Y, Zhu S, Patel TR, Tutino VM, Orouskhani M, Wang H, Mossa-Basha M, Zhu C, Rokuss MR, Kirchhoff Y, Disch N, Holzschuh J, Isensee F, Maier-Hein K, Sato Y, Hirsch S, Wegener S, Menze B. Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA. ArXiv [Preprint]. 2024 Apr 29:arXiv:2312.17670v3. PMID: 38235066; PMCID: PMC10793481.

18: Mensah GA, Fuster V, Murray CJL, Roth GA; Global Burden of Cardiovascular Diseases and Risks Collaborators. Global Burden of Cardiovascular Diseases and Risks, 1990-2022. J Am Coll Cardiol. 2023 Dec 19;82(25):2350-2473. doi: 10.1016/j.jacc.2023.11.007. PMID: 38092509; PMCID: PMC7615984.

19: Groppa S, Gonzalez-Escamilla G, Tinkhauser G, Baqapuri HI, Sajonz B, Wiest C, Pereira J, Herz DM, Dold MR, Bange M, Ciolac D, Almeida V, Neuber J, Mirzac D, Martín-Rodríguez JF, Dresel C, Muthuraman M, Adarmes Gomez AD, Navas M, Temiz G, Gunduz A, Rotaru L, Winter Y, Schuurman R, Contarino MF, Glaser M, Tangermann M, Leentjens AFG, Mir P, Torres Diaz CV, Karachi C, Linden DEJ, Tan H, Coenen VA; EuroDBS. Perspectives of Implementation of Closed-Loop Deep Brain Stimulation: From Neurological to Psychiatric Disorders. Stereotact Funct Neurosurg. 2024;102(1):40-54. doi: 10.1159/000535114. Epub 2023 Dec 12. PMID: 38086346.

20: Pemberton HG, Wu J, Kommers I, Müller DMJ, Hu Y, Goodkin O, Vos SB, Bisdas S, Robe PA, Ardon H, Bello L, Rossi M, Sciortino T, Nibali MC, Berger MS, Hervey-Jumper SL, Bouwknegt W, Van den Brink WA, Furtner J, Han SJ, Idema AJS, Kiesel B, Widhalm G, Kloet A, Wagemakers M, Zwinderman AH, Krieg SM, Mandonnet E, Prados F, de Witt Hamer P, Barkhof F, Eijgelaar RS. Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms. Sci Rep. 2023 Nov 2;13(1):18911. doi: 10.1038/s41598-023-44794-0. PMID: 37919354; PMCID: PMC10622563.

21: Rocca A, Lehner C, Wafula-Wekesa E, Luna E, Fernández-Cornejo V, Abarca- Olivas J, Soto-Sánchez C, Fernández-Jover E, González-López P. Robot-assisted implantation of a microelectrode array in the occipital lobe as a visual prosthesis: technical note. J Neurosurg. 2023 Oct 27;140(4):1169-1176. doi: 10.3171/2023.8.JNS23772. PMID: 37890180.

22: Le Rhun E, Weller M, van den Bent M, Brandsma D, Furtner J, Rudà R, Schadendorf D, Seoane J, Tonn JC, Wesseling P, Wick W, Minniti G, Peters S, Curigliano G, Preusser M; EANO Guidelines Committee and ESMO Guidelines Committee. Electronic address: clinicalguidelines@esmo.org. Leptomeningeal metastasis from solid tumours: EANO-ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. ESMO Open. 2023 Oct;8(5):101624. doi: 10.1016/j.esmoop.2023.101624. Epub 2023 Sep 19. PMID: 37863528; PMCID: PMC10619142.

23: Poldrack RA, Markiewicz CJ, Appelhoff S, Ashar YK, Auer T, Baillet S, Bansal S, Beltrachini L, Benar CG, Bertazzoli G, Bhogawar S, Blair RW, Bortoletto M, Boudreau M, Brooks TL, Calhoun VD, Castelli FM, Clement P, Cohen AL, Cohen-Adad J, D'Ambrosio S, de Hollander G, de la Iglesia-Vayá M, de la Vega A, Delorme A, Devinsky O, Draschkow D, Duff EP, DuPre E, Earl E, Esteban O, Feingold FW, Flandin G, Galassi A, Gallitto G, Ganz M, Gau R, Gholam J, Ghosh SS, Giacomel A, Gillman AG, Gleeson P, Gramfort A, Guay S, Guidali G, Halchenko YO, Handwerker DA, Hardcastle N, Herholz P, Hermes D, Honey CJ, Innis RB, Ioanas HI, Jahn A, Karakuzu A, Keator DB, Kiar G, Kincses B, Laird AR, Lau JC, Lazari A, Legarreta JH, Li A, Li X, Love BC, Lu H, Marcantoni E, Maumet C, Mazzamuto G, Meisler SL, Mikkelsen M, Mutsaerts H, Nichols TE, Nikolaidis A, Nilsonne G, Niso G, Norgaard M, Okell TW, Oostenveld R, Ort E, Park PJ, Pawlik M, Pernet CR, Pestilli F, Petr J, Phillips C, Poline JB, Pollonini L, Raamana PR, Ritter P, Rizzo G, Robbins KA, Rockhill AP, Rogers C, Rokem A, Rorden C, Routier A, Saborit-Torres JM, Salo T, Schirner M, Smith RE, Spisak T, Sprenger J, Swann NC, Szinte M, Takerkart S, Thirion B, Thomas AG, Torabian S, Varoquaux G, Voytek B, Welzel J, Wilson M, Yarkoni T, Gorgolewski KJ. The Past, Present, and Future of the Brain Imaging Data Structure (BIDS). ArXiv [Preprint]. 2024 Jan 9:arXiv:2309.05768v2. PMID: 37744469; PMCID: PMC10516110.

24: Castellano MS, Contreras-McKay I, Neyem A, Farfán E, Inzunza O, Ottone NE, Del Sol M, Alario-Hoyos C, Alvarado MS, Tubbs RS. Empowering human anatomy education through gamification and artificial intelligence: An innovative approach to knowledge appropriation. Clin Anat. 2024 Jan;37(1):12-24. doi: 10.1002/ca.24074. Epub 2023 Jul 15. PMID: 37453079.

25: Moawad AW, Janas A, Baid U, Ramakrishnan D, Saluja R, Ashraf N, Jekel L, Amiruddin R, Adewole M, Albrecht J, Anazodo U, Aneja S, Anwar SM, Bergquist T, Calabrese E, Chiang V, Chung V, Conte GMM, Dako F, Eddy J, Ezhov I, Familiar A, Farahani K, Iglesias JE, Jiang Z, Johanson E, Kazerooni AF, Kofler F, Krantchev K, LaBella D, Van Leemput K, Li HB, Linguraru MG, Link KE, Liu X, Maleki N, Meier Z, Menze BH, Moy H, Osenberg K, Piraud M, Reitman Z, Shinohara RT, Tahon NH, Nada A, Velichko YS, Wang C, Wiestler B, Wiggins W, Shafique U, Willms K, Avesta A, Bousabarah K, Chakrabarty S, Gennaro N, Holler W, Kaur M, LaMontagne P, Lin M, Lost J, Marcus DS, Maresca R, Merkaj S, Nada A, Pedersen GC, von Reppert M, Sotiras A, Teytelboym O, Tillmans N, Westerhoff M, Youssef A, Godfrey D, Floyd S, Rauschecker A, Villanueva-Meyer J, Pflüger I, Cho J, Bendszus M, Brugnara G, Cramer J, Perez-Carillo GJG, Johnson DR, Kam A, Kwan BYM, Lai L, Lall NU, Memon F, Patro SN, Petrovic B, So TY, Thompson G, Wu L, Schrickel EB, Bansal A, Barkhof F, Besada C, Chu S, Druzgal J, Dusoi A, Farage L, Feltrin F, Fong A, Fung SH, Gray RI, Ikuta I, Iv M, Postma AA, Mahajan A, Joyner D, Krumpelman C, Letourneau-Guillon L, Lincoln CM, Maros ME, Miller E, Morón F, Nimchinsky EA, Ozsarlak O, Patel U, Rohatgi S, Saha A, Sayah A, Schwartz ED, Shih R, Shiroishi MS, Small JE, Tanwar M, Valerie J, Weinberg BD, White ML, Young R, Zohrabian VM, Azizova A, Brüßeler MMT, Fehringer P, Ghonim M, Ghonim M, Gkampenis A, Okar A, Pasquini L, Sharifi Y, Singh G, Sollmann N, Soumala T, Taherzadeh M, Yordanov N, Vollmuth P, Foltyn-Dumitru M, Malhotra A, Abayazeed AH, Dellepiane F, Lohmann P, Pérez-García VM, Elhalawani H, Al-Rubaiey S, Armindo RD, Ashraf K, Asla MM, Badawy M, Bisschop J, Lomer NB, Bukatz J, Chen J, Cimflova P, Corr F, Crawley A, Deptula L, Elakhdar T, Shawali IH, Faghani S, Frick A, Gulati V, Haider MA, Hierro F, Dahl RH, Jacobs SM, Hsieh KJ, Kandemirli SG, Kersting K, Kida L, Kollia S, Koukoulithras I, Li X, Abouelatta A, Mansour A, Maria-Zamfirescu RC, Marsiglia M, Mateo-Camacho YS, McArthur M, McDonnell O, McHugh M, Moassefi M, Morsi SM, Muntenu A, Nandolia KK, Naqvi SR, Nikanpour Y, Alnoury M, Nouh AMA, Pappafava F, Patel MD, Petrucci S, Rawie E, Raymond S, Roohani B, Sabouhi S, Sanchez-Garcia LM, Shaked Z, Suthar PP, Altes T, Isufi E, Dhermesh Y, Gass J, Thacker J, Tarabishy AR, Turner B, Vacca S, Vilanilam GK, Warren D, Weiss D, Willms K, Worede F, Yousry S, Lerebo W, Aristizabal A, Karargyris A, Kassem H, Pati S, Sheller M, Bakas S, Rudie JD, Aboian M. The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI. ArXiv [Preprint]. 2024 Jun 17:arXiv:2306.00838v2. PMID: 37396600; PMCID: PMC10312806.

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29: Mehta R, Filos A, Baid U, Sako C, McKinley R, Rebsamen M, Dätwyler K, Meier R, Radojewski P, Murugesan GK, Nalawade S, Ganesh C, Wagner B, Yu FF, Fei B, Madhuranthakam AJ, Maldjian JA, Daza L, Gómez C, Arbeláez P, Dai C, Wang S, Reynaud H, Mo Y, Angelini E, Guo Y, Bai W, Banerjee S, Pei L, Ak M, Rosas- González S, Zemmoura I, Tauber C, Vu MH, Nyholm T, Löfstedt T, Ballestar LM, Vilaplana V, McHugh H, Maso Talou G, Wang A, Patel J, Chang K, Hoebel K, Gidwani M, Arun N, Gupta S, Aggarwal M, Singh P, Gerstner ER, Kalpathy-Cramer J, Boutry N, Huard A, Vidyaratne L, Rahman MM, Iftekharuddin KM, Chazalon J, Puybareau E, Tochon G, Ma J, Cabezas M, Llado X, Oliver A, Valencia L, Valverde S, Amian M, Soltaninejad M, Myronenko A, Hatamizadeh A, Feng X, Dou Q, Tustison N, Meyer C, Shah NA, Talbar S, Weber MA, Mahajan A, Jakab A, Wiest R, Fathallah-Shaykh HM, Nazeri A, Milchenko M, Marcus D, Kotrotsou A, Colen R, Freymann J, Kirby J, Davatzikos C, Menze B, Bakas S, Gal Y, Arbel T. QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results. J Mach Learn Biomed Imaging. 2022 Aug;2022:https://www.melba-journal.org/papers/2022:026.html. PMID: 36998700; PMCID: PMC10060060.

30: Cepeda S, Luppino LT, Pérez-Núñez A, Solheim O, García-García S, Velasco- Casares M, Karlberg A, Eikenes L, Sarabia R, Arrese I, Zamora T, Gonzalez P, Jiménez-Roldán L, Kuttner S. Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI. Cancers (Basel). 2023 Mar 22;15(6):1894. doi: 10.3390/cancers15061894. PMID: 36980783; PMCID: PMC10047582.

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