KI in der Wirbelsäulenchirurgie: Die Macht der Vorhersage

Zusammenfassung

Die Kunst der Vorhersage ist seit jeher ein wesentlicher Bestandteil des ärztlichen Handelns. In der frühen Geschichte eher intuitiv und mit übersinnlichen verknüpft, vertrauen Patienten heute auf unsere wissenschaftlich-medizinischen Kenntnisse, um verlässliche medizinische Vorhersagen zu erhalten. Dabei gilt es Wahrscheinlichkeiten einzuschätzen, ob ein bestimmter Gesundheitszustand vorliegt – Diagnostik, und ob ein bestimmtes Ereignis in der Zukunft eintreten wird – Prognostik.

Künstliche Intelligenz (KI) ist gerade dabei eine unschlagbare Vorhersage-Kompetenz in der Medizin zu entwickeln – ein Potenzial, das wir zum Wohle unserer Patienten nutzen können. Gleichzeitig stellt diese Entwicklung eine Herausforderung für das ärztliche Selbstverständnis dar.

Diese narrative Übersichtsarbeit beleuchtet die Rolle von KI in der Wirbelsäulenchirurgie, mit besonderem Fokus auf die Vorhersage klinischer Ergebnisse. Ziel ist es, dem Leser ein Verständnis der aktuellen Entwicklungen in der KI zu vermitteln, sie einzuordnen und ihre Bedeutung für die Zukunft unseres Berufsbildes zu reflektieren.

Abstract

The art of prediction has always been an essential part of medical practice. In earlier times, it was more intuitive and linked to the supernatural, but today, patients trust in our scientific-medical knowledge to receive reliable medical predictions. This involves assessing the probability of a certain health condition – diagnostics – and whether a certain event will occur in the future – prognostics.

Artificial intelligence (AI) is currently developing an unparalleled predictive capability in medicine – a potential that we can harness for the benefit of our patients. At the same time, this development presents a challenge to the very essence of the medical profession.

This narrative review explores the role of AI in spine surgery, with a particular focus on the prediction of clinical outcomes. The goal is to enable the reader to understand and contextualize the current advancements in AI, while reflecting on their implications for the future of our profession.

Schlüsselwörter

Künstliche Intelligenz in der Wirbelsäulenchirurgie - prädiktive Modelle in der Medizin - Machine Learning in der Wirbelsäulenchirurgie - personalisierte Medizin durch KI - Multimodale KI-Systeme in der Chirurgie

Keywords

artificial intelligence in spine surgery - predictive models in medicine - machine learning in spine surgery - personalized medicine with AI - multimodal AI systems in surgery

• Literatur

• 1 Snyder CF, Aaronson NK. Use of patient-reported outcomes in clinical practice. Lancet 2009; 374: 369-370
• 2 Gambhir SS, Ge TJ, Vermesh O. et al. Continuous health monitoring: An opportunity for precision health. Sci Transl Med 2021; 13
• 3 Babu M, Snyder M. Multi-Omics Profiling for Health. Mol Cell Proteomics 2023; 22
• 4 Shaw JD, McEntarfer R, Ferrel J. et al. What Does Your PROMIS Score Mean? Improving the Utility of Patient-Reported Outcomes at the Point of Care. Global Spine J 2020; 12: 588-597
• 5 Bombardier C. Outcome assessments in the evaluation of treatment of spinal disorders: summary and general recommendations. Spine 2000; 25: 3100-3103
• 6 Greenberg JK, Javeed S, Zhang JK. et al. Current and future applications of mobile health technology for evaluating spine surgery patients: a review. J Neurosurg Spine 2023; 38: 617-626
• 7 Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol 2017; 18: 83
• 8 Haddad S, Pizones J, Raganato R. et al. Future Data Points to Implement in Adult Spinal Deformity Assessment for Artificial Intelligence Modeling Prediction: The Importance of the Biological Dimension. Int J Spine Surg 2023; 17
• 9 Singh M, Kumar A, Khanna NN. et al. Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review. EClinicalMedicine 2024; 73
• 10 Challen R, Denny J, Pitt M. et al. Artificial intelligence, bias and clinical safety. BMJ Quality & Safety 2019; 28: 231-237
• 11 Goldberg CB, Adams L, Blumenthal D. et al. To Do No Harm — and the Most Good — with AI in Health Care. NEJM AI 2024; 1: 13
• 12 Garnelo M, Shanahan M. Reconciling deep learning with symbolic artificial intelligence: representing objects and relations. Current Opinion in Behavioral Sciences 2019; 29: 17-23
• 13 Chang M, Canseco JA, Nicholson KJ. et al. The Role of Machine Learning in Spine Surgery: The Future Is Now. Front Surg 2020; 7: 54
• 14 Lee NJ, Lombardi JM, Lehman RA. Artificial Intelligence and Machine Learning Applications in Spine Surgery. Int J Spine Surg 2023; 17: 18-25
• 15 Silver D, Hubert T, Schrittwieser J. et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science 2018; 362: 1140-1144
• 16 Emani S, Swaminathan A, Grobman B. et al. Critically reading machine learning literature in neurosurgery: a reader’s guide and checklist for appraising prediction models. Neurosurg Focus 2023; 54: E3
• 17 Polotskaya K, Muñoz-Valencia CS, Rabasa A. et al. Bayesian Networks for the Diagnosis and Prognosis of Diseases: A Scoping Review. Mach Learn Knowl Extr 2024; 6: 1243-1262
• 18 Abbott D. Model Ensembles. Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst. Indianapolis: John Wiley & Sons; 2014. ISBN: 978-1-118-72796-6
• 19 Saravi B, Hassel F, Ülkümen S. et al. Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models. J Pers Med 2022; 12: 509
• 20 Lopez CD, Boddapati V, Lombardi JM. et al. Artificial Learning and Machine Learning Applications in Spine Surgery: A Systematic Review. Global Spine J 2022; 12: 1561-1572
• 21 Ghanem M, Ghaith AK, El-Hajj VG. et al. Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A Systematic Review. Brain Sci 2023; 13: 1723
• 22 Tangsrivimol JA, Schonfeld E, Zhang M. et al. Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future. Diagnostics (Basel) 2023; 13: 2429
• 23 Tragaris T, Benetos IS, Vlamis J. et al. Machine Learning Applications in Spine Surgery. Cureus 2023; 15
• 24 Katsos K, Johnson SE, Ibrahim S. et al. Current Applications of Machine Learning for Spinal Cord Tumors. Life (Basel) 2023; 13: 520
• 25 Constant C, Aubin CE, Kremers HM. et al. The use of deep learning in medical imaging to improve spine care: A scoping review of current literature and clinical applications. N Am Spine Soc J 2023; 15
• 26 Titov O, Bykanov A, Pitskhelauri D. Neurosurgical skills analysis by machine learning models: systematic review. Neurosurg Rev 2023; 46: 121
• 27 Liu RW, Ong W, Makmur A. et al. Application of Artificial Intelligence Methods on Osteoporosis Classification with Radiographs-A Systematic Review. Bioengineering (Basel) 2024; 11: 484
• 28 Habibi MA, Naseri Alavi SA, Soltani Farsani A. et al. Predicting the Outcome and Survival of Patients with Spinal Cord Injury using Machine Learning Algorithms; A Systematic Review. World Neurosurg 2024;
• 29 Greenberg JK, Frumkin M, Xu Z. et al. Preoperative Mobile Health Data Improve Predictions of Recovery From Lumbar Spine Surgery. Neurosurgery 2024;
• 30 Berg B, Gorosito MA, Fjeld O. et al. Machine Learning Models for Predicting Disability and Pain Following Lumbar Disc Herniation Surgery. JAMA Netw Open 2024; 7
• 31 Harada GK, Siyaji ZK, Mallow GM. et al. Artificial intelligence predicts disk re-herniation following lumbar microdiscectomy: development of the “RAD” risk profile. Eur Spine J 2021; 30: 2167-2175
• 32 Khor S, Lavallee D, Cizik AM. et al. Development and Validation of a Prediction Model for Pain and Functional Outcomes After Lumbar Spine Surgery. JAMA Surg 2018; 153: 634-642
• 33 Schönnagel L, Caffard T, Vu-Han TL. et al. Predicting postoperative outcomes in lumbar spinal fusion: development of a machine learning model. Spine J 2024; 24: 239-249
• 34 Saravi B, Zink A, Ülkümen S. et al. Performance of Artificial Intelligence-Based Algorithms to Predict Prolonged Length of Stay after Lumbar Decompression Surgery. J Clin Med 2022; 11: 4050
• 35 Karabacak M, Margetis K. Machine Learning-Based Prediction of Short-Term Adverse Postoperative Outcomes in Cervical Disc Arthroplasty Patients. World Neurosurg 2023;
• 36 Mohanty S, Hassan FM, Lenke LG. et al. Machine learning clustering of adult spinal deformity patients identifies four prognostic phenotypes: a multicenter prospective cohort analysis with single surgeon external validation. Spine J 2024; 24: 1095-1108
• 37 Buchlak QD, Yanamadala V, Leveque JC. et al. The Seattle spine score: Predicting 30-day complication risk in adult spinal deformity surgery. J Clin Neurosci 2017; 43: 247-255
• 38 Schonfeld E, Pant A, Shah A. et al. Evaluating Computer Vision, Large Language, and Genome-Wide Association Models in a Limited Sized Patient Cohort for Pre-Operative Risk Stratification in Adult Spinal Deformity Surgery. J Clin Med 2024; 13: 656
• 39 Khaledian N, Bagheri SR, Sharifi H. et al. The efficacy of machine learning models in forecasting treatment failure in thoracolumbar burst fractures treated with short-segment posterior spinal fixation. J Orthop Surg Res 2024; 19: 211
• 40 Cai S, Liu W, Cai X. et al. Predicting osteoporotic fractures post-vertebroplasty: a machine learning approach with a web-based calculator. BMC Surg 2024; 24: 142
• 41 Collins GS, Moons KGM, Dhiman P. et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 2024; 385
• 42 Van Calster B, Steyerberg EW, Wynants L. et al. There is no such thing as a validated prediction model. BMC Med 2023; 21: 70
• 43 Khalid SI, Massaad E, Roy JM. et al. An Appraisal of the Quality of Development and Reporting of Predictive Models in Neurosurgery: A Systematic Review. Neurosurgery 2024;
• 44 Schmidhuber J. Deep learning in neural networks: An overview. Neural networks 2014; 61: 85-117
• 45 Vaswani A, Shazeer NM, Parmar N. et al. Attention is All you Need. Advances in Neural Information Processing Systems 2017; 30
• 46 London AJ. Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability. Hastings Cent Rep 2019; 49: 15-21
• 47 Allen B. The Promise of Explainable AI in Digital Health for Precision Medicine: A Systematic Review. Journal of Personalized Medicine 2024; 14: 277
• 48 Umapathi LK, Pal A, Sankarasubbu M. Med-HALT: Medical Domain Hallucination Test for Large Language Models. ArXiv 2023;
• 49 Zhou H, Gu B, Zou X. et al. A Survey of Large Language Models in Medicine: Progress, Application, and Challenge. ArXiv 2023;
• 50 Katz U, Cohen E, Shachar E. et al. GPT versus Resident Physicians — A Benchmark Based on Official Board Scores. NEJM AI 2024; 1: 3
• 51 Rydzewski NR, Dinakaran D, Zhao SG. et al. Comparative Evaluation of LLMs in Clinical Oncology. NEJM AI 2024; 1: 2
• 52 Ali R, Tang OY, Connolly ID. et al. Performance of ChatGPT, GPT-4, and Google Bard on a Neurosurgery Oral Boards Preparation Question Bank. Neurosurgery 2023; 93: 1090-1098
• 53 Guerra GA, Hofmann H, Sobhani S. et al. GPT-4 Artificial Intelligence Model Outperforms ChatGPT, Medical Students, and Neurosurgery Residents on Neurosurgery Written Board-Like Questions. World Neurosurg 2023; 179: e160-e165
• 54 Saab K, Tu T, Weng W-H. et al. Capabilities of Gemini Models in Medicine. ArXiv 2024;
• 55 Ling C, Zhao X, Lu J. et al. Domain specialization as the key to make large language models disruptive: A comprehensive survey. ArXiv 2023;
• 56 Schick T, Dwivedi-Yu J, Dessì R. et al. Toolformer: Language Models Can Teach Themselves to Use Tools. ArXiv 2023;
• 57 Lewis P, Perez E, Piktus A. et al. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. ArXiv 2020;
• 58 Zhuge M, Liu H, Faccio F. et al. Mindstorms in Natural Language-Based Societies of Mind. ArXiv 2023;
• 59 Guo T, Chen X, Wang Y. et al. Large Language Model based Multi-Agents: A Survey of Progress and Challenges. ArXiv 2024;
• 60 Seufert S, Meier C. Hybride Intelligenz: Zusammenarbeit mit KI-Assistenzsystemen in wissensintensiven Bereichen. HMD Praxis der Wirtschaftsinformatik 2023; 60: 1194-1209
• 61 Griffen Z, Owens K. From “Human in the Loop” to a Participatory System of Governance for AI in Healthcare. The American Journal of Bioethics 2024; 24: 81-83
• 62 Nong P, Hamasha R, Singh K. et al. How Academic Medical Centers Govern AI Prediction Tools in the Context of Uncertainty and Evolving Regulation. NEJM AI 2024; 1: 3
• 63 Ditzingen: Reclam, Universal-Bibliothek; 2022. ISBN: 978-3-15-014243-1
• 64 Esposito E. Kommunikation mit unverständlichen Maschinen. Wien - Salzburg: Residenz Verlag; 2024. ISBN: 978-3-7017-3609-6