Using Augmented Reality in neurosurgical training

The future of artificial intelligence in neurosurgery

AI is rapidly transforming the landscape of medicine, and neurosurgery stands to
gain immense benefits from its integration. Imagine enhanced diagnostic capabilities,
enabling the detection of high-grade gliomas at their earliest stages, even before
symptoms appear. Think of spinal or cranial surgical procedures guided by real-time
data analysis, minimizing risks, and maximizing precision. Envision personalized
rehabilitation programs tailored to patient’s needs, accelerating recovery and
improving long-term outcomes. These are just glimpses of the potential AI holds as a
powerful ally to surgeons. In this article, let’s accompany UpSurgeOn into the
definition of AI, its applications across different subspecialties of neurosurgery, and
the ethical considerations surrounding its implementation. [1, 2]

What is Artificial Intelligence

Before we delve deeper, let’s clarify some key terms. The term “Artificial
Intelligence” often conjures images of robots or machines surpassing human
intelligence in a general sense. While that might be the ultimate goal for some areas
of AI research, it doesn’t paint the complete picture. A true definition of AI is still
evolving. To make it easier, instead of “intelligence,” It is possible to think of AI as
systems or tools designed to tackle specific challenges typically requiring human
intelligence.
Machine learning instead is a specific tool used within AI that allows it to learn and
improve automatically from data. Algorithms are trained on vast datasets, enabling
them to identify patterns and relationships that might be missed by humans. These
learned patterns allow them to make predictions, recommendations, and even
decisions based on new data they encounter.
One particular example of a tool utilizing AI is ChatGPT, a large language model
capable of generating human-like text. It’s built on several state-of-the-art
technologies, including:

  • Natural Language Processing (NLP): This branch of AI enables ChatGPT to
    understand and generate text in a natural-sounding way through techniques
    like tokenization, named entity recognition, and sentiment analysis.
  • Machine Learning: This allows ChatGPT to learn from massive amounts of
    text data and predict the next word in a sentence based on the previous
    words.
  • Deep Learning: This utilizes complex neural networks called transformers to
    process and understand text data, giving ChatGPT the ability to generate
    coherent and natural-sounding text.

AI and neurosurgery

With this understanding, we can begin to explore the specific ways AI can empower
neurosurgery. This transformative potential holds immense promise for neurosurgery
in three key areas, which are described below. [3]

1. Early detection and personalized care

Imagine a future where brain tumors, aneurysms, or other neurological conditions
are detected in their earliest stages, even before symptoms appear. By analyzing
mountains of medical data like MRI scans and genetic information, AI can detect
subtle abnormalities that might escape the human eye. This earlier detection not only
improves treatment success rates but also paves the way for personalized care.
Tailoring interventions based on individual needs, AI can help identify patients who
might benefit from minimally invasive procedures or specific drug therapies,
minimizing risks and maximizing outcomes.
For example, AI algorithms have shown promise in outperforming human experts in
detecting subtle high-grade glioma recurrence potentially leading to earlier
diagnosis and – hopefully- a better prognosis. [4, 5]

2. Enhanced Precision and Surgical Guidance

The operating room, a high-pressure environment where split-second decisions can
have life-altering consequences, is where AI can truly shine. Here’s how:

  • Real-time data analysis: Imagine AI systems acting as vigilant assistants,
    analyzing data from multiple sources during surgery, including vital signs,
    brain activity, and even surgical tools. This data could be used to alert
    surgeons to potential complications in real time suggest optimal surgical
    strategies based on individual patient anatomy, and even predict individual
    responses to treatments. [6]
  • Simulation: Additionally, surgical simulators integrated with AI are enabling
    surgeons to practice complex procedures in realistic virtual environments,
    receiving real-time feedback on their techniques and improving their skills
    before operating on real patients. [7]
Using Augmented Reality in neurosurgical training

3. Personalized Recovery and Remote Monitoring

The journey to recovery after neurosurgery in general and brain surgery in particular
doesn’t end in the operating room. AI could play a crucial role in personalized
rehabilitation programs, tailoring exercises and therapies to each patient’s specific
needs and progress. Additionally, AI-powered remote monitoring systems could track
patients’ vitals, cognitive function, and other key indicators, allowing for early
detection of potential complications and timely intervention. [8]

Navigating the Ethical Landscape

With any revolutionary technology, ethical considerations are paramount. Here are
some key challenges that need to be addressed:

  • Data security and privacy: Protecting sensitive patient data is of utmost
    importance. Robust security measures and ethical frameworks are crucial.
  • Algorithmic bias: AI algorithms are trained on data, and if that data is biased,
    the algorithms can perpetuate those biases. Careful data selection and
    ongoing monitoring are essential to mitigate this risk and ensure fairness and
    inclusivity in AI-driven healthcare.


Delve deeper into the world of AI-powered neurosurgery with UpSurgeOn Academy.
Our comprehensive library of informative guides, specifically designed for medical
professionals, equips you with the knowledge and insights to revolutionize your
surgical training. Continue your journey today and join us on the frontier of this
groundbreaking field!

Authors


Grazia Menna, MD | LinkedIn


References

[1] Danilov GV, Shifrin MA, Kotik KV, et al. Artificial Intelligence in Neurosurgery: a Systematic Review Using Topic Modeling. Part I: Major Research Areas. Sovrem Tekhnologii Med. 2021;12(5):106-112. doi:10.17691/stm2020.12.5.12

[2] Danilov GV, Shifrin MA, Kotik KV, et al. Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives. Sovrem Tekhnologii Med. 2021;12(6):111-118. doi:10.17691/stm2020.12.6.12

[3] Dagi TF, Barker FG, Glass J. Machine Learning and Artificial Intelligence in Neurosurgery: Status, Prospects, and Challenges. Neurosurgery. 2021;89(2):133-142. doi:10.1093/neuros/nyab170

[4] Della Pepa GM, Caccavella VM, Menna G, et al. Machine Learning-Based Prediction of Early Recurrence in Glioblastoma Patients: A Glance Towards Precision Medicine. Neurosurgery. 2021;89(5):873-883. doi:10.1093/neuros/nyab320

[5] Della Pepa GM, Caccavella VM, Menna G, et al. Machine Learning-Based Prediction of 6-Month Postoperative Karnofsky Performance Status in Patients with Glioblastoma: Capturing the Real-Life Interaction of Multiple Clinical and Oncologic Factors. World Neurosurg. 2021;149:e866-e876. doi:10.1016/j.wneu.2021.01.082

[6] Tanzi L, Piazzolla P, Porpiglia F, Vezzetti E. Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance. Int J Comput Assist Radiol Surg. 2021;16(9):1435-1445. doi:10.1007/s11548-021-02432-y

[7] Winkler-Schwartz A, Yilmaz R, Mirchi N, et al. Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual Reality Simulation. JAMA Netw Open. 2019;2(8):e198363. Published 2019 Aug 2. doi:10.1001/jamanetworkopen.2019.8363

[8] Schmid W, Fan Y, Chi T, et al. Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries. J Neural Eng. 2021;18(4):10.1088/1741-2552/ac1982. Published 2021 Aug 19. doi:10.1088/1741-2552/ac1982

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