Artificial Intelligence Algorithm‐Based Intraoperative Magnetic Resonance Navigation for Glioma Resection

Wei, Jianqiang; Zhang, Chunman; Ma, Liujia; Zhang, Chunrui

doi:10.1155/2022/4147970

Cited by 5 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of iFC, machine learning algorithms could be trained to recognize subtle patterns, potentially enhancing the accuracy of real-time tumor margin evaluations. This is already a reality for iMRI and the analysis of gliomas [65,66], with promising results. Similar methodologies are already developing for 5 ALA fluorescence analysis [67].…”

Section: Future Prospectsmentioning

confidence: 98%

Critical Assessment of Cancer Characterization and Margin Evaluation Techniques in Brain Malignancies: From Fast Biopsy to Intraoperative Flow Cytometry

Liaropoulos,

Liaropoulos

2023

Cancers

View full text Add to dashboard Cite

Brain malignancies, given their intricate nature and location, present significant challenges in both diagnosis and treatment. This review critically assesses a range of diagnostic and surgical techniques that have emerged as transformative tools in brain malignancy management. Fast biopsy techniques, prioritizing rapid and minimally invasive tissue sampling, have revolutionized initial diagnostic stages. Intraoperative flow cytometry (iFC) offers real-time cellular analysis during surgeries, ensuring optimal tumor resection. The advent of intraoperative MRI (iMRI) has seamlessly integrated imaging into surgical procedures, providing dynamic feedback and preserving critical brain structures. Additionally, 5-aminolevulinic acid (5-ALA) has enhanced surgical precision by inducing fluorescence in tumor cells, aiding in their complete resection. Several other techniques have been developed in recent years, including intraoperative mass spectrometry methodologies. While each technique boasts unique strengths, they also present potential limitations. As technology and research continue to evolve, these methods are set to undergo further refinement. Collaborative global efforts will be pivotal in driving these advancements, promising a future of improved patient outcomes in brain malignancy management.

show abstract

Section: Future Prospectsmentioning

confidence: 98%

Critical Assessment of Cancer Characterization and Margin Evaluation Techniques in Brain Malignancies: From Fast Biopsy to Intraoperative Flow Cytometry

Liaropoulos,

Liaropoulos

2023

Cancers

View full text Add to dashboard Cite

show abstract

“…There was no significant difference in the Karnovsky scores, NIHSS scores, or infection rates between the two groups. The study clearly showed that using AI-MRI interoperative guidance improved tumor resection [ 24 ].…”

Section: Reviewmentioning

confidence: 99%

The Future of Artificial Intelligence in Surgery

Hamilton

2024

Cureus

View full text Add to dashboard Cite

Until recently, innovations in surgery were largely represented by extensions or augmentations of the surgeon’s perception. This includes advancements such as the operating microscope, tumor fluorescence, intraoperative ultrasound, and minimally invasive surgical instrumentation. However, introducing artificial intelligence (AI) into the surgical disciplines represents a transformational event. Not only does AI contribute substantively to enhancing a surgeon’s perception with such methodologies as three-dimensional anatomic overlays with augmented reality, AI-improved visualization for tumor resection, and AI-formatted endoscopic and robotic surgery guidance. What truly makes AI so different is that it also provides ways to augment the surgeon’s cognition. By analyzing enormous databases, AI can offer new insights that can transform the operative environment in several ways. It can enable preoperative risk assessment and allow a better selection of candidates for procedures such as organ transplantation. AI can also increase the efficiency and throughput of operating rooms and staff and coordinate the utilization of critical resources such as intensive care unit beds and ventilators. Furthermore, AI is revolutionizing intraoperative guidance, improving the detection of cancers, permitting endovascular navigation, and ensuring the reduction in collateral damage to adjacent tissues during surgery (e.g., identification of parathyroid glands during thyroidectomy). AI is also transforming how we evaluate and assess surgical proficiency and trainees in postgraduate programs. It offers the potential for multiple, serial evaluations, using various scoring systems while remaining free from the biases that can plague human supervisors. The future of AI-driven surgery holds promising trends, including the globalization of surgical education, the miniaturization of instrumentation, and the increasing success of autonomous surgical robots. These advancements raise the prospect of deploying fully autonomous surgical robots in the near future into challenging environments such as the battlefield, disaster areas, and even extraplanetary exploration. In light of these transformative developments, it is clear that the future of surgery will belong to those who can most readily embrace and harness the power of AI.

show abstract

Assessing the benefits of digital twins in neurosurgery: a systematic review

Chumnanvej,

Tripathi

2024

Neurosurg Rev

View full text Add to dashboard Cite

Artificial Intelligence Algorithm‐Based Intraoperative Magnetic Resonance Navigation for Glioma Resection

Cited by 5 publications

References 34 publications

Critical Assessment of Cancer Characterization and Margin Evaluation Techniques in Brain Malignancies: From Fast Biopsy to Intraoperative Flow Cytometry

Critical Assessment of Cancer Characterization and Margin Evaluation Techniques in Brain Malignancies: From Fast Biopsy to Intraoperative Flow Cytometry

The Future of Artificial Intelligence in Surgery

Assessing the benefits of digital twins in neurosurgery: a systematic review

Contact Info

Product

Resources

About