This chapter provides a summary of the application of AI in neurosurgery, with a focus on neurooncology, and describes how AI-based solutions have the potential to improve all phases of neurooncological care, from diagnosis to follow-up. The chapter introduces the diagnostic tools in the form of detection and segmentation algorithms that represent the first AI-based applications that are ready to be implemented into clinical practice. The current applications can perform accurate glioma segmentation and the ability of AI to predict histological, molecular and genetic variances of any given tumor in the preoperative radiology data or intraoperative frozen sections is described. The current status of outcome prediction models in this field is also briefly outlined. The challenges preventing AI application are discussed including the trustworthiness of the algorithms and the shareability of data, which represent the cornerstones upon which safe and efficient AI-based clinical tools can be developed. The future directions and potential application of AI in surgical neurooncology are also presented.
Chapter 30: Artificial intelligence in neurosurgery—a focus on neuro-oncology
Boaro, A.
;
2023-01-01
Abstract
This chapter provides a summary of the application of AI in neurosurgery, with a focus on neurooncology, and describes how AI-based solutions have the potential to improve all phases of neurooncological care, from diagnosis to follow-up. The chapter introduces the diagnostic tools in the form of detection and segmentation algorithms that represent the first AI-based applications that are ready to be implemented into clinical practice. The current applications can perform accurate glioma segmentation and the ability of AI to predict histological, molecular and genetic variances of any given tumor in the preoperative radiology data or intraoperative frozen sections is described. The current status of outcome prediction models in this field is also briefly outlined. The challenges preventing AI application are discussed including the trustworthiness of the algorithms and the shareability of data, which represent the cornerstones upon which safe and efficient AI-based clinical tools can be developed. The future directions and potential application of AI in surgical neurooncology are also presented.File | Dimensione | Formato | |
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