Advanced Technologies

Artificial Intelligence Algorithms in the Identification and Demonstrating of Pain Generators Treated with Endoscopic Spine Surgery

Author(s): Sandeep Shah, Narendran Muraleedharan Basme, Vikram Sobti, Jorge Felipe Ramírez León and Kai-Uwe Lewandrowski *

Pp: 38-52 (15)

DOI: 10.2174/9789815051544122030006

* (Excluding Mailing and Handling)


 Identifying pain generators in multilevel lumbar degenerative disc disease focuses on artificial intelligence (AI) applications in endoscopic spine care to assure adequate symptom relief with the targeted endoscopic spinal decompression surgery. Artificial intelligence (AI) applications of deep learning neural networks to analyze routine lumbar MRI scans could improve clinical outcomes. One way to accomplish this is to apply AI management of patient records using a highly automated workflow, highlighting degenerative and acute abnormalities using unique three-dimensional patient anatomy models. These models help with the identification of the most suitable endoscopic treatment protocol. Radiology AI bots could help primary care doctors, specialists including surgeons and radiologists to read the patient's MRI scans and more accurately and transcribe radiology reports. In this chapter, the authors introduce the concept of AI applications in endoscopic spine care and present some initial feasibility data validating its use based on intraoperatively visualized pathology. This research's ultimate objective is to assist in the development of AI algorithms predictive of the most successful and cost-effective outcomes with lumbar spinal endoscopy by using the radiologist's MRI grading and the grading of an AI deep learning neural network (Multus Radbot™) as independent prognosticators.

Keywords: Artificial intelligence, Endoscopic spinal surgery, Magnetic resonance imaging, Pain generator prognostication.

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