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.