Using Data and AI to Predict Patient Outcomes in Spinal Osteotomies

John H. Shin, MD, is the director of Metastatic Spine Oncology and Spinal Deformity Surgery at Massachusetts General Hospital. He is researching the use of innovative technologies to help us predict which patients will benefit from complex spinal osteotomies for deformity, which has a very high complication rate. In this video, he discusses how these technologies such as interoperative CT help guide and make operations safer for patients.

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Transcript

When performing these complex spinal osteotomies for deformity, oftentimes we have to rely on anatomical landmarks alone at the time of surgery for execution of what we're trying to do. And a lot of this is based on the preoperative imaging studies, which include X-rays, MRIs and CAT scans. Our research is looking at how we can use innovative technologies that are actually present in what we have here at Mass General to help us predict which patients will benefit from these types of reconstructions and also help us plan and perform these safely.

These are operations that are associated with a very high complication rate, unlike most simple degenerative cervical spine operations. The complications can be significant and can be life-altering. For this reason, we were very much interested in looking at and assessing the patient outcomes and how they're doing after these operations. Specifically, we're using image guidance navigation to help plan the actual bony cuts in the cervical and thoracic spine. These are cuts made around the spinal cord, around the nerve roots and around the major blood vessels going to the brain. These cuts allow us to manipulate and actually reshape and mold the spine to the configuration that's best for the patient.

We're taking some of the guesswork out of this operation by using these innovative technologies and using the Intraoperative CT to help guide us and make these operations safer for patients. By doing so, we've been able to show that the complication rates are much lower and also that we're achieving the desired result. 

We're continuing to expand this type of technology in terms of seeing how we can integrate computer-based learning as well as artificial intelligence to further understand how much correction is needed for these deformities and how extensive the reconstructions need to be for these patients. We're taking the patients' preoperative studies and looking at computer modeling to understand really how much their cervical spines and their spines can tolerate with regards to manipulation and reconstruction.

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