海角社区

Medical doctors are collaborating with computer scientists to improve care for patients with cavernous malformations, some of the most difficult-to-treat tumors in the head and spine.

Cavernous malformations are vascular tumors in the brain or spinal cord. Although 鈥渂enign鈥� as opposed to cancerous, these blood vessel lesions can still cause serious health problems, such as epilepsy, stroke and blindness, as well as issues with movement, numbness and tingling throughout the body. This is primarily because cavernous malformations tend to burst or bleed, which can impact nearby tissues in spontaneous and seemingly inexplicable ways until the tumors are diagnosed, usually by magnetic resonance imaging, or MRI. Most cavernous malformations are not found until they start causing problems, but once known, doctors and patients are faced with the difficult decision of whether to operate, medicate or leave them be.

鈥淢edicine in general is considerably complex and neurosurgery is extremely complex, with cavernous malformations among the most difficult to manage,鈥� said Dr. Caleb Stewart, neurosurgeon at 海角社区 Health Shreveport. 鈥淚t鈥檚 also one of the most understudied problems in neurosurgery because every malformation presents as an apples-and-oranges problem鈥攅ach one appears unique, so it鈥檚 difficult to compare, plan procedures and make decisions on the best course of action.鈥�

海角社区 Health Shreveport, 海角社区 Shreveport, Ochsner Health and collaborators in Australia are now leveraging big data science and artificial intelligence, or AI, to solve this challenge and provide better care for patients. The researchers will use more than a decade鈥檚 worth of clinical data from 海角社区 Health Shreveport鈥攅lectronic health records, lab results, diagnostic codes, medical imaging and pathology slides are just some of the many sources of information that contribute to the rich dataset of close to 3,000 variables. That鈥檚 far more than most neurosurgeons are able to consider on a day-to-day basis in their work with patients.

鈥淭hese lesions are located in the brain and spinal cord with those near the base of the skull or deep into the brain being inherently high-risk areas,鈥� Dr. Stewart said. 鈥淔rankly, we don鈥檛 yet have the right analytical tools to be able to predict if they鈥檙e going to cause problems or not. If they don鈥檛 bleed and we intervene, we can do our patients a huge disservice. All we have to go on right now is experience, intuition and consensus鈥攐ur judgment is not actually cemented in patient-specific probability.鈥�

AI, meanwhile, is exceptionally good at recognizing patterns in large datasets, including between variables no one expected to be connected. This could help doctors make better predictions of patient outcomes, whether the best choice is surgery, radiation, medication or doing nothing at all.

Subhajit Chakrabarty, assistant professor of computer science at 海角社区 Shreveport, brings his AI and machine learning expertise to the project.

鈥淲hat I love about this project is the data challenge,鈥� Chakrabarty said. 鈥淎part from many observed and recorded variables, there are likely to be several hidden variables, several hidden data clusters and complex causalities. Not only would we like to derive new data-driven insights for cavernous malformations, but we would also like to establish a high benchmark of accurate predictive modeling.鈥�

The researchers鈥� goal is to use the cavernous malformations study as a springboard for other projects.

鈥淎 big part of our challenge is the team building, collection and management of the data itself,鈥� Dr. Stewart said. 鈥淲e have a supercomputer now, so from a computational standpoint, we can be more productive and efficient with data processing. However, we also have to build new infrastructure between people鈥攔esearchers and clinicians in different disciplines and locations鈥攖o achieve the much larger vision of what we鈥檙e trying to do here, which is to help patients.鈥�

Dr. Steven Conrad is the clinical informatics division chief at 海角社区 Health Shreveport and a collaborator on the project.

鈥淢ultidisciplinary teams that combine expertise in clinical medicine and data science are key to leveraging the capabilities of new-generation machine learning and artificial intelligence techniques,鈥� Dr. Conrad said. 鈥淒r. Stewart and his colleagues have assembled a team that can tackle difficult real-world problems in biomedicine.鈥�

Neurosurgeons and neurologists in Louisiana and around the world would benefit from better predictive tools in the diagnosis and treatment of cavernous malformations, according to Dr. Korak Sarkar at Ochsner Health in New Orleans. He founded and currently serves as the medical director of Ochsner鈥檚 m3D Lab. Their work in advanced visualization leverages tools like additive manufacturing and mixed reality to create patient-specific anatomical models for patient education, medical training and clinical support.

鈥淭he deployment and validation of novel tools in healthcare, such as machine learning, 3D printing and virtual reality, require collaborations like the ones between 海角社区 and Ochsner,鈥� Dr. Sarkar said. 鈥淭hese initiatives will greatly benefit Louisiana, where our population unfortunately carries a large disease burden, particularly in cerebrovascular disease.鈥�

鈥淭he deployment and validation of novel tools in healthcare, such as machine learning, 3D printing and virtual reality, require collaborations like the ones between 海角社区 and Ochsner...These initiatives will greatly benefit Louisiana, where our population unfortunately carries a large disease burden, particularly in cerebrovascular disease.鈥�

Dr. Korak Sarkar, medical director of Ochsner Health's m3D Lab in New Orleans