With the fields of machine learning and artificial intelligence (AI) evolving almost daily, experts in several industries are trying to figure out ways the technology can advance their specific sectors. Artificial intelligence can analyze an unfathomable number of data points in shockingly little time and come up with conclusions that would have otherwise taken hundreds if not thousands of human work hours.
In medicine, researchers hope this ability can help them treat two of the most difficult-to-treat diseases on the planet: brain and spinal tumors. Referred to as cavernous malformations, these are vascular tumors that develop in the spinal cord or brain and often result in severe health issues such as stroke, blindness and epilepsy. Patients with cavernous malformations can also suffer from numbness, trouble with movement and tingling feelings throughout their entire body.
Even though cavernous malformations aren’t cancerous, their tendency to bleed or burst often affects nearby tissues in a way that cannot be easily tied to the tumors.
Top medical experts are now partnering up with computer scientists to figure out if AI technology can be used to aid in the early diagnosis of these brain tumors. Physicians usually use magnetic resonance imaging (MRI) to detect cavernous malformations, but they are often discovered well after they have developed and begin causing other health problems.
LSU Health Shreveport neurosurgeon Dr. Caleb Stewart said that even though neurosurgery itself is exceptionally complex, cavernous malformations add an extra level of complexity and difficulty. Furthermore, Stewart said, these kinds of tumors have barely been studied in neurosurgery as their extreme diversity makes it difficult for researchers to compare different malformations and come up with diagnoses and treatment procedures.
LSU Shreveport, Oschner Health and LSU Health Shreveport have partnered with Australian players to leverage machine learning and artificial intelligence and come up with a viable solution for dealing with cavernous malformations. Researchers from these organizations will use clinical data collected over more than 10 years at LSU Health including lab results, medical imaging, diagnostic codes, pathology slides and electronic health records.
In total, the researchers will feed data with nearly 3,000 variables to the AI, significantly more than most neurosurgeons can consider while they work with their patients. According to Stewart, the lesions of cavernous malformations tend to grow in the brain and spinal cord close to the base of the skull or deep in the brain, areas that are extremely high risk and increase the odds of negative health outcomes. Taking advantage of AI could allow physicians to develop precise analytical tools to predict whether a malformation will bleed or not and which course of action to take.
Those advanced technologies are also powering the drug-development efforts of enterprises such as CNS Pharmaceuticals Inc. (NASDAQ: CNSP) in their bids to bring to market precision treatments that are better than what is currently on the market.
NOTE TO INVESTORS: The latest news and updates relating to CNS Pharmaceuticals Inc. (NASDAQ: CNSP) are available in the company’s newsroom at https://ibn.fm/CNSP
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