Jason Lu, PhD, associate professor in the division of biomedical informatics at Cincinnati Children’s, and his colleagues analyzed results of pre-surgery hearing tests. The researchers found that elevated activity during functional magnetic resonance imaging (fMRI) in two regions of the brain was highly predictive of which child would benefit the most from their implants.
Explore This IssueMay 2017
The stimuli used in the fMRI testing before the surgery included both natural language speech and narrow-band noise tones. When the investigators analyzed data from both pre-surgery auditory tests and two-year language tests, the brain activation patterns in natural language speech showed greater predictive ability. “This study identifies two features of our computer analysis that are potential biomarkers for predicting CI outcomes,” said Dr. Lu. “More accurate screening could reduce the number of children undergoing this costly and invasive procedure, only to be disappointed with the results.”
Early Stages Still
Although CM is promising, the researchers stressed that the tool is still very much in the proof-of-concept stage. The studies have been prospective in nature and small in number. They are also looking at the next stage of development, which will include using larger datasets and validating the results in other institutions.
“CM is a very powerful and promising tool, but it requires a close collaboration between physicians and computational scientists to develop a model,” said Dr. Lu. “The physicians provide domain knowledge to computational scientists. With further development, more advanced tools could be developed, providing real-time guidance for physicians and, hopefully, making surgery easier and more successful.”
Surgeon/Programmer Communication Essential
Better communication and understanding among the players involved may help build trust in the models. “One of the reasons there is skepticism around the use of models is that people don’t know what is under the hood,” said Dr. Peng. “They don’t have a good idea of how the numbers were found and what they mean.” One of her bigger challenges is getting modelers in contact with potential users, she said. “More people could then embrace modeling in their research and clinical practices. This would hopefully lead to models that are usable at the bedside independent of the size of the facility,” she added.
The next step is trialing these CM algorithms in a more structured manner. After that, there is confidence that the computer/clinical interface will become more important as times goes on. “I am hoping within the next 10 years we’ll have an initial set of surgeons embracing this technology once it becomes available,” said Dr. Frank-Ito. “By integrating CM with surgical navigation systems, doctors will be able to plan and even practice different procedures long before they enter the operating room. With the aid of navigation systems, our CM approach will identify sites of greatest nasal obstruction and then guide surgeons to these sites for correction. Our goal is that the surgeon will use our technology to complement what is currently done, rather than replace what they do.”