Using AI to Power the Study and Treatment of Hearing Loss

AI and the inner ear

Buswinka’s thesis, which he defended in September 2024, focused on applications of machine learning to the study of the inner ear. He developed two tools he’s particularly proud of.

One is a program to help researchers make the most of the reams of data they get when imaging the hair cells of the cochlea, which can number between 2,500 and 4,000 cells, depending on the animal. Researchers often make estimates rather than counting each cell manually, but that could leave interesting information undocumented.

Buswinka addressed this need by building an algorithm-powered software informed by fully analyzed and labeled images from the Indzhykulian Lab and those from colleagues throughout the field.

“Hopefully as adoption, which has been growing, gets larger, the new standard in the field will be to use the models that I have created to use all of the data they collect and hopefully do better science,” he said.

In a more niche project, he developed a tool to analyze the health of mitochondria in cochlear hair cells. These cells don’t regenerate, so what you are born with is all nature is going to give you, Buswinka said. He and members of his lab asked: Do loud noises destroy these cells by damaging their mitochondria?

At the time, most deep-learning models for analyzing mitochondrial images were optimized for neural tissue and did not work well to answer Buswinka’s question. So he created an algorithm specifically to handle the large, high-resolution, three-dimensional electron microscope images of the mitochondria-packed hair cells.

“It worked really, really well,” he said. “It allowed us to probe some of the biological effects of noise or different drugs in these cells.”

Expanding the research to other students and sensory systems

With AI now a major topic in public discourse, Buswinka notes that there is a world of machine learning that has been aiding researchers in specific situations for years separate from the more recent generative AI programs powering tools such as ChatGPT.

“Rather than solving everyone’s problem at the same time, the AI tools I’ve created have tackled a specific problem that was very challenging to solve in any other way,” he said.

Buswinka started paying forward the mentorship and AI education he received at HMS. He enlisted high school and undergraduate interns to annotate the data used to train his models and took time to explain how AI works and show them why their work was important. He made sure they all received credit for contributing to the papers and projects.

Buswinka has now taken his interest in AI and the senses to a computer vision start-up on Boston’s North Shore. He lives there with his girlfriend and his cat Sydney (and fondly remembers his cat Darwin, who died around the time of Buswinka’s defense), to whom he credits much of his success.

“I believe computer vision and machine learning have great potential to enable better science,” he said.

Whether Buswinka next pursues entrepreneurship, an academic position, or another career path, he believes his work will be useful.

“Science is a noble goal and should be pursued, especially when the outcomes of our work can benefit many people,” he said.

“Ultimately, I think Harvard was the best place for me to do the work that I wanted to do. SHBT was phenomenal,” he added. “They were just lovely people to be around. Extremely smart, very supportive. I’d do it again if I could.”