Dr Mehrnaz Shoushtarian claims she has found a way to objectively measure tinnitus severity and whether a person has tinnitus or not via fNIRS (functional near-infrared spectroscopy).
I've read a few articles on this already. I'll link two below:
Technology lets clinicians objectively detect tinnitus for first time
I found this statement rather interesting:
fNIRS revealed a statistically significant difference in the connectivity between areas of the brain in people with and without tinnitus. Moreover, the brain's response to both visual and auditory stimuli was dampened among patients with tinnitus. When a machine learning approach was applied to the data, a program could differentiate patients with slight/mild tinnitus from those with moderate/severe tinnitus with an 87.32% accuracy. The authors conclude that fNIRS may be a feasible way to objectively assess tinnitus to assess new treatments or monitor the effectiveness of a patient's treatment program.
AI that can diagnose tinnitus from brain scans may improve treatment
Their study can be found here:
Objective measurement of tinnitus using functional near-infrared spectroscopy and machine learning
@Markku @Hazel, maybe you could reach out to her and see if she would be interested in doing an episode for the Tinnitus Talk Podcast?
Good news: We have now published our interview with the Bionics Institute as the next episode of the Tinnitus Talk Podcast!
For now, it's available to Patreon supporters only, but never fear, it will be publicly available soon. Patreons will also have exclusive access to the video recording of the podcast. You can become a Patreon supporter for as little as $2 USD per month.
It was a very interesting interview with a clearly very dedicated and capable brain researcher, Mehrnaz Shoushtarian (PhD). We spoke about the technical ins and outs of the objective measure, the underlying theories the work is based on, the future commercialisation of the technique, the ultimate impact the Bionics Institute is hoping for, and much more.
We hope you will find the episode informative; let us know down below once you've had a listen!
I've read a few articles on this already. I'll link two below:
Technology lets clinicians objectively detect tinnitus for first time
I found this statement rather interesting:
fNIRS revealed a statistically significant difference in the connectivity between areas of the brain in people with and without tinnitus. Moreover, the brain's response to both visual and auditory stimuli was dampened among patients with tinnitus. When a machine learning approach was applied to the data, a program could differentiate patients with slight/mild tinnitus from those with moderate/severe tinnitus with an 87.32% accuracy. The authors conclude that fNIRS may be a feasible way to objectively assess tinnitus to assess new treatments or monitor the effectiveness of a patient's treatment program.
AI that can diagnose tinnitus from brain scans may improve treatment
Their study can be found here:
Objective measurement of tinnitus using functional near-infrared spectroscopy and machine learning
@Markku @Hazel, maybe you could reach out to her and see if she would be interested in doing an episode for the Tinnitus Talk Podcast?
From @Hazel:
Good news: We have now published our interview with the Bionics Institute as the next episode of the Tinnitus Talk Podcast!
For now, it's available to Patreon supporters only, but never fear, it will be publicly available soon. Patreons will also have exclusive access to the video recording of the podcast. You can become a Patreon supporter for as little as $2 USD per month.
It was a very interesting interview with a clearly very dedicated and capable brain researcher, Mehrnaz Shoushtarian (PhD). We spoke about the technical ins and outs of the objective measure, the underlying theories the work is based on, the future commercialisation of the technique, the ultimate impact the Bionics Institute is hoping for, and much more.
We hope you will find the episode informative; let us know down below once you've had a listen!