Novel Speech Recognizing App may help Predict HF by recognising fluid buildup in lungs: Study
Key findings of the study:
- The researchers analyzed a total of 1,484 recordings and found that the discharge recordings were successfully tagged as distinctly different from baseline (wet) in 94% of cases, with distinct differences shown for all 5 SMs in 87.5% of cases.
- The largest change from baseline was documented for SM2 (218%).
- As a complementary test, they further evaluated 72 untagged admissions and the discharge recordings from 9 patients and demonstrated for all 5 SMs. The system successfully segregated the recordings into 2 distinct unknown sets, which, when unblinded, were shown to correspond to the 2 different clinical statuses (ie, admission/discharge), with the exception of only 1 recording (2.2%).
The current observations provided substantial proof of concept that this novel automated speech processing and analysis approach can reliably identify these differences between 2 states of pulmonary congestion in patients with HF at the time of hospitalization for ADHF and following a full course of inpatient treatment.
The authors concluded, “Automated speech analysis technology can identify voice alterations reflective of HF status. This platform is expected to provide a valuable contribution to in-person and remote follow-up of patients with HF, by alerting to imminent deterioration, thereby reducing hospitalization rates.”
In an accompanying editorial, Dr Ravindra and Dr Kao wrote, “ Active speech analysis as described by Dr Amir et al. is an important advance toward expanding the tools available to assess patients with HF. Although nascent, the use of commonly available mobile technologies suggests the potential for wide use compared with highly invasive strategies requiring dedicated hardware. Extensive development and validation are required before clinical use, but success in a use case such as HearO may pave the way for even more convenient and generalizable strategies.”
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