Sam Brusco, Associate Editor05.24.22
Artificial intelligence (AI)-driven healthtech company Anumana has received U.S. Food and Drug Administration (FDA) Breakthrough Device Designation for its AI-enhanced, ECG-based pulmonary hypertension (PH) early detection algorithm.
The precise and non-invasive screening tool supports earlier PH diagnosis, which may otherwise not be noticed until the disease has advanced, delaying treatment and limiting efficacy while adversely impacting outcomes.
“Electrophysiology waveforms hold immense untapped potential for detecting diseases earlier in their natural history, particularly for conditions in which earlier diagnosis and therapeutic intervention can prolong survival and improve quality of life,” Venky Soundararajan, Ph.D., co-founder and chief scientific officer of Anumana told the press. “The FDA's Breakthrough Device Designation for Anumana's PH Early Detection Algorithm is one step forward for the field of ECG AI overall, and more saliently, a giant leap forward for PH patients.”
The AI-enhanced ECG algorithm was designed to detect PH at an earlier stage via the widespread availability of 12-lead ECGs in various care settings. The algorithm is enabled by the nference platform, which gathers insight from over six million de-identified patient records—including over eight million ECGs.
The algorithm was created via a partnership between data scientists and physicians at Anumana, Janssen Research & Development, and Mayo Clinic.
If approved the early detection algorithm will be available as a Software as a Medical Device (SaMD) to be downloaded on a clinician’s smartphone, tablet, or computer, or accessed via the Cloud via EHR or ECG information management system interface.
Using a 12-lead ECG, the algorithm analyzes voltage-time data and provides prediction of PH likelihood within seconds.
“While therapeutic options for patients with pulmonary hypertension have evolved in recent years, we have not seen significant advancement in reducing the time from symptom onset to diagnosis – and our hypothesis was that data science could help change this,” said Najat Khan, PhD, chief data science officer and Global Head, Strategy and Operations, Janssen Research & Development. “We leveraged the power of artificial intelligence, the ingenuity of our data scientists and researchers, and deep collaboration to co-develop this AI-based innovation, with the ultimate goal of helping to improve patient outcomes and transform the trajectory of this devastating disease.”
“Early diagnosis of pulmonary hypertension is paramount due to its progression and potential severity,” said Paul Friedman, M.D., chair of the Department of Cardiovascular Medicine at Mayo Clinic. “The addition of AI to a standard ECG—a painless, inexpensive, widely used test that is routinely performed—transforms the ECG into a screening tool for PH, with the opportunity to improve outcomes via early detection by guiding appropriate testing.”
The precise and non-invasive screening tool supports earlier PH diagnosis, which may otherwise not be noticed until the disease has advanced, delaying treatment and limiting efficacy while adversely impacting outcomes.
“Electrophysiology waveforms hold immense untapped potential for detecting diseases earlier in their natural history, particularly for conditions in which earlier diagnosis and therapeutic intervention can prolong survival and improve quality of life,” Venky Soundararajan, Ph.D., co-founder and chief scientific officer of Anumana told the press. “The FDA's Breakthrough Device Designation for Anumana's PH Early Detection Algorithm is one step forward for the field of ECG AI overall, and more saliently, a giant leap forward for PH patients.”
The AI-enhanced ECG algorithm was designed to detect PH at an earlier stage via the widespread availability of 12-lead ECGs in various care settings. The algorithm is enabled by the nference platform, which gathers insight from over six million de-identified patient records—including over eight million ECGs.
The algorithm was created via a partnership between data scientists and physicians at Anumana, Janssen Research & Development, and Mayo Clinic.
If approved the early detection algorithm will be available as a Software as a Medical Device (SaMD) to be downloaded on a clinician’s smartphone, tablet, or computer, or accessed via the Cloud via EHR or ECG information management system interface.
Using a 12-lead ECG, the algorithm analyzes voltage-time data and provides prediction of PH likelihood within seconds.
“While therapeutic options for patients with pulmonary hypertension have evolved in recent years, we have not seen significant advancement in reducing the time from symptom onset to diagnosis – and our hypothesis was that data science could help change this,” said Najat Khan, PhD, chief data science officer and Global Head, Strategy and Operations, Janssen Research & Development. “We leveraged the power of artificial intelligence, the ingenuity of our data scientists and researchers, and deep collaboration to co-develop this AI-based innovation, with the ultimate goal of helping to improve patient outcomes and transform the trajectory of this devastating disease.”
“Early diagnosis of pulmonary hypertension is paramount due to its progression and potential severity,” said Paul Friedman, M.D., chair of the Department of Cardiovascular Medicine at Mayo Clinic. “The addition of AI to a standard ECG—a painless, inexpensive, widely used test that is routinely performed—transforms the ECG into a screening tool for PH, with the opportunity to improve outcomes via early detection by guiding appropriate testing.”