AI Hotline: Startup Analyzes Emergency Calls to Identify Cardiac Arrest Victims

by Isha Salian

“911. What’s your emergency?”

Denmark-based startup Corti knows that the stakes are high when dialing emergency services. So it built an AI tool to provide immediate feedback and guidance to emergency call responders, helping them ask the right questions and quickly identify highly acute cases.

Its speech-recognition software, Corti AI, can cut down the number of undetected out-of-hospital cardiac arrests by almost half, and help responders more quickly dispatch emergency services.

Corti AI can detect cardiac arrest within 50 seconds of an emergency phone call, which is more than 10 seconds faster than dispatchers unaided by AI — and every second counts.

Cardiac arrest “is the highest critical diagnosis there is,” said Lars Maaloee, Corti’s co-founder and chief technology officer. When the heart stops beating, all organs — including the brain — are deprived of oxygen. Immediate CPR is critical for the patient to have any chance of survival. “Responders need to act extremely fast in sending the ambulance and instructing the bystander on what to do.”

If cardiac arrest treatment is delayed by more than 10 minutes, a victim’s chance of survival is less than 5 percent.

A study found that Corti AI identified cardiac arrest 95 percent of the time from emergency call audio, while emergency dispatchers in Copenhagen spotted 73 percent of cases.

Corti’s solution is currently deployed throughout the Copenhagen metropolitan area, covering nearly 2 million residents. A member of the NVIDIA Inception program, the startup was a finalist at last year’s GTC Europe Inception Awards.

Making the Right Call

On the desk of each Corti-enabled emergency dispatcher sits a white cylinder a few inches tall, similar to a miniature lampshade or Bluetooth speaker. Called the Orb, it connects to a responder’s telephone and captures audio during emergency calls.

Corti Orb
Each Orb contains an NVIDIA Jetson TX2 module that connects to an emergency dispatcher’s phone line. (Image courtesy of Corti.)

Designed in collaboration with Danish lamp designer Tom Rossau, each Orb houses a powerful NVIDIA Jetson TX2 module. With the JetPack SDK, Corti can run multiple neural networks on the device, including a combination of CNNs and RNNs.

The Orb identifies relevant parts of the phone conversation, even looking for clues by sifting through background noise and non-verbal signals like breathing patterns.

Audio snippets are then sent from the Orb to Corti’s servers. Powered by NVIDIA GPUs, the servers rapidly send back insights that are displayed on the emergency responder’s computer screen in a desktop interface called Corti Triage.

The Triage tool guides dispatchers during the call, suggesting questions to ask and alerting responders of possible serious conditions.

“Throughout the medical sector, there are a lot of decisions taken by a single person that can have major consequences,” said Maaloee. “Though the dispatchers are highly trained, it’s always possible to help them make better decisions.”

Though trained on data from past emergency calls, the AI can be easily customized to the protocol of a specific department. To help emergency call centers improve in the long term, a complementary software module called Corti Review analyzes data from each call and provides feedback to dispatchers and managers.

Corti is collaborating with the University of Copenhagen and the University of Washington to study the efficacy of its AI tools.

The startup is using transfer learning to train its algorithms to process languages other than English, and is expanding its services to other cities in Europe. Corti is also making its AI more flexible, so that the software can be used to analyze other types of medical conversations — such as a primary care physician’s interaction with a patient.