top of page

AI Case Study

Autonomous Healthcare detects different types of ventilator asynchrony in ICU patients with machine learning

Autonomous Healthcare has developed technology that can manage a patient's ventilation in the ICU. Using machine learning, it is able to detect anomalies between the patient's own and a mechanical ventilator's inhalation and exhalation patterns. The system was trained on data of waveforms from patients on ventilators and learned the signatures of different asynchrony types. In its first assessment on data it achieved the same accuracy as human doctors, and it is now being tested with real patients.



Healthcare Providers And Services

Project Overview

Autonomous Healthcare, based in Hoboken, N.J., is "designing and building some of the first AI systems for the ICU. These technologies are intended to provide vigilant and nuanced care, as if an expert were at the patient’s bedside every second, carefully calibrating treatment. Such systems could relieve the burden on the overtaxed staff in critical-care units. What’s more, if the technology helps patients get out of the ICU sooner, it could bring down the skyrocketing costs of health care. We’re focusing initially on hospitals in the United States, but our technology could be useful all around the world as populations age and the prevalence of chronic diseases grows. Our methodologies spring from an unlikely source: the aerospace industry."

When it comes to mechanical ventilators, "mismatches between the patient’s demand and the machine’s delivery are all too common, which can cause a patient to 'fight the ventilator'.

The first step in addressing this problem is to detect it. Experienced respiratory therapists can identify different types of asynchrony if they continuously monitor the waveforms on a ventilator’s display screen indicating the pressure and flow. But in an ICU, one respiratory therapist typically oversees 10 or more patients and can’t possibly monitor all of them constantly.

We've designed a machine-learning framework that replicates that human expertise in detecting different types of asynchrony. To train our system, we used a data set of waveforms from patients on ventilators, in which each waveform had been evaluated by a panel of clinical experts. Our algorithm learned the signatures of different asynchrony types—such as a particular dip in the flow signal at a specific point in time. In our first assessments of the algorithm’s performance, we focused on what’s called cycling asynchrony, which is the most challenging type to detect. Here the ventilator’s initiation of the exhale doesn’t match the patient’s own exhalation. The accuracy of our algorithm in detecting cycling asynchrony in a new data set matched that of human experts.

We’re now testing the algorithm at Northeast Georgia Medical Center’s ICU to detect respiratory asynchrony in real patients and in real time. The technology has been incorporated into a clinical-decision support system, which is designed to help respiratory therapists assess a patient’s needs. This framework can also provide researchers with a tool to better understand the underlying causes of asynchrony and its impact on patients. Our long-term goal is to design mechanical ventilators that can automatically adjust their own settings in response to each patient’s needs."

Reported Results

In its first assessment on data, the system's accuracy matched that of human experts in detecting cycling asynchrony. The system is now being tested with real patients to detect respiratory asynchrony in real time, at Northeast Georgia Medical Center’s ICU.


Machine learning


R And D

Core Research And Development


"In a hospital’s intensive care unit (ICU), the sickest patients receive round-the-clock care as they lie in beds with their bodies connected to a bevy of surrounding machines. This advanced medical equipment is designed to keep an ailing person alive. Intravenous fluids drip into the bloodstream, while mechanical ventilators push air into the lungs. Sensors attached to the body track heart rate, blood pressure, and other vital signs, while bedside monitors graph the data in undulating lines. When the machines record measurements that are outside of normal parameters, beeps and alarms ring out to alert the medical staff to potential problems.

While this scene is laden with high tech, the technology isn’t being used to best advantage. Each machine is monitoring a discrete part of the body, but the machines aren’t working in concert. The rich streams of data aren’t being captured or analyzed. And it’s impossible for the ICU team—critical-care physicians, nurses, respiratory therapists, pharmacists, and other specialists—to keep watch at every patient’s bedside.

In the United States, ICUs are among the most expensive components of the health care system. About 55,000 patients are cared for in an ICU every day, with the typical daily cost ranging from US $3,000 to $10,000. The cumulative cost is more than $80 billion per year.
Today, more than half of ICU patients in the United States are over the age of 65—a demographic group that’s expected to grow from 46 million in 2014 to 74 million by 2030. Similar trends in Europe and Asia make this a worldwide problem. To meet the growing demand for acute clinical care, ICUs will need to increase their capacity as well as their capabilities.

In ICUs today, the data from the raft of bedside monitors is usually lost as the monitor screens refresh every few seconds. While some advanced ICUs are now trying to archive these measurements, they still struggle to mine the data for clinical insights."



"Data set of waveforms from patients on ventilators, in which each waveform had been evaluated by a panel of clinical experts. Our algorithm learned the signatures of different asynchrony types—such as a particular dip in the flow signal at a specific point in time."

bottom of page