AI Case Study

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."