AI vs. spirometry for COPD
Researchers in the United States and Romania, led by Paul Bogdan, PhD, at the University of Southern California Viterbi School of Engineering, developed a model that predicted COPD with an accuracy of almost 99% (98.66%) and avoids many of the shortcomings of spirometry, Dr. Bogdan said.
USC Viterbi School of Engineering
Dr. Bogdan
The models developed by Dr. Bogdan and collaborators use a different principle than existing AI platforms, Dr. Bogdan said. They analyze the properties of the data. As he explained it, they exploit what he called the “geometry of these data” to make inferences and decisions on a patient’s risk for COPD.
“Deep learning is very good for images, for videos, but it doesn’t work that well for signals,” said Mihai Udrescu, PhD, one of the Romanian collaborators. “But if we process the data with the technique brought up by Paul [Bogdan] and his team at USC, deep learning also works well on physiological signals.”
Dr. Paul Bogdan
Dr. Paul Bogdan, (left) and research associate Chenzhong Yin are standing in front of a summary of their research.
Said Dr. Bogdan, “Nobody thought about using physiological signals to predict COPD before this work. They used spirometry, but spirometry is cumbersome and several steps have to be performed in order to have an accurate spirometry.” His team’s AI models extract and analyze risk data based on 10 minutes of monitoring.
Dr. Udrescu
This technology also has the potential to improve accessibility of COPD screening, Dr. Udrescu said. “It can democratize the access to the health care because you don’t need to travel for 100 or 200 miles to see a specialist,” he said. “You just send an app to the mobile phone of a patient, the person puts on a smart watch and wears it for a couple of minutes and the data is either recorded locally or is transmitted and it is analyzed.” The computations can be done locally and in a matter of minutes, he said.
In Scotland, a 12-month feasibility study is underway to evaluate an AI model to identify COPD patients at greatest risk for adverse events. A press release from Lenus, the company developing the technology, said the study will use a COPD multidisciplinary team to consider real-time AI model outputs to enable proactive patient encounters and reduce emergency care visits.
Researchers in Paris built an AI model that showed a 68% accuracy in distinguishing people with asthma from people with COPD in administrative medical databases (BMC Pulmon Med. 2022 Sep 20. doi: 10.1186/s12890-022-02144-2). They found that asthma patients were younger than those with COPD (a mean of 49.9 vs. 72.1 years) and that COPD occurred mostly in men (68% vs. 33%). And an international team of researchers reported that an AI model that used chest CT scans determined that ever-smokers with COPD who met the imaging criteria for bronchiectasis were more prone to disease exacerbations (Radiology. 2022 Dec 13. doi: 10.1148/radiol.221109).