Last month, the Iowa Initiative for Artificial Intelligence (IIAI) approved six pilot grant proposals submitted by members of the Carver College of Medicine. These projects will provide subsidized and collaborative time for researchers to work with IIAI members on new ideas incorporating artificial intelligence (AI) and machine learning (ML) into clinical processes and patient care. Two projects have come to the Department of Internal Medicine. The first will use AI to assess syncope risks with a score generated from a series of standardized inputs.
The second has been awarded to a team led by Kamonpun Ussavarungsi, MD, clinical assistant professor in the Division of Pulmonary, Critical Care, and Occupational Medicine. Ussavarungsi’s project will develop a model to predict idiopathic pulmonary fibrosis (IPF) outcomes. IPF is a form of interstitial lung disease that scars the lungs and makes it difficult for people to breathe. Because IPF has unknown causes, it progresses in a variety of manners that can be difficult to gauge in terms of severity or stage.
“We intend to use artificial intelligence to develop a clinical model that will predict IPF progression and survival outcomes,” Ussavarungsi said. She and her team have developed quantitative parameters from thin-section computed tomography (CT) scans. “We plan to compare texture-based CT quantification and biomechanical measurements of structural lung damage before and after antifibrotic therapy.”
The results of these comparisons should result in an aid in clinical decision making. “By correlating the CT characteristics and mechanical measurements with IPF phenotypes,” Ussavarungsi said, the team theorizes that an AI-based prediction could be formed. Armed with a refined and reliable estimate of what might lie ahead for a particular patient, clinicians could recommend certain treatments or other responses that could slow IPF progression or better evaluate who may need a lung transplant.
Other members of Ussavarungi’s team include Internal Medicine’s Richard Hoffman, MD, MPH; Alicia Gerke, MD, MBA; and Nabeel Hamzeh, MD; the Department of Radiology’s Archana Laroia, MD; Junfeng Guo, PhD; and Joseph Reinhardt, MS, PhD.
Using AI to predict lung disease progression
Last month, the Iowa Initiative for Artificial Intelligence (IIAI) approved six pilot grant proposals submitted by members of the Carver College of Medicine. These projects will provide subsidized and collaborative time for researchers to work with IIAI members on new ideas incorporating artificial intelligence (AI) and machine learning (ML) into clinical processes and patient care. Two projects have come to the Department of Internal Medicine. The first will use AI to assess syncope risks with a score generated from a series of standardized inputs.
The second has been awarded to a team led by Kamonpun Ussavarungsi, MD, clinical assistant professor in the Division of Pulmonary, Critical Care, and Occupational Medicine. Ussavarungsi’s project will develop a model to predict idiopathic pulmonary fibrosis (IPF) outcomes. IPF is a form of interstitial lung disease that scars the lungs and makes it difficult for people to breathe. Because IPF has unknown causes, it progresses in a variety of manners that can be difficult to gauge in terms of severity or stage.
“We intend to use artificial intelligence to develop a clinical model that will predict IPF progression and survival outcomes,” Ussavarungsi said. She and her team have developed quantitative parameters from thin-section computed tomography (CT) scans. “We plan to compare texture-based CT quantification and biomechanical measurements of structural lung damage before and after antifibrotic therapy.”
The results of these comparisons should result in an aid in clinical decision making. “By correlating the CT characteristics and mechanical measurements with IPF phenotypes,” Ussavarungsi said, the team theorizes that an AI-based prediction could be formed. Armed with a refined and reliable estimate of what might lie ahead for a particular patient, clinicians could recommend certain treatments or other responses that could slow IPF progression or better evaluate who may need a lung transplant.
Other members of Ussavarungi’s team include Internal Medicine’s Richard Hoffman, MD, MPH; Alicia Gerke, MD, MBA; and Nabeel Hamzeh, MD; the Department of Radiology’s Archana Laroia, MD; Junfeng Guo, PhD; and Joseph Reinhardt, MS, PhD.