How Computational Modeling and Clinical Collaboration Are Transforming Care

Cincinnati Children’s Division of Pulmonary Medicine is advancing a new way to understand airway disease—one that blends engineering precision with clinical insight to better guide care for some of the most complex pediatric patients. At the center of this work is Alister Bates, PhD, a pulmonary researcher whose path to pediatric medicine began far from the hospital setting.

Applying Aerospace Engineering to the Human Airway

Before turning his focus to pediatric airways, Bates trained as an aerospace engineer at the University of Cambridge and worked in Formula One racing, designing aerodynamic components for an F1 team. While the work fulfilled a lifelong ambition, he found himself drawn to applications with a more direct human impact.

Bates went on to earn his doctorate degree from Imperial College London, where he began applying aerodynamic principles to the human airway. Early in that work, he saw how airflow modeling could resolve diagnostic uncertainty for patients with complex breathing problems—experiences that shaped the direction of his research and ultimately led him to Cincinnati Children’s in 2016.

Modeling the Airway as It Truly Moves

Since joining Cincinnati Children’s, Bates and his Respiratory Modeling Group team have developed patient-specific virtual airway models that move realistically through the breathing cycle. Using high-speed dynamic imaging rather than static snapshots, the models capture how an airway narrows, collapses or stabilizes during inhalation, exhalation and pauses between breaths.

The realistic motion is a key differentiator, particularly for conditions such as obstructive sleep apnea and tracheomalacia, where airway collapse plays a central role. By synchronizing airflow data with airway motion, the models allow researchers to distinguish whether collapse is driven by pressure changes, neuromuscular control or structural weakness—insight that is difficult to obtain through conventional imaging alone.

Measuring the Energy Cost of Breathing

The second and most clinically promising differentiator of Bates’ work is the ability to quantify how much energy a patient expends to breathe—and precisely where that energy is being lost within the airway.

While total work of breathing has been studied for decades, Bates’ group uses computational fluid dynamics to map energy expenditure along the airway, identifying bottlenecks that disproportionately increase breathing effort. Even small areas of narrowing can drive dramatic increases in energy use. In neonates with tracheomalacia, for example, Bates’ team has observed a seven-fold increase in energy expenditure compared with unaffected infants.

“For premature babies, that energy matters,” Bates says. When infants are forced to devote excessive energy to breathing, less remains for growth and development. By distinguishing whether breathing effort is driven primarily by lung mechanics or airway collapse, the models can help clinicians target interventions more effectively.

Bates and his fellow researchers are now applying the same approach to older children with complex airway histories, including patients who have undergone multiple surgeries. In these cases, the team can rank the most significant contributors to breathing difficulty, helping otolaryngologists prioritize which issues to address first.

Moving Toward a Clinical Tool for Pulmonary Care

Bates believes energy expenditure may provide the missing metric needed to bring airflow modeling into routine clinical decision-making. He often parallels the work with blood-flow analysis, which is already used clinically to guide cardiovascular care.

“Blood flow simulation became clinically useful because there was a number that mattered, and modeling could match that number non-invasively,” he explains. “In the airway, energy expenditure has the potential to be that number.”

While computational fluid dynamics remains a research tool rather than a Food and Drug Administration-approved clinical technology, Bates sees its greatest near-term value in patients with the most complicated care situations—those for whom standard evidence offers limited guidance. By building evidence in these challenging cases, his team aims to lay the groundwork for broader clinical adoption.

Purpose Through Collaboration

For Bates, the most rewarding aspect of his work is not the modeling itself, but the collaboration it enables.

“What makes what I do special is working with clinicians who are open to engineering input and willing to use that information to help patients,” he says. “When a physician brings a difficult case, and we can provide evidence that informs care, that’s incredibly meaningful.”

Bates says he’s found his purpose in work that bridges disciplines—and in seeing research insights translate into better understanding for individual patients and entire patient populations alike.

(Published March 2026)

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