Daily Respiratory Research Analysis
Across respiratory research, three studies stand out: a cohort study links early-life ozone exposure to higher odds of asthma and wheeze at ages 4–6; an open-source chest radiograph AI score robustly stratifies long-term respiratory mortality risk in an Asian screening cohort; and a novel 3D printing method (PixelPrint 4D) fabricates lifelike deformable lung phantoms that closely replicate patient respiratory motion and CT attenuation for technology evaluation.
Summary
Across respiratory research, three studies stand out: a cohort study links early-life ozone exposure to higher odds of asthma and wheeze at ages 4–6; an open-source chest radiograph AI score robustly stratifies long-term respiratory mortality risk in an Asian screening cohort; and a novel 3D printing method (PixelPrint 4D) fabricates lifelike deformable lung phantoms that closely replicate patient respiratory motion and CT attenuation for technology evaluation.
Research Themes
- Air pollution and pediatric respiratory outcomes
- AI-based risk stratification from chest radiographs
- Advanced imaging phantoms and respiratory motion modeling
Selected Articles
1. PixelPrint 4D : A 3D Printing Method of Fabricating Patient-Specific Deformable CT Phantoms for Respiratory Motion Applications.
The authors introduce PixelPrint 4D, a voxel-by-voxel 3D printing workflow using flexible materials to fabricate patient-specific deformable lung phantoms from 4DCT. The phantom closely matched patient anatomy and nonrigid deformation: SSIM was 0.93; mean tumor motion errors were ≤0.7±0.6 mm per axis; and attenuation–volume change relationships were statistically indistinguishable from the patient (ANCOVA P=0.83).
Impact: This method provides a realistic, reproducible respiratory motion testbed surpassing existing phantoms, enabling rigorous evaluation of CT motion compensation and tumor tracking, including AI algorithms.
Clinical Implications: While preclinical, the technology can standardize benchmarking of motion-reduction, 4D dose calculation, and tracking algorithms, accelerating translation of safer, more accurate respiratory imaging and radiotherapy.
Key Findings
- High structural fidelity: SSIM between phantom and patient lungs was 0.93.
- Realistic motion: mean tumor motion errors ≤0.7±0.6 mm in each orthogonal direction.
- Physiologic attenuation–volume coupling preserved: ANCOVA P=0.83 indicating no significant difference vs patient.
- Voxel-wise density control via PixelPrint produced realistic textures and attenuation profiles under compression.
Methodological Strengths
- Quantitative validation against patient 4DCT (SSIM, displacement, attenuation-volume coupling).
- Voxel-by-voxel material deposition enabling realistic heterogeneity and deformation.
Limitations
- Derived from a single patient 4DCT case; generalizability to diverse anatomies/motions requires expansion.
- Compression-based pseudo-4D generation may not capture all in vivo mechanics (e.g., hysteresis, airflow effects).
Future Directions: Scale to multi-patient libraries and left/right lungs, integrate airflow mechanics, and establish multi-center benchmarking datasets for evaluating CT motion-compensation and AI reconstruction/tracking.
OBJECTIVES: Respiratory motion poses a significant challenge for clinical workflows in diagnostic imaging and radiation therapy. Many technologies such as motion artifact reduction and tumor tracking have been developed to compensate for its effect. To assess these technologies, respiratory motion phantoms (RMPs) are required as preclinical testing environments, for instance, in computed tomography (CT). However, current CT RMPs are highly simplified and do not exhibit realistic tissue structures or deformatio
2. Early-Life Ozone Exposure and Asthma and Wheeze in Children.
In a pooled prospective cohort of 1,188 term-born children in six US cities with relatively low ozone levels, each 2 ppb higher early-life ambient ozone was associated with higher odds of current asthma (OR 1.31) and wheeze (OR 1.30) at ages 4–6, supported by mixture analyses (BKMR). Associations were not evident at ages 8–9.
Impact: Provides policy-relevant evidence that even low-level early-life ozone exposure increases early childhood asthma and wheeze risk, emphasizing air-quality regulation and pediatric prevention.
Clinical Implications: Clinicians should consider early-life ambient ozone as a modifiable risk factor when counseling families, especially in urban settings, and advocate for air quality improvements to reduce pediatric asthma burden.
Key Findings
- Per 2 ppb increase in early-life ozone, odds of current asthma at age 4–6 were 1.31 (95% CI 1.02–1.68).
- Per 2 ppb increase in early-life ozone, odds of current wheeze at age 4–6 were 1.30 (95% CI 1.05–1.64).
- Bayesian kernel machine regression indicated positive associations for ozone within pollutant mixtures.
- No significant associations were observed for outcomes at ages 8–9 years.
Methodological Strengths
- Prospective multisite cohort with validated spatiotemporal exposure modeling and mixture analysis (BKMR).
- Adjustment for anthropometric, socioeconomic, and neighborhood factors across six US cities.
Limitations
- Outcomes based on caregiver report; potential misclassification.
- Associations attenuated by ages 8–9; temporal dynamics and residual confounding remain possible.
Future Directions: Replicate in diverse populations, link to objective lung function and biomarkers, and quantify benefits of ozone mitigation on pediatric respiratory outcomes.
IMPORTANCE: Ozone (O3) is the most frequently exceeded air pollutant standard in the US. While short-term exposure is associated with acute respiratory health, the epidemiologic evidence linking postnatal O3 exposure to childhood asthma and wheeze is inconsistent and rarely evaluated as a mixture with other air pollutants. OBJECTIVES: To determine associations between ambient O3 and subsequent asthma and wheeze outcomes both independently and in mixture with fine particulate matter and nitrogen dioxide in regions with low annual O3 concentrations. DESIGN, SETTING, AND PARTICIPANTS: This cohort study consisted of a pooled, multisite analysis across 6 US cities using data from the prospective ECHO-PATHWAYS consortium (2007-2023). Included children had complete airway surveys, complete address histories from age 0 to 2 years, and a full term birth (≥37 weeks). Logistic regression and bayesian kernel machine regression (BKMR) mixture analyses were adjusted for child anthropomorphic, socioeconomic, and neighborhood factors. EXPOSURES: Exposure to ambient O3 in the first 2 years of life derived from a validated point-based spatiotemporal model using residential address histories. MAIN OUTCOMES AND MEASURES: The primary outcome was asthma and wheeze at ages 4 to 6 years; the secondary outcome was asthma and wheeze at ages 8 to 9 years. Outcomes were based on caregiver reports derived from a validated survey. RESULTS: The analytic sample of 1188 participants had a mean (SD) age of 4.5 (0.6) years at the age 4 to 6 years visit and consisted of 614 female participants (51.7%) and 663 mothers who had a bachelor's degree or higher (55.8%). The mean (SD) O3 concentration was 26.1 (2.9) parts per billion (ppb). At age 4 to 6 years, 148 children had current asthma (12.3%) and 190 had current wheeze (15.8%). The odds ratio per 2 ppb higher O3 concentration was 1.31 (95% CI, 1.02-1.68) for current asthma and 1.30 (95% CI, 1.05-1.64) for current wheeze at age 4 to 6 years; null associations were observed for outcomes at age 8 to 9 years, and for sensitivity covariate adjustment. BKMR suggested that higher exposure to O3 in mixture was associated with current asthma and wheeze in early childhood. CONCLUSIONS AND RELEVANCE: In this cohort study with relatively low ambient O3 exposure, early-life O3 was associated with asthma and wheeze outcomes at age 4 to 6 years and in mixture with other air pollutants but not at age 8 to 9 years. Regulating and reducing exposure to ambient O3 may help reduce the significant public health burden of asthma among US children.
3. Predicting Respiratory Disease Mortality Risk Using Open-Source AI on Chest Radiographs in an Asian Health Screening Population.
In 36,924 screened adults with median 11-year follow-up, the open-source CXR-Lung-Risk score from baseline chest radiographs independently predicted respiratory disease mortality (adjusted HR per 5-year risk age: 2.01), adding value beyond clinical factors. Time-series clustering of risk trajectories highlighted subgroups with persistently high imaging-based risk.
Impact: Demonstrates scalable, open-source imaging biomarker for long-term respiratory mortality risk stratification in an Asian population, informing targeted prevention and follow-up.
Clinical Implications: CXR-based AI risk can complement clinical risk factors to identify high-risk individuals for intensified smoking cessation support, lung health surveillance, and preventive interventions.
Key Findings
- Open-source CXR-Lung-Risk predicted respiratory disease mortality with adjusted HR per 5-year risk age of 2.01 (95% CI 1.76–2.39).
- Model added significant prognostic value beyond clinical factors (likelihood ratio testing).
- Time-series clustering over 3 years identified subgroups with persistently high imaging-derived risk trajectories.
- External testing supported generalizability of baseline score for lung disease/lung cancer mortality.
Methodological Strengths
- Large cohort (n=36,924) with long follow-up and competing risk analysis adjusted for clinical covariates.
- External testing and open-source algorithm enhance transparency and reproducibility.
Limitations
- Single-center retrospective cohort; potential selection biases and imaging protocol variability.
- Cause-of-death misclassification and residual confounding cannot be fully excluded.
Future Directions: Prospective, multi-center validation with integration into clinical workflows; assess intervention response when AI-identified high-risk groups receive targeted prevention.
Purpose To assess the prognostic value of an open-source deep learning-based chest radiographs algorithm, CXR-Lung-Risk, for stratifying respiratory disease mortality risk among an Asian health screening population using baseline and follow-up chest radiographs. Materials and Methods This single-center, retrospective study analyzed chest radiographs from individuals who underwent health screenings between January 2004 and June 2018. The CXR-Lung-Risk scores from baseline chest radiographs were externally tested for predicting mortality due to lung disease or lung cancer, using competing risk analysis, with adjustments made for clinical factors. The additional value of these risk scores beyond clinical factors was evaluated using the likelihood ratio test. An exploratory analysis was conducted on the CXR-Lung-Risk trajectory over a 3-year follow-up period for individuals in the highest quartile of baseline respiratory disease mortality risk, using a time-series clustering algorithm. Results Among 36 924 individuals (median age, 58 years [IQR, 53-62 years]; 22 352 male), 264 individuals (0.7%) died of respiratory illness, over a median follow-up period of 11.0 years (IQR, 7.8-12.7 years). CXR-Lung-Risk predicted respiratory disease mortality (adjusted hazard ratio [HR] per 5 years: 2.01; 95% CI: 1.76, 2.39;