Kanhere Adway, Navarathna Nithya, Yi Paul H, Parekh Vishwa S, Pickle Jerrah, Cloak Christine C, Ernst Thomas, Chang Linda, Li Dongdong, Redline Susan, Isaiah Amal
University of Maryland Baltimore, Otorhinolaryngology, Baltimore, Maryland, United States.
University of Maryland Institute for Health Computing , Bethesda, Maryland, United States.
Am J Respir Crit Care Med. 2025 Jun 3. doi: 10.1164/rccm.202409-1748OC.
One in ten children experiences sleep-disordered breathing (SDB). Untreated SDB is associated with poor cognition, but the underlying mechanisms are less understood.
We assessed the relationship between magnetic resonance imaging (MRI)-derived upper airway volume and children's cognition and regional cortical gray matter volumes.
We used five-year data from the Adolescent Brain Cognitive Development study (n=11,875 children, 9-10 years at baseline). Upper airway volumes were derived using a deep learning model applied to 5,552,640 brain MRI slices. The primary outcome was the Total Cognition Composite score from the National Institutes of Health Toolbox (NIH-TB). Secondary outcomes included other NIH-TB measures and cortical gray matter volumes.
The habitual snoring group had significantly smaller airway volumes than non-snorers (mean difference=1.2 cm; 95% CI, 1.0-1.4 cm; P<0.001). Deep learning-derived airway volume predicted the Total Cognition Composite score (estimated mean difference=3.68 points; 95% CI, 2.41-4.96; P<0.001) per one-unit increase in the natural log of airway volume (~2.7-fold raw volume increase). This airway volume increase was also associated with an average 0.02 cm increase in right temporal pole volume (95% CI, 0.01-0.02 cm; P<0.001). Similar airway volume predicted most NIH-TB domain scores and multiple frontal and temporal gray matter volumes. These brain volumes mediated the relationship between airway volume and cognition.
We demonstrate a novel application of deep learning-based airway segmentation in a large pediatric cohort. Upper airway volume is a potential biomarker for cognitive outcomes in pediatric SDB, offers insights into neurobiological mechanisms, and informs future studies on risk stratification. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).
十分之一的儿童患有睡眠呼吸障碍（SDB）。未经治疗的SDB与认知能力差有关，但其潜在机制尚不太清楚。
我们评估了磁共振成像（MRI）得出的上气道容积与儿童认知及区域皮质灰质容积之间的关系。
我们使用了青少年大脑认知发展研究的五年数据（n = 11875名儿童，基线时年龄为9至10岁）。上气道容积通过应用于5552640张脑部MRI切片的深度学习模型得出。主要结局是美国国立卫生研究院工具箱（NIH-TB）的总认知综合评分。次要结局包括其他NIH-TB测量指标和皮质灰质容积。
习惯性打鼾组的气道容积明显小于非打鼾者（平均差异 = 1.2 cm；95%置信区间，1.0 - 1.4 cm；P < 0.001）。深度学习得出的气道容积每增加一个单位的气道容积自然对数（原始容积增加约2.7倍），就可预测总认知综合评分（估计平均差异 = 3.68分；95%置信区间，2.41 - 4.96；P < 0.001）。这种气道容积增加还与右侧颞极容积平均增加0.02 cm有关（95%置信区间，0.01 - 0.02 cm；P < 0.001）。类似的气道容积可预测大多数NIH-TB领域评分以及多个额叶和颞叶灰质容积。这些脑容积介导了气道容积与认知之间的关系。
我们展示了基于深度学习的气道分割在大型儿科队列中的新应用。上气道容积是小儿SDB认知结局的潜在生物标志物，为神经生物学机制提供了见解，并为未来的风险分层研究提供了信息。本文为开放获取文章，根据知识共享署名非商业性无衍生作品许可协议4.0（http://creativecommons.org/licenses/by-nc-nd/4.0/）发布。
