Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks.

ABSTRACT: For the one billion sufferers of respiratory disease, managing their disease with inhalers crucially influences their quality of life. Generic treatment plans could be improved with the aid of computational models that account for patient-specific features such as breathing pattern, lung pathology and morphology. Therefore, we aim to develop and validate an automated computational framework for patient-specific deposition modelling. To that end, an image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images. We evaluated the airway and lung morphology produced by our image processing framework, and assessed deposition compared to in vivo data. The 2D-to-3D image processing reproduces airway diameter to 9% median error com

SUBMITTER: Williams J 

PROVIDER: S-EPMC10817191 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature