TY - JOUR
T1 - Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks
AU - Williams, Josh
AU - Ahlqvist, Haavard
AU - Cunningham, Alexander
AU - Kirby, Andrew
AU - Katz, Ira
AU - Fleming, John
AU - Conway, Joy
AU - Cunningham, Steve
AU - Ozel, Ali
AU - Wolfram, Uwe
N1 - Copyright: © 2024 Williams et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2024/1/26
Y1 - 2024/1/26
N2 - 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 compared to ground truth segmentations, but is sensitive to outliers of up to 33% due to lung outline noise. Predicted regional deposition gave 5% median error compared to in vivo measurements. The proposed framework is capable of providing patient-specific deposition measurements for varying treatments, to determine which treatment would best satisfy the needs imposed by each patient (such as disease and lung/airway morphology). Integration of patient-specific modelling into clinical practice as an additional decision-making tool could optimise treatment plans and lower the burden of respiratory diseases.
AB - 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 compared to ground truth segmentations, but is sensitive to outliers of up to 33% due to lung outline noise. Predicted regional deposition gave 5% median error compared to in vivo measurements. The proposed framework is capable of providing patient-specific deposition measurements for varying treatments, to determine which treatment would best satisfy the needs imposed by each patient (such as disease and lung/airway morphology). Integration of patient-specific modelling into clinical practice as an additional decision-making tool could optimise treatment plans and lower the burden of respiratory diseases.
KW - Humans
KW - Quality of Life
KW - Neural Networks, Computer
KW - Imaging, Three-Dimensional/methods
KW - Image Processing, Computer-Assisted/methods
KW - Lung/diagnostic imaging
U2 - 10.1371/journal.pone.0297437
DO - 10.1371/journal.pone.0297437
M3 - Article
C2 - 38277381
SN - 1932-6203
VL - 19
SP - e0297437
JO - PLoS ONE
JF - PLoS ONE
IS - 1
ER -