In this section
@article{Angelini:2021:10.1007/s11046-021-00546-0,
author = {Angelini, E and Shah, A},
doi = {10.1007/s11046-021-00546-0},
journal = {Mycopathologia},
pages = {733--737},
title = {Using artificial intelligence in fungal lung disease: CPA CT imaging as an example},
url = {http://dx.doi.org/10.1007/s11046-021-00546-0},
volume = {186},
year = {2021}
}
TY - JOUR
AB - This positioning paper aims to discuss current challenges and opportunities for artificial intelligence (AI) in fungal lung disease, with a focus on chronic pulmonary aspergillosis and some supporting proof-of-concept results using lung imaging. Given the high uncertainty in fungal infection diagnosis and analyzing treatment response, AI could potentially have an impactful role; however, developing imaging-based machine learning raises several specific challenges. We discuss recommendations to engage the medical community in essential first steps towards fungal infection AI with gathering dedicated imaging registries, linking with non-imaging data and harmonizing image-finding annotations.
AU - Angelini,E
AU - Shah,A
DO - 10.1007/s11046-021-00546-0
EP - 737
PY - 2021///
SN - 0301-486X
SP - 733
TI - Using artificial intelligence in fungal lung disease: CPA CT imaging as an example
T2 - Mycopathologia
UR - http://dx.doi.org/10.1007/s11046-021-00546-0
UR - https://www.ncbi.nlm.nih.gov/pubmed/33840005
VL - 186
ER -
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