From Image to Insight: A Review of Deep Learning Approaches for
Cystic Fibrosis Detection in Computed Tomography

D 2023

From Image to Insight: A Review of Deep Learning Approaches for Cystic Fibrosis Detection in Computed Tomography

KVAKOVÁ, Karolína a Daniel KVAK

Základní údaje

Originální název

From Image to Insight: A Review of Deep Learning Approaches for Cystic Fibrosis Detection in Computed Tomography

Název anglicky

From Image to Insight: A Review of Deep Learning Approaches for Cystic Fibrosis Detection in Computed Tomography

Autoři

KVAKOVÁ, Karolína a Daniel KVAK

Vydání

35. vyd. Praha, Medsoft 2023, 2023

Nakladatel

Creative Connections, a.s.

Další údaje

Typ výsledku

Stať ve sborníku

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Klíčová slova anglicky

artificial intelligence, computed tomography, computer-aided diagnosis, cystic fibrosis, deep learning

Změněno: 12. 12. 2023 11:01, Mgr. Daniel Kvak

Anotace

V originále

Cystic fibrosis (CF) is a genetic disease caused by mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene. This disorder causes a wide range of clinical complications, primarily affecting the respiratory and digestive systems and extending its impact to other physiological areas. Early detection and careful monitoring are paramount to mitigate disease progression and improve the quality of life of individuals with CF. Computed tomography (CT), particularly high-resolution CT (HRCT), has become a key diagnostic method for detecting pulmonary manifestations of CF. However, manual analysis of CT images requires a high level of expertise and is time consuming. The combination of artificial intelligence (AI) and deep learning with CT imaging predicts significant advances in CF detection. Deep learning, a subset of AI, uses neural networks to analyse complex morphological patterns indicative of disease from large datasets. This review traces the journey from the earliest attempts to use artificial intelligence in CF detection to recent advances made using deep learning algorithms. By exploring various deep learning architectures and their integration into clinical practice, this review illuminates the potential of these new technologies to revolutionize CF detection using CT imaging. Automated and accurate analysis enabled by deep learning aims to reduce the diagnostic burden on radiologists, speed up the diagnostic process and pave the way for timely and personalized therapeutic interventions, which is in line with the ultimate goal of improving patient care.

Citovat

KVAKOVÁ, Karolína a Daniel KVAK. From Image to Insight: A Review of Deep Learning Approaches for Cystic Fibrosis Detection in Computed Tomography. In Jiří Kofránek a kol. Medsoft 2023. 35. vyd. Praha: Creative Connections, a.s., 2023.

@inproceedings{2348880,
   author = {Kvaková, Karolína and Kvak, Daniel},
   address = {Praha},
   booktitle = {Medsoft 2023},
   edition = {35},
   editor = {Jiří Kofránek a kol.},
   keywords = {artificial intelligence, computed tomography, computer-aided diagnosis, cystic fibrosis, deep learning},
   howpublished = {tištěná verze "print"},
   location = {Praha},
   publisher = {Creative Connections, a.s.},
   title = {From Image to Insight: A Review of Deep Learning Approaches for Cystic Fibrosis Detection in Computed Tomography},
   year = {2023}
}

TY  - JOUR
ID  - 2348880
AU  - Kvaková, Karolína - Kvak, Daniel
PY  - 2023
TI  - From Image to Insight: A Review of Deep Learning Approaches for Cystic Fibrosis Detection in Computed Tomography
PB  - Creative Connections, a.s.
CY  - Praha
KW  - artificial intelligence, computed tomography, computer-aided diagnosis, cystic fibrosis, deep learning
N2  - Cystic fibrosis (CF) is a genetic disease caused by mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene. This disorder causes a wide range of clinical complications, primarily affecting the respiratory and digestive systems and extending its impact to other physiological areas. Early detection and careful monitoring are paramount to mitigate disease progression and improve the quality of life of individuals with CF. Computed tomography (CT), particularly high-resolution CT (HRCT), has become a key diagnostic method for detecting pulmonary manifestations of CF. However, manual analysis of CT images requires a high level of expertise and is time consuming. The combination of artificial intelligence (AI) and deep learning with CT imaging predicts significant advances in CF detection. Deep learning, a subset of AI, uses neural networks to analyse complex morphological patterns indicative of disease from large datasets. This review traces the journey from the earliest attempts to use artificial intelligence in CF detection to recent advances made using deep learning algorithms. By exploring various deep learning architectures and their integration into clinical practice, this review illuminates the potential of these new technologies to revolutionize CF detection using CT imaging. Automated and accurate analysis enabled by deep learning aims to reduce the diagnostic burden on radiologists, speed up the diagnostic process and pave the way for timely and personalized therapeutic interventions, which is in line with the ultimate goal of improving patient care.
ER  -

KVAKOVÁ, Karolína a Daniel KVAK. From Image to Insight: A Review of Deep Learning Approaches for Cystic Fibrosis Detection in Computed Tomography. In Jiří Kofránek a kol. \textit{Medsoft 2023}. 35. vyd. Praha: Creative Connections, a.s., 2023.

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