Detecting Pulmonary Lesions in Low-Prevalence Real-World
Settings using Deep Learning

D 2024

Detecting Pulmonary Lesions in Low-Prevalence Real-World Settings using Deep Learning

KVAK, Daniel, Anna CHROMCOVÁ, Petra OVESNÁ, Jakub DANDÁR, Marek BIROŠ et. al.

Basic information

Original name

Detecting Pulmonary Lesions in Low-Prevalence Real-World Settings using Deep Learning

Name (in English)

Detecting Pulmonary Lesions in Low-Prevalence Real-World Settings using Deep Learning

Authors

KVAK, Daniel, Anna CHROMCOVÁ, Petra OVESNÁ, Jakub DANDÁR, Marek BIROŠ, Robert HRUBÝ, Daniel DUFEK and Marija PAJDAKOVIĆ

Edition

Singapore, Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023), p. 3-20, 2024

Publisher

Springer, Singapore

Other information

Type of outcome

Stať ve sborníku

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

ISBN

978-981-97-1334-9

DOI

http://dx.doi.org/10.1007/978-981-97-1335-6_1

Keywords in English

Artificial Intelligence, Computer-Aided Detection, Deep Learning, Chest X-ray, Lung Cancer, Pulmonary Lesion

Abstract

V originále

The rapid advancement of artificial intelligence (AI) in medical imaging has presented an exciting prospect of enhancing diagnostic accuracy and efficiency. One of the active areas of research is the use of deep-learning-based automatic detection algorithms (DLAD) in chest radiography, which has shown tremendous potential in identifying various findings such as tuberculosis or pulmonary lesions. However, despite the promising results in the controlled, high-prevalence simulated conditions typically observed in research settings, there are concerns about the use of these applications in real-world scenarios. For our study, we collected 956 chest X-ray images (CXR) from daily clinical practice at a municipal hospital. Two central readers with access to the patient’s previous and subsequent examinations achieved blinded agreement for 901 CXRs, of which 21 were visually confirmed to contain one or more pulmonary lesions (prevalence: 2.3%) and 880 were found to contain no pulmonary lesions. Six radiologists of varying expertise were asked to conduct a retrospective analysis of these images. Subsequently, the performance of each radiologist was benchmarked against the ground truth and the proposed DLAD (2.0.20-v2.01). The proposed DLAD demonstrated higher sensitivity (Se of 0.905 (0.715–0.978)) than that of all assessed radiologists (RAD 1 0.238 (0.103-0.448), p < 0.001, RAD 2 0.333 (0.170-0.544), p < 0.001, RAD 3 0.524 (0.324-0.717), p < 0.001, RAD 4 0.619 (0.410-0.794), p < 0.001, RAD 5 0.667 (0.456-0.83), p < 0.001, RAD 6 0.619 (0.41-0.794), p < 0.001), and the difference was statistically significant. The DLAD specificity (Sp of 0.893 (0.871-0.912)) was significantly lower than that of five compared radiologists (RAD 1 0.999 (0.994-1), p < 0.001, RAD 2 0.933 (0.915-0.948), p < 0.001, RAD 4 0.968 (0.955-0.978), p < 0.001, RAD 5 0.991 (0.982-0.996), p < 0.001, RAD 6 0.989 (0.979-0.994), p < 0.001), with the exception of one, mid-level experienced radiologist but the difference was not statistically significant (RAD 3 0.884 (0.861-0.904), p = 0.685). The results of this study demonstrate that the proposed DLAD achieves a high level of sensitivity and a relatively reliable level of specificity even when applied in low-prevalence real-world settings. As a result, the proposed DLAD can be considered beneficial for both junior and more experienced radiologists.

Citovat

KVAK, Daniel, Anna CHROMCOVÁ, Petra OVESNÁ, Jakub DANDÁR, Marek BIROŠ, Robert HRUBÝ, Daniel DUFEK and Marija PAJDAKOVIĆ. Detecting Pulmonary Lesions in Low-Prevalence Real-World Settings using Deep Learning. In Ruidan Su, Yudong Zhang, Han Liu, Alejandro F Frangi. Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023). Singapore: Springer, Singapore, 2024, p. 3-20. ISBN 978-981-97-1334-9. Available from: https://dx.doi.org/10.1007/978-981-97-1335-6_1.

@inproceedings{2308040,
   author = {Kvak, Daniel and Chromcová, Anna and Ovesná, Petra and Dandár, Jakub and Biroš, Marek and Hrubý, Robert and Dufek, Daniel and Pajdaković, Marija},
   address = {Singapore},
   booktitle = {Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023)},
   doi = {http://dx.doi.org/10.1007/978-981-97-1335-6_1},
   editor = {Ruidan Su, Yudong Zhang, Han Liu, Alejandro F Frangi},
   keywords = {Artificial Intelligence, Computer-Aided Detection, Deep Learning, Chest X-ray, Lung Cancer, Pulmonary Lesion},
   howpublished = {tištěná verze "print"},
   location = {Singapore},
   isbn = {978-981-97-1334-9},
   pages = {3-20},
   publisher = {Springer, Singapore},
   title = {Detecting Pulmonary Lesions in Low-Prevalence Real-World Settings using Deep Learning},
   year = {2024}
}

TY  - JOUR
ID  - 2308040
AU  - Kvak, Daniel - Chromcová, Anna - Ovesná, Petra - Dandár, Jakub - Biroš, Marek - Hrubý, Robert - Dufek, Daniel - Pajdaković, Marija
PY  - 2024
TI  - Detecting Pulmonary Lesions in Low-Prevalence Real-World Settings using Deep Learning
PB  - Springer, Singapore
CY  - Singapore
SN  - 9789819713349
KW  - Artificial Intelligence, Computer-Aided Detection, Deep Learning, Chest X-ray, Lung Cancer, Pulmonary Lesion
N2  - The rapid advancement of artificial intelligence (AI) in medical imaging has presented an exciting prospect of enhancing diagnostic accuracy and efficiency. One of the active areas of research is the use of deep-learning-based automatic detection algorithms (DLAD) in chest radiography, which has shown tremendous potential in identifying various findings such as tuberculosis or pulmonary lesions. However, despite the promising results in the controlled, high-prevalence simulated conditions typically observed in research settings, there are concerns about the use of these applications in real-world scenarios. For our study, we collected 956 chest X-ray images (CXR) from daily clinical practice at a municipal hospital. Two central readers with access to the patient’s previous and subsequent examinations achieved blinded agreement for 901 CXRs, of which 21 were visually confirmed to contain one or more pulmonary lesions (prevalence: 2.3%) and 880 were found to contain no pulmonary lesions. Six radiologists of varying expertise were asked to conduct a retrospective analysis of these images. Subsequently, the performance of each radiologist was benchmarked against the ground truth and the proposed DLAD (2.0.20-v2.01). The proposed DLAD demonstrated higher sensitivity (Se of 0.905 (0.715–0.978)) than that of all assessed radiologists (RAD 1 0.238 (0.103-0.448), p < 0.001, RAD 2 0.333 (0.170-0.544), p < 0.001, RAD 3 0.524 (0.324-0.717), p < 0.001, RAD 4 0.619 (0.410-0.794), p < 0.001, RAD 5 0.667 (0.456-0.83), p < 0.001, RAD 6 0.619 (0.41-0.794), p < 0.001), and the difference was statistically significant. The DLAD specificity (Sp of 0.893 (0.871-0.912)) was significantly lower than that of five compared radiologists (RAD 1 0.999 (0.994-1), p < 0.001, RAD 2 0.933 (0.915-0.948), p < 0.001, RAD 4 0.968 (0.955-0.978), p < 0.001, RAD 5 0.991 (0.982-0.996), p < 0.001, RAD 6 0.989 (0.979-0.994), p < 0.001), with the exception of one, mid-level experienced radiologist but the difference was not statistically significant (RAD 3 0.884 (0.861-0.904), p = 0.685). The results of this study demonstrate that the proposed DLAD achieves a high level of sensitivity and a relatively reliable level of specificity even when applied in low-prevalence real-world settings. As a result, the proposed DLAD can be considered beneficial for both junior and more experienced radiologists.
ER  -

KVAK, Daniel, Anna CHROMCOVÁ, Petra OVESNÁ, Jakub DANDÁR, Marek BIROŠ, Robert HRUBÝ, Daniel DUFEK and Marija PAJDAKOVI$\backslash$'C. Detecting Pulmonary Lesions in Low-Prevalence Real-World Settings using Deep Learning. In Ruidan Su, Yudong Zhang, Han Liu, Alejandro F Frangi. \textit{Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023)}. Singapore: Springer, Singapore, 2024, p.~3-20. ISBN~978-981-97-1334-9. Available from: https://dx.doi.org/10.1007/978-981-97-1335-6\_{}1.

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