Can Deep Learning Reliably Recognize Abnormality Patterns on
Chest X-rays? A Multi-Reader Study Examining One Month of AI
Implementation in Everyday Radiology Clinical Practice

j 2023

Can Deep Learning Reliably Recognize Abnormality Patterns on Chest X-rays? A Multi-Reader Study Examining One Month of AI Implementation in Everyday Radiology Clinical Practice

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

Základní údaje

Originální název

Can Deep Learning Reliably Recognize Abnormality Patterns on Chest X-rays? A Multi-Reader Study Examining One Month of AI Implementation in Everyday Radiology Clinical Practice

Autoři

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

Vydání

arXiv preprint, 2023

Další údaje

Typ výsledku

Publikace v odborném periodiku – kromě recenzovaných typů article, review a letter

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Označené pro přenos do RIV

Klíčová slova anglicky

Artificial Intelligence; Computer-Aided Detection; Deep Learning; Chest X-ray; Radiology.

Příznaky

Mezinárodní význam

Změněno: 19. 5. 2023 15:49, Mgr. Daniel Kvak

Anotace

V originále

In this study, we developed a deep-learning-based automatic detection algorithm (DLAD, Carebot AI CXR) to detect and localize seven specific radiological findings (atelectasis (ATE), consolidation (CON), pleural effusion (EFF), pulmonary lesion (LES), subcutaneous emphysema (SCE), cardiomegaly (CMG), pneumothorax (PNO)) on chest X-rays (CXR). We collected 956 CXRs and compared the performance of the DLAD with that of six individual radiologists who assessed the images in a hospital setting. The proposed DLAD achieved high sensitivity (ATE 1.000 (0.624-1.000), CON 0.864 (0.671-0.956), EFF 0.953 (0.887-0.983), LES 0.905 (0.715-0.978), SCE 1.000 (0.366-1.000), CMG 0.837 (0.711-0.917), PNO 0.875 (0.538-0.986)), even when compared to the radiologists (LOWEST: ATE 0.000 (0.000-0.376), CON 0.182 (0.070-0.382), EFF 0.400 (0.302-0.506), LES 0.238 (0.103-0.448), SCE 0.000 (0.000-0.634), CMG 0.347 (0.228-0.486), PNO 0.375 (0.134-0.691), HIGHEST: ATE 1.000 (0.624-1.000), CON 0.864 (0.671-0.956), EFF 0.953 (0.887-0.983), LES 0.667 (0.456-0.830), SCE 1.000 (0.366-1.000), CMG 0.980 (0.896-0.999), PNO 0.875 (0.538-0.986)). The findings of the study demonstrate that the suggested DLAD holds potential for integration into everyday clinical practice as a decision support system, effectively mitigating the false negative rate associated with junior and intermediate radiologists.

Citovat

KVAK, Daniel; Anna CHROMCOVÁ; Petra OVESNÁ; Jakub DANDÁR; Marek BIROŠ; Robert HRUBÝ; Daniel DUFEK a Marija PAJDAKOVIĆ. Can Deep Learning Reliably Recognize Abnormality Patterns on Chest X-rays? A Multi-Reader Study Examining One Month of AI Implementation in Everyday Radiology Clinical Practice. arXiv preprint. 2023, 2305.10116, 26 s.

@article{2283999,
   author = {Kvak, Daniel and Chromcová, Anna and Ovesná, Petra and Dandár, Jakub and Biroš, Marek and Hrubý, Robert and Dufek, Daniel and Pajdaković, Marija},
   article_number = {2305.10116},
   keywords = {Artificial Intelligence; Computer-Aided Detection; Deep Learning; Chest X-ray; Radiology.},
   journal = {arXiv preprint},
   title = {Can Deep Learning Reliably Recognize Abnormality Patterns on Chest X-rays? A Multi-Reader Study Examining One Month of AI Implementation in Everyday Radiology Clinical Practice},
   url = {https://arxiv.org/abs/2305.10116},
   year = {2023}
}

TY  - JFULL
ID  - 2283999
AU  - Kvak, Daniel - Chromcová, Anna - Ovesná, Petra - Dandár, Jakub - Biroš, Marek - Hrubý, Robert - Dufek, Daniel - Pajdaković, Marija
PY  - 2023
TI  - Can Deep Learning Reliably Recognize Abnormality Patterns on Chest X-rays? A Multi-Reader Study Examining One Month of AI Implementation in Everyday Radiology Clinical Practice
JF  - arXiv preprint
IS  - 2305.10116
KW  - Artificial Intelligence
KW  - Computer-Aided Detection
KW  - Deep Learning
KW  - Chest X-ray
KW  - Radiology.
UR  - https://arxiv.org/abs/2305.10116
N2  - In this study, we developed a deep-learning-based automatic detection algorithm (DLAD, Carebot AI CXR) to detect and localize seven specific radiological findings (atelectasis (ATE), consolidation (CON), pleural effusion (EFF), pulmonary lesion (LES), subcutaneous emphysema (SCE), cardiomegaly (CMG), pneumothorax (PNO)) on chest X-rays (CXR). We collected 956 CXRs and compared the performance of the DLAD with that of six individual radiologists who assessed the images in a hospital setting. The proposed DLAD achieved high sensitivity (ATE 1.000 (0.624-1.000), CON 0.864 (0.671-0.956), EFF 0.953 (0.887-0.983), LES 0.905 (0.715-0.978), SCE 1.000 (0.366-1.000), CMG 0.837 (0.711-0.917), PNO 0.875 (0.538-0.986)), even when compared to the radiologists (LOWEST: ATE 0.000 (0.000-0.376), CON 0.182 (0.070-0.382), EFF 0.400 (0.302-0.506), LES 0.238 (0.103-0.448), SCE 0.000 (0.000-0.634), CMG 0.347 (0.228-0.486), PNO 0.375 (0.134-0.691), HIGHEST: ATE 1.000 (0.624-1.000), CON 0.864 (0.671-0.956), EFF 0.953 (0.887-0.983), LES 0.667 (0.456-0.830), SCE 1.000 (0.366-1.000), CMG 0.980 (0.896-0.999), PNO 0.875 (0.538-0.986)). The findings of the study demonstrate that the suggested DLAD holds potential for integration into everyday clinical practice as a decision support system, effectively mitigating the false negative rate associated with junior and intermediate radiologists.
ER  -

KVAK, Daniel; Anna CHROMCOVÁ; Petra OVESNÁ; Jakub DANDÁR; Marek BIROŠ; Robert HRUBÝ; Daniel DUFEK a Marija PAJDAKOVI$\backslash$'C. Can Deep Learning Reliably Recognize Abnormality Patterns on Chest X-rays? A Multi-Reader Study Examining One Month of AI Implementation in Everyday Radiology Clinical Practice. \textit{arXiv preprint}. 2023, 2305.10116, 26 s.

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