Evaluating the Performance of Deep Learning Model and Junior Radiologists in Reading Major Pathologies on Chest X-Rays: A Population-Based, Multi-reader Study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00209805%3A_____%2F25%3A00080516" target="_blank" >RIV/00209805:_____/25:00080516 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-981-96-3863-5_33" target="_blank" >http://dx.doi.org/10.1007/978-981-96-3863-5_33</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-96-3863-5_33" target="_blank" >10.1007/978-981-96-3863-5_33</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluating the Performance of Deep Learning Model and Junior Radiologists in Reading Major Pathologies on Chest X-Rays: A Population-Based, Multi-reader Study

Popis výsledku v původním jazyce

Chest X-ray (CXR) is a fundamental diagnostic tool in detecting thoracic pathologies. However, the interpretation accuracy can vary significantly among radiologists, particularly those less experienced. Deep-learning-based automatic detection algorithms (DLAD) have emerged as a promising solution to augment diagnostic precision. This population-based, multi-reader study evaluates the performance of a DLAD (Carebot AI CXR) in detecting four major thoracic pathologies-Atelectasis (ATE), Consolidation (CON), Pulmonary Lesion (LES), and Pleural Effusion (EFF)-compared to the diagnostic accuracy of six junior radiologists in a real-world clinical setting. We retrospectively analyzed CXR images (n = 999) from a mid-sized hospital, reflecting real-world prevalence of the studied findings. The DLAD&apos;s performance was evaluated using sensitivity (Se), specificity (Sp), and likelihood ratios (PLR and NLR), and compared with radiologists&apos; assessments. A paired design was employed to compare Se and Sp with confidence intervals (CI) and p-values. The proposed DLAD demonstrated superior Se across all pathologies, with values of 0.938 (CI: 0.832-0.979) for ATE (n = 48), 0.946 (0.852-0.981) for CON (n = 55), 0.940 (0.887-0.969) for EFF (n = 134), and 0.818 (0.680-0.905) for LES (n = 44), where DLAD achieved lower Se than two assessed radiologists in multi-reader study (RAD 3 &amp; RAD 5), but the differences were not statistically significant. However, it achieved lower Sp compared to junior radiologists in all findings, showing values of 0.914 (0.894-0.931), 0.803 (0.775-0.829), 0.875 (0.852-0.895), and 0.879 (0.854-0.900), respectively. The results highlight the potential of integrating DLAD into clinical practice as a decision-support tool for less experienced radiologists. The proposed DLAD has the ability to increase the sensitivity of radiologists&apos; reading.

Název v anglickém jazyce

Evaluating the Performance of Deep Learning Model and Junior Radiologists in Reading Major Pathologies on Chest X-Rays: A Population-Based, Multi-reader Study

Popis výsledku anglicky

Chest X-ray (CXR) is a fundamental diagnostic tool in detecting thoracic pathologies. However, the interpretation accuracy can vary significantly among radiologists, particularly those less experienced. Deep-learning-based automatic detection algorithms (DLAD) have emerged as a promising solution to augment diagnostic precision. This population-based, multi-reader study evaluates the performance of a DLAD (Carebot AI CXR) in detecting four major thoracic pathologies-Atelectasis (ATE), Consolidation (CON), Pulmonary Lesion (LES), and Pleural Effusion (EFF)-compared to the diagnostic accuracy of six junior radiologists in a real-world clinical setting. We retrospectively analyzed CXR images (n = 999) from a mid-sized hospital, reflecting real-world prevalence of the studied findings. The DLAD&apos;s performance was evaluated using sensitivity (Se), specificity (Sp), and likelihood ratios (PLR and NLR), and compared with radiologists&apos; assessments. A paired design was employed to compare Se and Sp with confidence intervals (CI) and p-values. The proposed DLAD demonstrated superior Se across all pathologies, with values of 0.938 (CI: 0.832-0.979) for ATE (n = 48), 0.946 (0.852-0.981) for CON (n = 55), 0.940 (0.887-0.969) for EFF (n = 134), and 0.818 (0.680-0.905) for LES (n = 44), where DLAD achieved lower Se than two assessed radiologists in multi-reader study (RAD 3 &amp; RAD 5), but the differences were not statistically significant. However, it achieved lower Sp compared to junior radiologists in all findings, showing values of 0.914 (0.894-0.931), 0.803 (0.775-0.829), 0.875 (0.852-0.895), and 0.879 (0.854-0.900), respectively. The results highlight the potential of integrating DLAD into clinical practice as a decision-support tool for less experienced radiologists. The proposed DLAD has the ability to increase the sensitivity of radiologists&apos; reading.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

30224 - Radiology, nuclear medicine and medical imaging

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of 2024 International Conference on Medical Imaginag and Compiter-aided Diagnosis

ISBN

978-981-96-3863-5

ISSN

1876-1100

e-ISSN

1876-1119

Počet stran výsledku

15

Strana od-do

357-372

Název nakladatele

SPRINGER-VERLAG

Místo vydání

Singapore

Místo konání akce

Manchester, United Kingdom

Datum konání akce

19. 11. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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