Detection of left ventricular systolic dysfunction using an artificial intelligence–enabled chest X-ray

2022 ◽  
Author(s):  
Chih-Weim Hsiang ◽  
Chin Lin ◽  
Wen-Cheng Liu ◽  
Chin-Sheng Lin ◽  
Wei-Chou Chang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Left Ventricular Systolic Dysfunction ◽  
Systolic Dysfunction ◽  
Left Ventricular ◽  
Ventricular Systolic Dysfunction ◽  
X Ray ◽  
Chest X Ray

scholarly journals Electrocardiography and Chest X-ray in Prediction of Left Ventricular Systolic Dysfunction

KYAMC Journal ◽  
2013 ◽  
Vol 2 (1) ◽  
pp. 118-122
Author(s):  
Md Nure Alom Siddiqui ◽  
Shahnaj Sultana ◽  
Abul Hossain ◽  
Muhammad Afsar Siddiqui
Keyword(s):  
Primary Care ◽  
Left Ventricular Systolic Dysfunction ◽  
Systolic Function ◽  
Systolic Dysfunction ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Ventricular Systolic Dysfunction ◽  
X Ray ◽  
Chest X Ray ◽  
Trained Manpower

Introduction: Echocardiography is the definitive diagnostic tool for left ventricular systolic dysfunction. But it is expensive and requires trained manpower and thus might not be available in the primary care set up. EGG and Chest X ray, the more basic investigations, may help diagnose LVSD or at least streamline those who absolutely require echocardiography in primary care setup. Methods: ECG, Chest X ray and Echocardiography along with clinical assessment were performed on 70 patients with some form of complaints related to heart. The inferences on systolic function obtained from ECG, Chest X ray were compared with Echocardiography findings. Results: Out of 70 participants, 50 had left ventricular ejection fraction less than 45%, 56 had abnormal EGG, 60 had cardiomegaly in chest X-ray. A set of pre-selected ECG abnormalities had a sensitivity of 100% (83.4-100), specificity of 70% (35.4-91.9) and a positive predictive value of 89.3% (70.6-97.2) in diagnosing LVSD. Likewise, the figures were 92% (72.5-98.6), 30% (8.l-64.6) and 76.7% (57.3-89.4) respectively for a cardiothoracic ratio of more than 0.5 in chest X-ray. Conclusions: Although, ECG and Chest X-ray could not replace Echocardiography, they could very well give an idea of the systolic function of an individual and suggest the need or no need for an echo-study in primary care setup.DOI: http://dx.doi.org/10.3329/kyamcj.v2i1.13515 KYAMC Journal Vol.2(1) 2011 pp.118-122

scholarly journals Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients–The SaMi-Trop cohort

2021 ◽  
Vol 15 (12) ◽  
pp. e0009974
Author(s):  
Bruno Oliveira de Figueiredo Brito ◽  
Zachi I. Attia ◽  
Larissa Natany A. Martins ◽  
Pablo Perel ◽  
Maria Carmo P. Nunes ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ejection Fraction ◽  
Chagas Disease ◽  
Left Ventricular Systolic Dysfunction ◽  
Systolic Dysfunction ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Ventricular Systolic Dysfunction ◽  
Ventricular Ejection Fraction ◽  
Male Sex

Background Left ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve symptoms and reduce mortality. Recently, an artificial intelligence (AI)-enabled ECG algorithm showed excellent accuracy to detect LVSD in a general population, but its accuracy in ChD has not been tested. Objective To analyze the ability of AI to recognize LVSD in patients with ChD, defined as a left ventricular ejection fraction determined by the Echocardiogram ≤ 40%. Methodology/principal findings This is a cross-sectional study of ECG obtained from a large cohort of patients with ChD named São Paulo-Minas Gerais Tropical Medicine Research Center (SaMi-Trop) Study. The digital ECGs of the participants were submitted to the analysis of the trained machine to detect LVSD. The diagnostic performance of the AI-enabled ECG to detect LVSD was tested using an echocardiogram as the gold standard to detect LVSD, defined as an ejection fraction <40%. The model was enriched with NT-proBNP plasma levels, male sex, and QRS ≥ 120ms. Among the 1,304 participants of this study, 67% were women, median age of 60; there were 93 (7.1%) individuals with LVSD. Most patients had major ECG abnormalities (59.5%). The AI algorithm identified LVSD among ChD patients with an odds ratio of 63.3 (95% CI 32.3–128.9), a sensitivity of 73%, a specificity of 83%, an overall accuracy of 83%, and a negative predictive value of 97%; the AUC was 0.839. The model adjusted for the male sex and QRS ≥ 120ms improved the AUC to 0.859. The model adjusted for the male sex and elevated NT-proBNP had a higher accuracy of 0.89 and an AUC of 0.874. Conclusion The AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD.

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