Automatic prediction of left cardiac chamber enlargement from chest radiographs using convolutional neural network

Nam, Ju Gang; Kim, Jin Wook; Noh, Keonwoo; Choi, Hyewon; Kim, Dasom; Yoo, Seung Jin; Yang, Hyun-Lim; Hwang, Eui Jin; Goo, Jin Mo; Park, Eun Ah; Sun, Hye Young; Kim, Min Soo; Park, Chang Min

doi:10.1007/s00330-021-07963-1

Cited by 6 publications

(4 citation statements)

References 34 publications

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“…In one method, CTR is the ratio of the maximal horizontal cardiac diameter to the maximal horizontal thoracic diameter (inner edge of ribs/edge of pleura), and CTR > 0.50 is usually considered cardiomegaly. Several previous studies 27,28 have been conducted on the automatic calculation of the CTR by referring to the above method; However, in professional reference book 19 , CTR is defined as the ratio of the cardiac diameter (the horizontal distance between the most rightward and most leftward margins of the cardiac shadow) to the thoracic diameter (the distance from the inner margin of the ribs at the level of the dome of the right hemidiaphragm), and CTR > 0.55 is usually considered to be cardiomegaly. The automatic calculation of CTR according to the former method only requires the segmentation of both lungs and the heart.…”

Section: Discussionmentioning

confidence: 99%

A deep-learning-based framework for identifying and localizing multiple abnormalities and assessing cardiomegaly in chest X-ray

Fan,

Yang,

et al. 2024

Nat Commun

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Accurate identification and localization of multiple abnormalities are crucial steps in the interpretation of chest X-rays (CXRs); however, the lack of a large CXR dataset with bounding boxes severely constrains accurate localization research based on deep learning. We created a large CXR dataset named CXR-AL14, containing 165,988 CXRs and 253,844 bounding boxes. On the basis of this dataset, a deep-learning-based framework was developed to identify and localize 14 common abnormalities and calculate the cardiothoracic ratio (CTR) simultaneously. The mean average precision values obtained by the model for 14 abnormalities reached 0.572-0.631 with an intersection-over-union threshold of 0.5, and the intraclass correlation coefficient of the CTR algorithm exceeded 0.95 on the held-out, multicentre and prospective test datasets. This framework shows an excellent performance, good generalization ability and strong clinical applicability, which is superior to senior radiologists and suitable for routine clinical settings.

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Section: Discussionmentioning

confidence: 99%

A deep-learning-based framework for identifying and localizing multiple abnormalities and assessing cardiomegaly in chest X-ray

Fan,

Yang,

et al. 2024

Nat Commun

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“…Recently, some attempts to utilize artificial intelligence methods have been conducted. Nam et al [ 35 ] developed a way to detect left atrial enlargement (LAE) and LVE on chest X-rays using deep learning algorithms, but the results for LVE detection were all p > 0.05 compared to those for LAE, suggesting a need for inclusion of more images. The current ML-based methods demonstrate a patient-friendly and feasible tool for the first time to effectively differentiate LVE patients from the LVE and non-LVE groups using pulse wave signals.…”

Section: Discussionmentioning

confidence: 99%

Pulse wave signal-driven machine learning for identifying left ventricular enlargement in heart failure patients

Wu,

Ono,

Wang

et al. 2024

BioMed Eng OnLine

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Background Left ventricular enlargement (LVE) is a common manifestation of cardiac remodeling that is closely associated with cardiac dysfunction, heart failure (HF), and arrhythmias. This study aimed to propose a machine learning (ML)-based strategy to identify LVE in HF patients by means of pulse wave signals. Method We constructed two high-quality pulse wave datasets comprising a non-LVE group and an LVE group based on the 264 HF patients. Fourier series calculations were employed to determine if significant frequency differences existed between the two datasets, thereby ensuring their validity. Then, the ML-based identification was undertaken by means of classification and regression models: a weighted random forest model was employed for binary classification of the datasets, and a densely connected convolutional network was utilized to directly estimate the left ventricular diastolic diameter index (LVDdI) through regression. Finally, the accuracy of the two models was validated by comparing their results with clinical measurements, using accuracy and the area under the receiver operating characteristic curve (AUC-ROC) to assess their capability for identifying LVE patients. Results The classification model exhibited superior performance with an accuracy of 0.91 and an AUC-ROC of 0.93. The regression model achieved an accuracy of 0.88 and an AUC-ROC of 0.89, indicating that both models can quickly and accurately identify LVE in HF patients. Conclusion The proposed ML methods are verified to achieve effective classification and regression with good performance for identifying LVE in HF patients based on pulse wave signals. This study thus demonstrates the feasibility and potential of the ML-based strategy for clinical practice while offering an effective and robust tool for diagnosing and intervening ventricular remodeling.

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“…There are many applications of deep learning within the medical field, such as lung nodule classification [9], skin lesion classification [10], prostate cancer detection [11], glaucoma detection [12], COVID-19 detection [13], and Alzheimer's disease recognition [14], among others. Previous research has also estimated the cardiac state using deep learning, as in the following cases: left cardiac chamber enlargement detection [15], cardiomegaly detection [16], and heart failure detection [17,18]. Moreover, there are extensive reviews on the techniques and results of inputting chest X-ray images into a CNN [19,20].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, there are extensive reviews on the techniques and results of inputting chest X-ray images into a CNN [19,20]. Some of these have the function outputting activated regions as reason of estimation [12][13][14][15]17].…”

Section: Introductionmentioning

confidence: 99%

Reliability Metrics of Explainable CNN based on Wasserstein Distance for Cardiac Evaluation

Omae

Kakimoto

Saito

et al. 2022

Preprint

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In recent works, convolutional neural networks (CNN) have been used in the non-invasive examination of the cardiac region for estimating pulmonary artery wedge pressure (PAWP) from chest radiographs.Moreover, because CNNs are able to output activated regions, physicians can estimate PAWP along with reasons. However, when new patient radiograph data are fed into the CNN, there is a possibility that activated regions that contain areas other than the cardiac appear.In this case, although we expect a large estimation error, it is not well known.Therefore, we verify this hypothesis by distance theory and statistic approaches.In particular, we build the probability distributions for the cardiac region and the regression activation map (RAM) and measure the similarity between these distributions by Wasserstein distance (WSD).When the CNN estimates PAWP from areas other than the cardiac region, the WSD value is high.Therefore, WSD is a reliability metrics for explainable CNN.We created two groups, normal and anomaly classes, based on WSD values.Chest radiographs which had a high WSD were assigned to the anomaly class, and those with a low WSD were assigned to the normal class. By comparing the normal and anomaly classes based on the PAWP estimation error, we confirmed that the errors from the anomaly class were higher than those of the normal class.Therefore, physicians need to be aware that there might be large estimation errors when activated regions contain areas other than the cardiac.

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Automatic prediction of left cardiac chamber enlargement from chest radiographs using convolutional neural network

Cited by 6 publications

References 34 publications

A deep-learning-based framework for identifying and localizing multiple abnormalities and assessing cardiomegaly in chest X-ray

A deep-learning-based framework for identifying and localizing multiple abnormalities and assessing cardiomegaly in chest X-ray

Pulse wave signal-driven machine learning for identifying left ventricular enlargement in heart failure patients

Reliability Metrics of Explainable CNN based on Wasserstein Distance for Cardiac Evaluation

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