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A deep learning-based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease:
Cherry Kim1, Gaeun Lee2, Hongmin Oh3
1Department of Radiology, Korea University Ansan Hospital, Ansan, Korea.
Insights
A new deep learning algorithm (CB_auto) accurately analyzes cardiovascular borders on chest X-rays for valvular heart disease. This automated method shows high reliability comparable to manual analysis, aiding clinical diagnosis and research.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiology
Background:
- Cardiovascular border (CB) analysis on chest radiographs (CXRs) is crucial for assessing cardiovascular disease severity.
- Existing methods often rely on manual interpretation, which can be time-consuming and subjective.
Purpose of the Study:
- To develop and validate a deep learning-based automatic algorithm (CB_auto) for analyzing cardiovascular borders on CXRs.
- To assess the algorithm's utility in diagnosing and quantitatively evaluating valvular heart disease (VHD).
Main Methods:
- The CB_auto algorithm was developed using a large dataset of normal and VHD CXRs.
- Validation was performed using independent datasets from multiple hospitals and a public dataset.
- Reliability was assessed by comparing CB parameters from CB_auto with manual measurements (CB_hand) and echocardiography.
Main Results:
- CB_auto demonstrated excellent reliability (intraclass correlation coefficient > 0.98) and high accuracy, with measurement errors below 10% for most parameters compared to manual drawing.
- The algorithm successfully processed 93.9% of CXRs in an external public dataset.
- Significant differences in CB parameters were observed between VHD patients and normal controls, correlating well with echocardiographic findings.
Conclusions:
- The deep learning-based CB_auto system provides reliable and accurate cardiovascular border measurements from CXRs.
- CB_auto shows potential for routine clinical use in diagnosing and monitoring VHD.
- The algorithm can also serve as a valuable tool for cardiovascular research.
Objectives:
Cardiovascular border (CB) analysis is the primary method for detecting and quantifying the severity of cardiovascular disease using posterior-anterior chest radiographs (CXRs). This study aimed to develop and validate a deep learning-based automatic CXR CB analysis algorithm (CB_auto) for diagnosing and quantitatively evaluating valvular heart disease (VHD).
Methods:
We developed CB_auto using 816 normal and 798 VHD CXRs. For validation, 640 normal and 542 VHD CXRs from three different hospitals and 132 CXRs from a public dataset were assigned. The reliability of the CB parameters determined by CB_auto was evaluated. To evaluate the differences between parameters determined by CB_auto and manual CB drawing (CB_hand), the absolute percentage measurement error (APE) was calculated. Pearson correlation coefficients were calculated between CB_hand and echocardiographic measurements.
Results:
CB parameters determined by CB_auto yielded excellent reliability (intraclass correlation coefficient > 0.98). The 95% limits of agreement for the cardiothoracic ratio were 0.00 ± 0.04% without systemic bias. The differences between parameters determined by CB_auto and CB_hand as defined by the APE were < 10% for all parameters except for carinal angle and left atrial appendage. In the public dataset, all CB parameters were successfully drawn in 124 of 132 CXRs (93.9%). All CB parameters were significantly greater in VHD than in normal controls (all p < 0.05). All CB parameters showed significant correlations (p < 0.05) with echocardiographic measurements.
Conclusions:
The CB_auto system empowered by deep learning algorithm provided highly reliable CB measurements that could be useful not only in daily clinical practice but also for research purposes.
Key Points:
• A deep learning-based automatic CB analysis algorithm for diagnosing and quantitatively evaluating VHD using posterior-anterior chest radiographs was developed and validated. • Our algorithm (CB_auto) yielded comparable reliability to manual CB drawing (CB_hand) in terms of various CB measurement variables, as confirmed by external validation with datasets from three different hospitals and a public dataset. • All CB parameters were significantly different between VHD and normal control measurements, and echocardiographic measurements were significantly correlated with CB parameters measured from normal control and VHD CXRs.
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