Challenges of using artificial intelligence to detect valvular heart disease from chest radiography – Authors' reply
Authors: Daiju Ueda, Shoichi Ehara, Akira Yamamoto, Shannon L Walston, Taro Shimono, Yukio Miki
Journal: The Lancet Digital Health (2023)

We thank Sepehr Nayebirad and colleagues for commenting on our study.1Ueda D Matsumoto T Ehara S et al.Artificial intelligence-based model to classify cardiac functions from chest radiographs: a multiinstitutional, retrospective model development and validation study.Lancet Digit Health. 2023; 5: e525-e533Summary Full Text Full Text PDF PubMed Scopus (10) Google Scholar We agree that artificial intelligence (AI) models, such as ours, can complement and enhance diagnostic processes, especially in resource-scarce settings. Although our study might seem implausible, harnessing AI to visualise the invisible holds substantial potential in medicine. Indeed, chest radiographs have inherent limitations, and AI predictive models are not yet equivalent to echocardiography. However, we argue that AI can be used to decode the inherent information that exists in radiographs. Regarding the F1 score, this metric can be calculated from the confusion matrix in the Article's appendix. The area under the precision-recall curve is also suitable for performance evaluation with imbalanced datasets and can be found in the Article's appendix.2Faghani S Khosravi B Zhang K et al.Mitigating bias in radiology machine learning: 3. Performance metrics.Radiol Artif Intell. 2022; 4e220061Crossref Scopus (28) Google Scholar Regarding the second comment, our supplementary correlation charts shows several naturally related factors. For instance, there was a correlation between the left ventricular ejection fraction cutoffs, as well as between different severity cutoffs of valvular diseases. We concur that deeper exploration of these relationships could enhance our AI model's performance. For the third and fourth comments, we acknowledge the inherent limitations posed by the dynamic nature of the heart and the static nature of chest radiographs. However, chest radiographs show the pericardium shadow, which has less variation than the heart's contractions.3Gammill SL Krebs C Meyers P Nice Jr, CM Becker HC Cardiac measurements in systole and diastole.Radiology. 1970; 94: 115-120Crossref PubMed Scopus (16) Google Scholar Capturing radiograph images synchronised with electrocardiogram during both the systold and diastole phases could be an avenue for further research and refinement. Addressing the fifth and sixth points, we chose the more common posteroanterior view with inspiration.4Dahnert WF Radiology review manual. Lippincott Williams & Wilkins, Green Bay, WI2017Google Scholar However, preliminary trials did suggest enhanced performance with dual-direction imaging. Capturing bi-directional images or anteroposterior view images to be used by our model might become a feasible option. In conclusion, we are grateful for the depth and clarity of the feedback. Such rigorous peer discussions are what advance the field of medical AI. We look forward to further collaborations and interactions with our scientific community. We declare no competing interests. Artificial intelligence-based model to classify cardiac functions from chest radiographs: a multi-institutional, retrospective model development and validation studyThe deep learning-based model can accurately classify cardiac functions and valvular heart diseases using information from digital chest radiographs. This model can classify values typically obtained from echocardiography in a fraction of the time, with low system requirements and the potential to be continuously available in areas where echocardiography specialists are scarce or absent. Full-Text PDF Open Access