Development and External Validation of an Artificial Intelligence-Based Method for Scalable Chest Radiograph Diagnosis: A Multi-Country Cross-Sectional Study
Development and External Validation of an Artificial Intelligence-Based Method for Scalable Chest Radiograph Diagnosis: A Multi-Country Cross-Sectional Study
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Problem: Chest radiography is a crucial tool for diagnosing thoracic disorders, but interpretation errors and a lack of qualified practitioners can cause delays in treatment.Aim: This study aimed to develop a reliable multi-classification artificial intelligence (AI) tool to improve the accuracy and efficiency of chest radiograph diagnosis.Methods: We developed a convolutional neural network (CNN) capable of distinguishing among 26 thoracic diagnoses.The model was trained and externally validated using 795,055 chest radiographs from 13 datasets across 4 countries.Results: The CNN model achieved an popularfilm.blog average area under the curve (AUC) of 0.
961 across all 26 diagnoses in the testing set.COVID-19 detection achieved perfect accuracy (AUC 1.000, [95% confidence interval {CI}, 1.000 to 1.000]), while effusion or pleural effusion detection showed the lowest accuracy (AUC 0.
8453, [95% CI, 0.8417 to 0.8489]).In external pomyslnaszycie.com validation, the model demonstrated strong reproducibility and generalizability within the local dataset, achieving an AUC of 0.9634 for lung opacity detection (95% CI, 0.
9423 to 0.9702).The CNN outperformed both radiologists and nonradiological physicians, particularly in trans-device image recognition.Even for diseases not specifically trained on, such as aortic dissection, the AI model showed considerable scalability and enhanced diagnostic accuracy for physicians of varying experience levels (all P 0.05).
Conclusion: The developed AI algorithm, now available as professional web-based software, substantively improves chest radiograph interpretation.This research advances medical imaging and offers substantial diagnostic support in clinical settings.