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AI WAVEMAR: Design of an artificial intelligence model for detecting suspicious findings in screening mammography
E N Arenas Rivera1, J M Maiques2, R Alcantara3
1Universidad Pompeu Fabra, Barcelona, Spain; Departamento de Radiología y Medicina Nuclear, DIBI-Hospital del Mar, Barcelona, Spain.
Objective:
To describe the development, training and evaluation of AI WaveMar, an artificial intelligence (AI)-based tool designed for the automated detection of suspicious mammographic findings in breast cancer screening.
Materials And Methods:
An image classification model based on convolutional neural networks has been developed. The model has been trained on a mixed dataset of 48,562 anonymised mammographic projections, obtained from a public hospital in Spain and the external Chinese Mammography Database (CMMD). The model was evaluated on an independent test set composed of 4902 images. Performance metrics calculated included sensitivity (S or recall), specificity (SP), positive predictive value (PPV or precision), negative predictive value (NPV), accuracy and F1-score.
Results:
AI WaveMar achieved a sensitivity of 92.95% (P < .001) and a specificity of 98.12% (P < .001). The PPV was 89.79% (P < .001), and the NPV was 98.74% (P < .001). Accuracy reached 97,34% (P < .001), with an F1-score of 92.34%, indicating high and balanced diagnostic performance.
Conclusion:
AI WaveMar is an AI-based tool developed to support mammographic interpretation, with preliminary results suggesting it could help optimise breast cancer screening. However, prospective clinical validation in real-world practice is required to confirm its diagnostic utility.
