Akademik Veri Yönetim Sistemi
Revista de Senologia y Patologia Mamaria, cilt.37, sa.1, 2024 (Scopus)
Objectives: It was aimed to use AI retrospectively to evaluate US images of pathologically confirmed benign inflammatory lesions, to compare the results of AI with our US reports, and to test the reliability of AI in itself. Methods: US images of 71 histopathologically confirmed benign inflammatory breast lesions were analysed by the FDA-approved AI programme (Koios Decision Support) using 2 orthogonal projections. The lesions' probability of malignancy based on AI and BI-RADS categories of the lesion based on initial US interpretations were recorded. Categories obtained by both systems were divided into 2 groups as unsuspicious and suspicious in terms of malignancy and compared statistically. Reliability of AI was also evaluated. Results: No statistically significant difference was found in the lesions' likelihood of malignancy based on the AI and initial US interpretations (P = .512). Additionally, a positive and substantial association (τ-b = 0.458, P < .001) between the levels of suspicion by AI and the initial US interpretation reports was discovered, as per Kendall-b correlation analysis. With a Cronbach alpha correlation coefficient of 0.727, the reliability was high for AI. Conclusions: Benign inflammatory breast lesions may show suspicious appearances in terms of malignancy with US and AI. Artificial intelligence produces results comparable to radiologists' US reports for benign inflammatory diseases. AI has high reliability within itself.