Use of novel artificial intelligence computer-assisted detection (AI-CAD) for screening mammography: an analysis of 17,884 consecutive two-view full-field digital mammography screening exams

Article Radiology, Nuclear Medicine & Medical Imaging

Interval Cancer Detection Using a Neural Network and Breast Density in Women with Negative Screening Mammograms

Alexander J. T. Wanders et al.

Summary: This study evaluated the performance of a neural network-based model that combines assessments of mammographic breast density and an artificial intelligence cancer detection system in the prediction of interval cancer risk among women with negative screening mammography results.

RADIOLOGY (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

An Artificial Intelligence-based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload

Andreas D. Lauritzen et al.

Summary: This study investigates the potential use of an artificial intelligence (AI) system to detect normal, moderate-risk, and suspicious mammograms in breast cancer screening. The AI-based screening shows comparable sensitivity and higher specificity compared to radiologist screening. It effectively reduces the workload of radiologists and avoids false-positive screenings. The AI system performs consistently across different breast densities.

RADIOLOGY (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Stand-Alone Use of Artificial Intelligence for Digit Mammography and Digital Breast Tomosynthesis Screening: A Retrospective Evaluation

Sara Romero-Martin et al.

Summary: This study retrospectively evaluated the performance of an AI system as an independent reader for digital mammography and digital breast tomosynthesis breast screening. The results showed that AI performed well in terms of noninferior sensitivity and could potentially replace radiologists in reading mammography and tomosynthesis images. However, there were differences in the recall rate between AI and human readers.

RADIOLOGY (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Mammographically occult breast cancers detected with AI-based diagnosis supporting software: clinical and histopathologic characteristics

Hee Jeong Kim et al.

Summary: This study retrospectively analyzed mammography using an artificial intelligence (AI) software and found that AI provided added value in the detection of mammographically occult breast cancers. The clinicopathologic characteristics of the AI-detected cancers were also determined.

INSIGHTS INTO IMAGING (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Identifying normal mammograms in a large screening population using artificial intelligence

Kristina Lang et al.

Summary: The study evaluated the potential of artificial intelligence in identifying normal mammograms and reducing false positives in a screening population. The AI system could correctly classify a proportion of exams as cancer-free, indicating potential to improve mammography screening efficiency.

EUROPEAN RADIOLOGY (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women

Xun Zhu et al.

Summary: The deep learning model showed superior performance in predicting the risk of screening-detected breast cancer but performed less effectively for interval-cancer risk compared to clinical risk factors including breast density.

RADIOLOGY (2021)

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Article Multidisciplinary Sciences