Algorithms from the 2023 RSNA Screening Mammography AI Challenge demonstrated strong performance, with leading models achieving high sensitivity and specificity in breast cancer detection.
Key Details
- 1The 2023 RSNA AI Challenge evaluated 1,537 algorithms on an independent dataset of 5,415 women from the US and Australia.
- 2Median recall rate of all algorithms was 1.7%, with the top-performing algorithm at 1.5%.
- 3Top algorithm sensitivity was 48.6% vs. median of 27.6%; specificity was 99.5% vs. 98.7% median.
- 4Ensemble models of the top 3 and top 10 algorithms achieved sensitivities of 60.7% and 67.8%, with corresponding recall rates of 2.4% and 3.5%.
- 5Sensitivity was higher in the Australian evaluation set (68.1%) than the US set (52.0%).
- 6The top models had higher sensitivity for invasive (68.0%) over noninvasive cancers (43.8%).
Why It Matters
These results highlight the continued advancement and clinical promise of AI tools for breast cancer screening in mammography, especially with ensemble and robust model approaches. The focus on real-world variability, dataset diversity, and robust benchmarking is crucial for safe, scalable adoption in radiology practice.
Source
AuntMinnie
Related News
•Health Imaging
AI as Second Reader Surpasses Radiologists in Breast Cancer Screening
AI used as a second reader on mammograms improves cancer detection rates compared to radiologists alone.
•AuntMinnie
ChatGPT-4 Turbo Powers Postdeployment Monitoring of ICH Detection AI
Researchers found ChatGPT-4 Turbo could efficiently monitor the performance of Aidoc's ICH detection AI across real-world radiology practices.
•Health Imaging
AI-Powered Ultrasound Tool Predicts Delivery Timing for Pregnant Patients
Researchers have created an AI model using ultrasound to accurately forecast expectant mothers’ delivery timelines.