If the performance of such systems proves to be accurate and robust in a clinical setting, incorporating them into the screening process can significantly benefit both the hospital and the screening participants. The most common use case, AI-CADe, would reduce radiologist workload and potentially reduce the rate of missed cancers.Some challenges remain despite the potential impact of adopting these technologies in the screening process.
In this paper, we present the multicenter validation of artificial intelligence for breast imaging platform, currently with data from three hospitals being processed by three AI-CADe systems for mammography-based breast cancer detection. We describe the process of extracting the data, obtaining the inference results from the AI-CADe systems, and loading a database with all the information needed to analyze their performance while preserving data privacy and security.
breastcancer AI Effective breast cancer prevention monitors bioaccumulation of PFAS, plasticizers (DEHP, bisphenol A/F/S), insecticides (DDT), fungicides, air pollutants, PAHs, uinary arsenic, serum cortisol, toxic metals, LPS. Protective butyrate, nocturnal melatonin. .