“The National Institutes of Health (NIH) is turning to the private sector to help it collect, store, and disseminate medical images and data related to cancer screening.”
“The data management solution is needed to manage data stemming from the NIH’s National Lung Screening Trial (NLST). In a Feb. 12 sources sought notice on Beta.Sam.gov, NIH explained that the NLST has demonstrated a substantial reduction in lung cancer mortality in subjects screened with low-dose computerized tomography (LDCT) as compared to chest radiographs.”
“However, there was also a very high false-positive rate (FPR) with the LDCTscreens. In addition to the high FPR, NIH explained that there is a need for improvement in predicting risk among those with positive LDCT screens. The impact of a high FPR and the limited ability to predict risk levels is detrimental to both patient care and wellbeing, as well as putting a strain on the health system.”
“NIH is turning to AI, which it says is ‘poised to transform medical imaging’ to help solve these issues. NIH believes that AI can substantially reduce the FPR of LDCT screening while minimally affecting test sensitivity, thereby reducing diagnostic uncertainty. For the AI to be useful, NIH says it needs to create high-quality image databases.”
“The image database must:
- Be sufficiently large, with an adequate number of images for training/deep learning data set and an independent validation data set;
- Be accessible to the research communities without burdensome technical and administrative hurdles but with adequate controls for data security;
- Have accurate and sufficient clinical and demographic data linked to the images;
- Have images produced on current, state-of-the-art imaging technology; and
- Have images performed on populations that are representative of the intended-use population of the AI tools…”
Source: NIH Needs Data Storage Solution to Manage Cancer Screening Data – By Kate Polit, February 16, 2021. MeriTalk.
