Project Grant R43GM134789

This $255,807 National Science Foundation project grant will fund the development of an AI-assisted software system to accelerate the labeling of medical tomographic images. Administered through the NSF Directorate for Engineering's Engineering program (CFDA 47.041), the grant aims to extract new information from medical images and improve patient outcomes. Alienbyte Scientific Software Inc. will apply machine learning algorithms to create an adaptive system that evolves to increase the speed,...

This Project Grant award of $306,873 from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) under the Diabetes, Digestive, and Kidney Diseases Extramural Research program (CFDA 93.847) aims to develop an AI-driven automated system for accurately generating Banff lesion scores from kidney biopsy slides. The project, titled "BANFF-AID: BANFF Automated Nephrology Feature Framework - Artificial Intelligence Diagnosis", will create custom AI models to extract...

The National Institute of General Medical Sciences (NIGMS) awarded a $275,756 Project Grant under the Biomedical Research and Research Training program (CFDA 93.859) to Rewire Neuroscience LLC, a woman-owned small business in Oregon. The funding supports the development of PipSqueak Pro, an AI-powered computer vision platform that aims to democratize access to advanced machine learning techniques for automated biomedical image analysis. The platform seeks to reduce human bias, subjectivity,...

This federal Project Grant award of $194,022, awarded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286), supports research to develop deep learning models that integrate radiology, histopathology, and clinico-genomic data to predict response to immune checkpoint inhibitor (ICI) therapy for brain metastases. The awardee, The General Hospital Corporation...

This Project Grant award from the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) to Michigan State University (MSU) provides USD 113,018 to develop robust machine learning methods for imaging applications. The award aims to advance supervised and unsupervised learning techniques that can effectively reconstruct and correct images from limited or corrupted measurement data, with applications in medical imaging, industrial and security systems,...

This Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), under the CFDA 93.286 "Discovery and Applied Research for Technological Innovations to Improve Human Health" program, provides $641,063 to develop a dynamic, customizable, vendor-neutral patient projection data library and virtual imaging trial (VIT) software platform (DICOM-CTPD-VIT). This platform will enable the generation of a diverse range of imaging conditions, including...

This federal Project Grant award, valued at $594,891 and provided by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under CFDA 93.286 "Discovery and Applied Research for Technological Innovations to Improve Human Health", aims to develop deep-learning anthropomorphic model observers (AMOs) as a substitute for human observers in studies assessing image quality for clinical diagnostic performance. The key objectives are to create AMOs that can accurately...

This National Cancer Institute (CFDA 93.394 Cancer Detection and Diagnosis Research) Project Grant award of $683,089 to the Sloan-Kettering Institute for Cancer Research supports the development of an improved, interpretable deep learning algorithm called DeepLIIF for more reproducible and accurate PD-L1 immunohistochemistry (IHC) biomarker quantification. The goal is to leverage virtual multiplex immunofluorescence (MPIF) restaining and large, diverse datasets across lung and bladder cancers to...

This $435,827 Project Grant from the National Cancer Institute (NCI) under the CFDA 93.394 Cancer Detection and Diagnosis Research program supports research conducted by New York University (NYU) School of Medicine to develop deep learning methods for analyzing mass spectrometry imaging (MSI) data. The goal is to make MSI data more accessible to existing machine learning workflows by expanding the dimensionality of the data structure to treat each metabolite or lipid as an individual "color...

This Project Grant award from the National Heart, Lung, and Blood Institute (NHLBI), under the Cardiovascular Diseases Research program (CFDA 93.837), provides $742,395 to Case Western Reserve University (CWRU) to develop and validate a machine learning-based analysis of coronary artery calcium scans (CTCS) to identify biomarkers for predicting heart failure risk. The 4-year project aims to create an automated tool for extracting CTCS-derived radiomics, develop a comprehensive heart failure risk...