Development of COVID-19 and Cancer Tools with Artificial Intelligence

Division Of Basic Sciences - Nci

Investigators

Linked publications, trials & patents

Abstract

Public data posting of CT scans on public NCI TCIA websites were made in past years. AI deep learning models were made alongside of multiple industry partners, to educate on the serial temporal dynamics of diseases. AI deep learning models were built and shared for research purposes to automatically segment tumors, thermal ablation treatment zones, liver organ, or COVID-19 opacities, as well as try to classify COVID-19. NIH CC and NCI were among the first to gather multi-national data and develop freeware public AI solutions based on COVID CTs for both academic and commercial developer use in early years of the pandemic. Federated learning was piloted with academic and industry partners in several projects in Nature Medicine and JAMIA publications. The NIH team is working with commercial and academic partners to assess deep learning tools in cancer. Ongoing work will attempt to assess voice input towards AI uses in healthcare. It was shown that pre-symptomatic CT AI can track disease in a predictable fashion. Prior work with extramural partners has demonstrated that federated learning can overcome shortcomings in unbalanced source data for imaging AI, and that the application of a specific federated learning technique can overcome the gap, thus showing that the data does not need to be shared in order to build quality AI models from medical imaging. This effort cross links with numerous campus efforts, including NCI/CCR efforts within AI Resource, and extramural partners in Bridge to AI. Highly impactful AI models were developed and licensed towards the detection, characterization, and assessment of prostate cancer using MRI and MRI-US fusion biopsy. These models may have broad scope impact. Initial feasibility was begun for data curation of voice and wearable sensor data towards health care AI models. A large language model pathway for use of procedural imaging data was outlined and connections with MDRIC were initiated towards an AI toolkit for procedural medicine applications. A checklist for publications in AI was created for multiple simultaneous journal publication for the specialty of Interventional Radiology. IR-GPT was proposed and begun. AI tools for streamlining clinical trials were proposed and designed.