SBIR Phase I: A Real-time Artificial Intelligence Dysplasia Detection (AIDD) system for Barrett's Esophagus and Early Esophageal Cancer

Docbot, Inc., Irvine CA

Investigators

Abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to, through development of a Real-time Artificial Intelligence algorithm, bring expertise in endoscopic imaging interpretation to physicians taking care of patients at risk for esophageal cancer, to detect precancer earlier, and lower the cost of healthcare. Esophageal cancer is one of the fastest growing cancers in the US with a 6 fold increase in incidence in the last 4 decades. Most endoscopists are not trained well to detect pre-cancer in the esophagus as <5% have received dedicated advanced imaging training to identify dysplasia (advanced precancer) within Barrett's Esophagus. There are well-proven minimally invasive endoscopic treatments for dysplasia and early esophageal cancer that are >95% effective in providing cure to the patient. On the other hand, when cancer is caught at a later stage the only option is removal of the esophagus with surgery in combination with expensive chemotherapy and radiation. Creating this real-time Artificial Intelligence system will assist endoscopists to detect precancer earlier, prevent this deadly cancer and expand access to esophageal cancer screening to the community at large and also to the underserved community, who typically have fewer well-trained physicians. This Small Business Innovation Research (SBIR) Phase I project will develop the first system that can detect dysplasia in Barrett's Esophagus in real-time during endoscopy. This proposed research will expand on the company's preliminary work in detecting dysplastic lesions in colonoscopy and applying transfer learning methods to develop the first ever dysplasia-detection algorithm for upper endoscopy. It will initially be trained using static, annotated images, and will expand on the company's software platform to be able to eventually process video feeds, in real-time, from device makers without frame-drop or lag. The company has begun engaging with endoscopy OEMs to define metrics for success in running real-time algorithms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.