Gut Feeling: Endoscopy Startup Uses AI to Spot Stomach, Colon Cancer

by Isha Salian

Even the most experienced doctors can’t catch every tiny polyp during an endoscopy, a screening of the digestive system.

But even in routine exams, the stakes are high — missing an early warning sign of cancer can lead to delayed diagnosis and treatment, lowering a patient’s chances for recovery.

To cut down on the rate of missed precancerous lesions, one Japanese endoscopist is turning to AI. His startup, AIM (short for AI Medical Service), is building a GPU-powered AI system that will analyze endoscopy video feeds in real time, spotting lesions and helping doctors identify which are cancerous or at risk of becoming so.

AI screening could also help clinicians manage a demanding workload: Japanese endoscopists must check more than 3,000 medical images a day, on average. Stomach and colon cancer are two of the three leading causes of cancer-related deaths in the country.

“Coming from 23 years of experience as an actual endoscopist, I saw firsthand the challenges facing experts in the field,” said Tomohiro Tada, CEO of AIM. “GPU-powered AI can help manage the overwhelming demand for checking endoscopic images, while improving the overall accuracy of lesion detection.”

A quarter of precancerous lesions are overlooked in endoscopy screenings, according to one Japanese study. In preclinical research trials, AIM’s AI model achieved 92% sensitivity in detecting stomach cancer lesions from endoscopy videos. The startup’s deep learning tool could help endoscopists better distinguish hard-to-spot lesions and improve consistency across different clinics.

AI Powers a Better Gut Check 

During an upper gastrointestinal endoscopy, a doctor examines a patient’s esophagus, stomach and upper region of the small intestine using a long tube with a small camera attached to it. The video feed from this camera is displayed on a larger screen for the clinician, who looks for bleeding, cancer or other conditions.

While doctors examine the endoscopy video footage live to check for polyps, they also check still images after the procedure. Having an AI to assist in real-time detection during a procedure could help doctors save time spent on secondary screening, Tada said.

AIM plans to deploy its AI model, which can identify different kinds of stomach lesions, in an NVIDIA Quadro RTX 4000 GPU-powered device that connects to existing endoscope systems. The device would receive the live endoscopy video feed and simultaneously process the footage to assist doctors during the procedure.

The startup uses a variety of NVIDIA GPUs, including the TITAN Xp and Quadro P6000, to train its deep learning models. It’s using an NVIDIA Quadro mobile workstation for inference in the prototype of its real-time AI device.

AIM’s deep-learning based object detection and classification algorithms are developed using tens of thousands of annotated endoscopy images from Tada’s clinic and from research partners including Japan’s Cancer Institute Hospital and the University of Tokyo Hospital.