AI Case Study

Szechwan People’s Hospital uses machine learning to detect early signs of lung cancer in CT scans broadening access to quality diagnosis given the scarcity of radiologists in China

Lung cancer clams 600,000 lives annually in China and there is a shortage of radiologists. Szechwan People’s Hospital worked with Infervision to train a deep learning networks model to recognise lung cancer signatures on 100,000 CT scans. The models will help improve the quality and accessibility of patient diagnosis and treatment.



Healthcare Providers And Services

Project Overview

"They spent a year working with two other team members at the Szechwan hospital, in order to learn how the tool they were developing could be integrated with systems used in the hospital such as the Picture Archiving and Communication System (PACS). While there they were able to begin training their algorithms using real data in order to increase its accuracy at spotting warning signs of potentially cancerous nodule growth in lung tissue."

Reported Results

Prediction accuracy levels were not disclosed. "In China there are just 80,000 radiologists who have to work through 1.4 billion radiology scans every year. By using AI and deep learning, we can augment the work of those doctors. In no way will this technology ever replace doctors – it is intended to eliminate much of the highly repetitive work and empower them to work much faster"


Deep supervised networks, likely convolutional neural networks (CNNS) were trained on CT scans of lung cancer. This would allow new scans to be classified as having cancer or not.



General Operations


"In China, lung cancer is the leading cause of death, claiming over 600,000 lives each year, largely due to high levels of air pollution. Radiologists work from CT scan images to hopefully diagnose sufferers at the earliest opportunity. But in a country where there is a serious shortage of qualified doctors, particularly radiologists, this often means they find themselves examining hundreds of images every day. It is incredibly tedious and due to fatigue, mistakes and misdiagnoses are not uncommon."

“In China there are just 80,000 radiologists who have to work through 1.4 billion radiology scans every year." The question was whether doctors could be augmented with machine learning that could predict diseases from scans.



Data sets were of CT scans of normal and cancerous lungs labelled with pathology from health records. Inversion has "company has processed roughly 100,000 CT scans and 100,000 x-rays since its initial installation last year."