Artificial intelligence can detect and diagnose colorectal cancer equal to or better than pathologists by examining tissue scans.

 – Tulane University researchers discovered that artificial intelligence can accurately detect and diagnose colorectal cancer by analyzing tissue scans as well or better than pathologists.

The study was a collaborative effort by Tulane, Central South University in China, the University of Oklahoma Health Sciences Center, Temple University, and Florida State University. The purpose was to determine whether artificial intelligence could be a tool to assist pathologists in keeping up with the rising demand for services.

According to the researchers, pathologists regularly evaluate and label thousands of histopathology images to identify whether a patient has cancer. However, their average workload has significantly increased, which could lead to unintended misdiagnoses.

“Even though a lot of their work is repetitive, most pathologists are extremely busy because there’s a huge demand for what they do but there’s a global shortage of qualified pathologists, especially in many developing countries” professor and director of the Tulane Center of Biomedical Informatics and Genomics at Tulane University School of Medicine, Hong-Wen Deng, PhD, said in a press release.

“This study is revolutionary because we successfully leveraged artificial intelligence to identify and diagnose colorectal cancer in a cost-effective way, which could ultimately reduce the workload of pathologists.”

The research team collected over 13,000 images of colorectal cancer from 8,803 subjects and 13 independent cancer centers in China, Germany, and the United States. Then, using images that technicians selected at random, the researchers built a machine learning program.

The program allows a computer to recognize images that depict colorectal cancer, which according to researchers, is one of the most common causes of cancer-related deaths in Europe and the US.

“The challenges of this study stemmed from complex large image sizes, complex shapes, textures, and histological changes in nuclear staining,” Deng explained.

“But ultimately the study revealed that when we used AI to diagnose colorectal cancer, the performance is shown comparable to and even better in many cases than real pathologists.”

Deng and this team used a performance measurement tool to determine the success of the study. After comparing the computer’s results with the work of the pathologists, the study indicated that the average pathologist scored around 0.969 for accurately when identifying colorectal cancer.

According to the research team, the average score for the machine-assisted AI computer program was 0.98, proving to be slightly more accurate than the manual data by pathologists.

Artificial Intelligence is an emerging technology when it comes to identifying cancers. According to the press release, Deng hopes that the study will influence pathologists to use more prescreening technology to speed up diagnosis.

“It’s still in the research phase and we haven’t commercialized it yet because we need to make it more user friendly and test and implement in more clinical settings. But as we develop it further, hopefully it can also be used for different types of cancer in the future. Using AI to diagnose cancer can expedite the whole process and will save a lot of time for both patients and clinicians,” Deng said.