Tulane University Study Uses AI to Detect Colorectal Cancer

A Tulane University researcher found that artificial intelligence (AI) can accurately detect and diagnose colorectal cancer from tissue scans as well or better than pathologists, according to a new study in the journal Nature Communications.

The study, which was conducted by researchers from Tulane, Central South University in China, the University of Oklahoma Health Sciences Center, Temple University, and Florida State University, was designed to test whether AI could be a tool to help pathologists keep pace with the rising demand for their services.

Pathologists evaluate and label thousands of histopathology images on a regular basis to tell whether someone has cancer. But their average workload has increased significantly and can sometimes cause unintended misdiagnoses due to fatigue.

"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" said Dr. Hong-Wen Deng, professor and director of the Tulane Center of Biomedical Informatics and Genomics at Tulane University School of Medicine. "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."

To conduct the study, Deng and his 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. Using the images, which were randomly selected by technicians, they built a machine assisted pathological recognition program that allows a computer to recognize images that show colorectal cancer, one of the most common causes of cancer related deaths in Europe and America.

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

The area under the receiver operating characteristic (ROC) curve or AUC is the performance measurement tool that Deng and his team used to determine the success of the study. After comparing the computer’s results with the work of highly experienced pathologists who interpreted data manually, the study found that the average pathologist scored at .969 for accurately identifying colorectal cancer manually. The average score for the machine-assisted AI computer program was .98, which is comparable if not more accurate.

Using artificial intelligence to identify cancer is an emerging technology and hasn’t yet been widely accepted. Deng’s hope is that the study will lead to more pathologists using prescreening technology in the future to make quicker diagnoses.

"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."

Yu G, Sun K, Xu C, Shi XH, Wu C, Xie T, Meng RQ, Meng XH, Wang KS, Xiao HM, Deng HW.
Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images.
Nat Commun. 2021 Nov 2;12(1):6311. doi: 10.1038/s41467-021-26643-8

Most Popular Now

Digital ECGs at Barts Health: A High-Imp…

Opinion Article by Dr Krishnaraj Sinhji Rathod, consultant in interventional cardiology, Barts Health NHS Trust. Picture the moment. A patient in an ambulance, enroute to hospital with new chest pain. Paramedics...

Study Sheds Light on Hurdles Faced in Tr…

Implementing artificial intelligence (AI) into NHS hospitals is far harder than initially anticipated, with complications around governance, contracts, data collection, harmonisation with old IT systems, finding the right AI tools...

Using Deep Learning for Precision Cancer…

Altuna Akalin and his team at the Max Delbrück Center have developed a new tool to more precisely guide cancer treatment. Described in a paper published in Nature Communications, the...

New AI Approach Paves Way for Smarter T-…

Researchers have harnessed the power of artificial intelligence (AI) to tackle one of the most complex challenges in immunology: predicting how T cells recognize and respond to specific peptide antigens...

Study Used AI Models to Improve Predicti…

Chronic kidney disease (CKD) is a complex condition marked by a gradual decline in kidney function, which can ultimately progress to end-stage renal disease (ESRD). Globally, the prevalence of the...

AI-Powered CRISPR could Lead to Faster G…

Stanford Medicine researchers have developed an artificial intelligence (AI) tool to help scientists better plan gene-editing experiments. The technology, CRISPR-GPT, acts as a gene-editing “copilot” supported by AI to help...

Groundbreaking AI Aims to Speed Lifesavi…

To solve a problem, we have to see it clearly. Whether it’s an infection by a novel virus or memory-stealing plaques forming in the brains of Alzheimer’s patients, visualizing disease processes...

AI Spots Hidden Signs of Depression in S…

Depression is one of the most common mental health challenges, but its early signs are often overlooked. It is often linked to reduced facial expressivity. However, whether mild depression or...