AI Approach Outperformed Human Experts in Identifying Cervical Precancer

An artificial intelligence approach called automated visual evaluation has the potential to revolutionize cervical cancer screening.

Photo: Cybrain/iStock

A research team led by investigators from NIH and Global Good has developed a computer algorithm that can analyze digital images of a woman’s cervix and accurately identify precancerous changes that require medical attention. This artificial intelligence (AI) approach, called automated visual evaluation, has the potential to revolutionize cervical cancer screening, particularly in low-resource settings.

To develop the method, researchers used comprehensive datasets to “train” a deep, or machine, learning algorithm to recognize patterns in complex visual inputs, such as medical images. The approach was created collaboratively by investigators at NCI and Global Good, a fund at Intellectual Ventures, and the findings were confirmed independently by experts at the National Library of Medicine. The results appeared in the Journal of the National Cancer Institute on Jan. 10.

“Our findings show that a deep learning algorithm can use images collected during routine cervical cancer screening to identify precancerous changes that, if left untreated, may develop into cancer,” said Dr. Mark Schiffman of NCI’s Division of Cancer Epidemiology and Genetics, who is senior author of the study. “In fact, the computer analysis of the images was better at identifying precancer than a human expert reviewer of Pap tests under the microscope [cytology].”

The new method has the potential to be of particular value in low-resource settings. Health care workers in such settings currently use a screening method called visual inspection with acetic acid (VIA). In this approach, a health worker applies dilute acetic acid to the cervix and inspects the cervix with the naked eye, looking for “aceto whitening,” which indicates possible disease. Because of its convenience and low cost, VIA is widely used where more advanced screening methods are not available. However, it is known to be inaccurate and needs improvement.

Automated visual evaluation is similarly easy to perform. Health workers can use a cell phone or similar camera device for cervical screening and treatment during a single visit. In addition, this approach can be performed with minimal training, making it ideal for countries with limited health care resources, where cervical cancer is a leading cause of illness and death among women.