Scientists say they have developed artificial intelligence (AI) based computer algorithm that can identify cervical pre-cancer with greater accuracy than a human expert. The approach, called automated visual evaluation, has the potential to revolutionise cervical cancer screening, particularly in low-resource settings, said researchers from the National Institutes of Health (NIH) in the US. They used comprehensive datasets to "train" a deep, or machine, learning algorithm to recognise patterns in complex visual inputs, such as medical images. "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 Mark Schiffman from National Cancer Institute (NCI) in the US.
"In fact, the computer analysis of the images was better at identifying pre-cancer than a human expert reviewer of Pap tests under the microscope (cytology)," said Schiffman, senior author of the study published in the Journal of the National Cancer Institute. 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 to 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. 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.
The research team used more than 60,000 cervical images from an NCI archive of photos collected during a cervical cancer screening study that was carried out in Costa Rica in the 1990s. Over 9,400 women participated in that study, with follow up that lasted up to 18 years. Because of the prospective nature of the study, the researchers gained nearly complete information on which cervical changes became pre-cancers and which did not.
The photos were digitised and then used to train a deep learning algorithm so that it could distinguish cervical conditions requiring treatment from those not requiring treatment. Overall, the algorithm performed better than all standard screening tests at predicting all cases diagnosed during the Costa Rica study. The automated visual evaluation identified pre-cancer with greater accuracy than a human expert review or conventional cytology.
(With inputs from agencies.)