Article from Applied Radiology. Artificial Intelligence in...

Article from Applied Radiology.

Artificial Intelligence in Radiology: Hype or hope?
By Mary Beth Massat.

Deep learning

Enlitic (San Francisco, CA) is developing a deep learning tool for radiologists that augments their reading and interpretation. Last May, the company won the top prize of €1 million at the first Cube Tech Fair in Berlin, Germany.

“We are developing AI with the goal to cover 95% of diagnostic radiology by 2020,” says Kevin Lyman, COO at Enlitic. The company’s focus, he explains, is to enhance radiologists’ efficiency, proficiency, and more importantly, accuracy.

There are three different ways that Enlitic sees the potential for deep learning AI in radiology. One is to perform quality assessments, or a second read, after initial interpretation on the images and the report. Two, to triage incoming studies in order to prioritize and appropriately route through the organization. Three, to deliver real-time diagnostic support alongside a radiologist.

Working with Capitol Health Ltd. in Melbourne, Australia, Enlitic deployed a wrist fracture detection system using in-house deep learning models to circle fractures in X-rays, displaying these annotations in the PACS viewer for reading by radiologists. The study measured the accuracy and efficiency of three specialist radiologists, each reading a total of 400 studies, with and without assistance from Enlitic models. The study found that radiologists augmented by Enlitic were 21% faster, 11% more sensitive and 9% more specific in their reading.

The company has also trained a deep learning model to detect visual patterns in a chest X-ray for a differential diagnosis. In a blind test deployed in several countries, the model was used as a triage tool to separate normal from abnormal exams and demonstrated a 16% increase in AUC (areas under the ROC curve) compared to human (radiologist) interpretation.

“Interestingly, we learned that in different parts of the world, there are different ways to use these models,” Lyman explains. “I often talk about how we aren’t seeking to replace radiologists. One of our goals is to help bring radiology to regions in the world that don’t have enough radiologists,” he adds.

Take China, for example. Lyman says that although the country has five times the population of the US, it has one-third fewer radiologists. He shares that a single network of clinics in China can perform up to 5 million chest X-rays a year, and would perform even more if they had enough radiologists.

In another ongoing clinical study that Lyman hopes will be accepted for publication in late 2018, Enlitic’s AI model is capable of finding malignancies in low-dose chest CT lung cancer screening studies up to two years earlier than radiologists. In Lyman’s opinion, this level of accuracy and foresight is one of the most interesting applications of clinical AI.

The development of these algorithms at Enlitic is a mix of deep learning and other AI tools, Lyman explains. “If we take a complex process, it needs to be a series of algorithms that together better replicate the system as a whole. First, we focus on the quality of the data for building the algorithms, then determine if it actually answers the clinical question. However, we must also consider the user experience. These are tools designed by radiologists for radiologists, not to just use them but to trust them. That idea of trust needs to be packed into our product development.”