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AI Case Study

Enlitic augments radiologists to achieve 21% faster, 11% more sensitive and 9% more specific reading of fractures in X-rays with deep learning

Enlitic, in collaboration with Capitol Health Ltd., tested a wrist fracture detection system in Melbourne, Australia. The research focus on measuring the accuracy and efficiency of three specialist radiologists in reading fracture annotations in wrist X-rays. When measuring their performance with and without assistance from Enlitic models, the study showed that the specialists were 21% faster, 11% more sensitive and 9% more specific in their reading when augmented by the system.



Healthcare Providers And Services

Project Overview

"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."

Reported Results

"The study found that radiologists augmented by Enlitic were 21% faster, 11% more sensitive and 9% more specific in their reading."


"Enlitic works with a wide range of partners and data sources to develop state-of-the-art clinical decision support products.

Our deep learning technology can incorporate a wide range of unstructured medical data, including radiology and pathology images, laboratory results such as blood tests and EKGs, genomics, patient histories, and electronic health records (EHRs). This richness allows higher accuracy and deeper insights for every patient.

Our solutions integrate seamlessly into your existing health system infrastructure. For example, our radiology solutions communicate with third party image viewers and archiving systems."


R And D

Core Research And Development


"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 rst 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, pro ciency, 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 appropri- ately route through the organization. Three, to deliver real-time diagnostic support alongside a radiologist." (

"The firm's technology is currently being tested in 40 clinics across Australia (Economist)."




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