We have currently developed algorithms for detecting breast (mammography) and chest X-Ray abnormalities. Soon we will have other modalities and body parts. Stay tuned!
Increase accuracy and reduce recall rate by comparing Aquila’s results with radiologists' results. Our platform will provide a report with the % of mismatch between man and machine, allowing imaging centers and hospitals to have a quality control system in place and analyse whether certain exams need to be re-interpreted.
Double Reading/Decision Support
Increase accuracy by providing a virtual radiologist to detect diseases at the same time as the radiologist. This process allows radiologists to view Aquila’s opinion before, during, or after his/her analysis.
Increase productivity by separating your current flow into normal and abnormal. By doing that radiologists can prioritize their worklist. When they arrive at the office in the morning, with a rested mind, radiologists can opt to read the high risk cases first. This new process allow radiologists to be quicker without jeopardizing quality.
Aquila was founded in 2017, when Fernando, co-founder of the second largest PACS and RIS company in South America, saw the possibility of using AI to support radiologists to increase the confidence in diagnosis. He decided to assemble a killer team with diverse expertise and experiences to tackle the problem. Rod is a growth aficionado and has experience in scaling small businesses, Miguel is a seasoned machine learning expert and Marco has extensive experience in building and scaling health IT platforms.
For those wondering about the name, Aquila, means "eagle" in Latin. Just like eagles, machines can "see" things the human eye can't. However, machines can't make the complex decisions humans can. That's why we are strong believers that machines AND humans should join efforts to achieve better results in medicine.
We believe in machines helping humans, not replacing them. Our vision is to create a world where radiologists use AI to have a flawless diagnosis at an increased speed, providing a much higher care delivery and, ultimately, saving lives.
We use proprietary machine learning algorithms to read medical imaging and detect diseases for breast and chest x-ray with accuracy above 90%. By using our algorithms, radiologists can achieve higher detection rates and reduce recall rates.