Short Read
Wake Radiology (WR), founded in 1953, is the oldest and largest medical imaging provider serving the cities of Raleigh, Durham and Chapel Hill, which have the largest and fastest growing population sizes in North Carolina, USA. Originally established to serve just 65,000 residents in Raleigh and seeing not more than 10 patients per day, WR now has presence in 14 locations in the Triangle, serving more than 2.7 million residents and growing.
Throughout its history, WR has been a pioneer in introducing cutting edge technologies to better serve its residents, such as the first introduced vascular/interventional radiology program in the 1960s till today, deploying AI alongside its radiologists to improve accuracy and efficiency.
To this end, WR has adopted Infervision’s CT Lung AI detection system to quickly assess patients and recommend likely diagnoses to keep up with demand.
Key Issues
After implementing AI, WR saw approximately a 33% reduction in reading time of CT chest low dose lung cancer screening studies, reducing the turnaround time to get actionable information to the patient and their physician, Wake Radiology has seen a spike in lung screening cases, which has lengthened the time it takes to analyze dataand charts. In 2020, WR performed 11,200 CT chest exams, including 1700 low-dose CTs for lung cancer screening. By 2024, that number had risen to 13,000. Nodule detection is a time-consuming and repetitive task, and human error can lead to inaccuracies. Since screenings may not show symptoms, while radiologists' time is limited and "blind spots" within the lungs exists.
Enhancing Results, Saving Precious Time
In light of the increased volume and the desire to increase throughput while maintaining a high level of clinical accuracy, Wake Radiology chose to work with Infervision, adopting Infervision's CT Lung AI solution. The CT Lung system is compatible with various PACS (Picture Archiving & Communication Systems) used by both WR and partner hospitals, which helps medical professionals save time and effort on communication.
In addition, it assists radiologists in finding commonly missed areas, such as the hilum and costophrenic sulcus. In 170 reviewed cases, AI showed great accuracy than radiologists in detection rates, amounting to 1.8% of reviewed cases, which were missed. After the success of the pilot program and reviewing the improvement in efficiency, WR has implemented the solution across all its centers for CT chest scans.
What’s next for Wake Radiology?
After seeing how AI can help alleviate staff workloads and improve patient outcomes, radiologists are excited for what AI technology can bring to healthcare. They are more open and optimistic about AI applications and how they can be applied to different clinical areas of need, bringing much needed help where time and accuracy matters.
Conclusion
AI in radiology will help save precious time and reduce mistakes for radiologists, easing the tedious aspects of the workload and improving patient diagnostic accuracy and outcomes. Both radiologists and patients will benefit greatly from AI-assisted algorithms in healthcare such as Infervision’s CT Lung.
