Role of an AI for Reliable Screening of Abnormality in X-rays: A Prospective Multicenter Study on Operational Efficiency using a CE approved solution
Chest X-rays are the most common diagnostic imaging technique used in clinical practice. The patient care pathway is, however, significantly hampered in most high-volume healthcare centers. Therefore, an Artificial Intelligence system that can quickly, reliably, and accurately identify anomalies in chest X-rays is being agreed as essential for enhancing radiological workflow.
Here are some of the key challenges in correctly identifying the abnormalities on a chest X-ray (CXR)
For example: Sometime even well-trained radiologists find it challenging to differentiate between the lesions or correctly identify very obscure pulmonary nodules.
Objective: To conduct a CE-approved post-market study which is also a prospective multi-center and multi-reader study prospective multicenter quality-improvement study. The team evaluated whether artificial intelligence (AI) can be used as a chest X-ray screening tool in real clinical settings.
Method: A team of expert radiologists used Qure.ai’s CE- approved AI-based chest X-ray screening tool (qXR) as a part of their daily reporting routine to report consecutive chest X-rays for this prospective multi–centre study. This study took place in a large radiology network in India for a period of 10 months. This was done is over 35 + sites by ~120 expert radiologists.
“AI-based chest X-ray solution (qXR
) screened chest X-rays and assisted in ruling out normal patients with high confidence, thus allowing the radiologists to focus more on assessing pathology on abnormal chest X-rays and treatment pathways.”
“qXR helped decrease the mean TAT by over 40%, and 99% of the AI reported normal CXRs were actually normal.”