The case study highlights how we assisted our customer, a leading DICOM analytics organization, in leveraging deep learning to detect anomalies in X-rays. Initially, healthcare providers relied on traditional methods for diagnosing pneumonia through Chest X-rays (CXR). This process was time-consuming due to the high volume of X-rays needing daily evaluation.
Recognizing the inefficiency, the client sought an AI-based solution to expedite the diagnostic process, allowing radiologists to focus more on further analysis rather than initial diagnosis.
Value Delivered:
- 99% improvement in efficiency for detecting abnormalities in X-ray images
- Radiologists could concentrate on determining the actual cause of abnormalities, such as pneumonia, rather than initial detection
- Increased patient throughput and overall patient care
Download our case study now to read more about:
- Customer’s needs and our execution methodology
- Tech stack for AI integration in medical imaging
- Our deep learning approach and value delivered
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