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Tia Albright

How AI Algorithms Work on X-Rays

Adoption, System Integration
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3.6 billion. That’s the number of diagnostic radiology exams, including x-rays, that are taken annually throughout the world, according to the World Health Organization (WHO). With the frequent use of x-rays and their role in diagnosis of sometimes time-sensitive conditions, like fractures and infections, AI can have a significant impact on speed, accuracy and efficiency. But how do they work together? 

For x-rays, AI algorithms use computer vision — a field that enables machines to see and interpret data — and deep learning — a type of machine learning that analyzes data at exceptional speeds without compromising accuracy — to detect patterns, classify conditions and assist in diagnosis.

The AI X-Ray Reading Process

Step 1: X-Ray AI Pre-Process
In the first step, the x-ray is uploaded. It can be uploaded via camera, direct capture or from PACS. The x-ray AI then normalizes the pixel values based on the acquisition metadata to ensure consistent interpretation across studies. This normalization adjusts the intensity range, so the brightness and contrast reflect meaningful anatomical detail.

Step 2: X-Ray AI Feature Extraction
Once uploaded and pre-processed, the image is then processed layer by layer using a convolutional neural network (CNN) — a neural network designed to detect patterns in images (think: edges, shapes, textures). The first layers detect simple patterns, like edges and textures, while the later layers delve into more complex anatomical cues that help the model spot subtle pathologies. These are then used to create a feature map showing visual patterns that might indicate disease.

Step 3: X-Ray AI Classification/Detection
With information gathered, the x-ray AI (based on trained data) classifies the image — for example as normal or fracture — or detects specific findings — for example nodules — with bounding boxes. In some cases, a model may generate a probability score to show the confidence in the diagnosis. 

Step 4: X-Ray AI Annotation/Visualization
Next, AI may overlay the findings on the x-ray. This can be with bounding boxes, explainability tools or heatmaps, providing transparency into why the AI made the decision. The radiologist can then use these visual tools to verify or challenge the results from the AI.

Step 5: Integrating X-Ray AI Findings into the Workflow 
Depending on the health system’s setup, the results may be sent to the radiologist’s worklist or embedded in the PACS viewer. These findings can then be prioritized if the case appears to be critical, pre-filled into report sections or trigger alerts for follow-up care.

Use Case: Algorithms in X-Ray AI Reading

Here’s an example of how Aidoc’s AI solution has impacted x-ray interpretation.

Enhanced detections. Gleamers’ BoneView solution, deployed at Boston University, completed 480 exams across 24 readers. It resulted in a 16% increase in sensitivity (64.8% to 75.2%) 22% increase in sensitivity rates for multi-fractures exams and 30% decrease in missed fractures.1

The Benefits of AI in Medical Imaging

As healthcare evolves, we’ll see even more benefits of AI in medical imaging. Today, the impact is very clear. Here are the five key benefits.

  1. Diagnostic aid – With the ability to analyze large amounts of imaging data in real time and highlight potential issues, clinicians can potentially get information earlier that leads to improved patient outcomes.
  2. Reduced human error – Radiologists are burned out and under pressure to read imaging as quickly as possible, AI can help reduce the risk of human error by detecting suspected abnormalities.
  3. Efficiency and speed – AI helps automate some of the imaging process, allowing radiologists to focus on more complex aspects of image interpretation.
  4. Better resource allocation – With AI support as mentioned above, radiologists can put their time into high-value tasks. The results: saved time and reduced costs.
  5. Personalized patient care – With AI’s personalized insights based on a patient’s unique imaging, clinicians are better able to predict disease progression or patient therapy response.

Explore these benefits further in our blog — “5 Benefits of AI in Medical Imaging”.

The Future of AI in Medical Imaging

While AI in medical imaging was roughly valued around $1.8 billion in 2025, it’s poised to surpass $14.8 billion by 2032, according to a report from Market.us Media. As more health systems bring AI into the mix, it can impact the field in a variety of ways, including: 

  • Becoming a staple in triage and prioritization 
  • Widespread adoption of multimodal AI systems
  • Standard imaging including predictive and preventative AI 

For a deeper dive into the future of AI in medical imaging, check out our webinar — “How It Started Versus How It’s Going: AI Early Adopters Discuss How the Technology Has Changed Practice”. 

Ready to bring AI to your health system?
Find out how aidoc can help.

Citations

  1. Boston University School of Medicine. “Study finds artificial intelligence accurately detects fractures on x-rays, alert human readers.” ScienceDaily. ScienceDaily, 21 December 2021. www.sciencedaily.com/releases/2021/12/211221102818.htm

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Tia Albright
Tia Albright
Manager, Radiology and ED Marketing Communications

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