AI-powered Radiology Reports for Smarter Patient Care

Building a teleradiology application isn’t easy. It is a huge challenge to integrate data from X-ray, MRI, Mammography, CT Scan, and Ultrasound machines into an app’s dashboard for analysis.

These machines spit out vast amounts of complex data, and the trick is to grab it, process it, and turn it into accurate reports, all in real time. Data transfer is a beast of its own. Moving huge chunks of scan data from the machines to an app’s database without losing speed or quality is a serious task.

It is not just about dumping the data in—it has to arrive clean, processed, and ready for analysis. This means building super-slick algorithms that can take in all that raw info and make sense of it fast. There will be no lags, no hiccups, just seamless performance from start to finish.

The client grappled with the monumental challenge of ensuring all the above tasks. They faced trouble transferring medical images from X-rays, MRIs, and other such machines to an app to impact patient care positively. They needed advanced AI to analyze complex radiological images, maximizing diagnostic precision.

Moreover, nobody wants to wait for reports, especially in critical medical cases. So, the application’s backend had to be lightning-fast and tough enough to handle a flood of incoming scans without breaking a sweat.

The client tasked Capital Numbers with cracking each of the above challenges. Not only did we have to pull all medical images using a mix of cutting-edge AI and a reporting solution, but we also had to add multiple user roles for radiologists, QA users, and the super admin to seamlessly access the app.

First up was pulling raw data from the machines and sending them to the application. This was no easy task. Capital Numbers leveraged Python scripts to pull raw data from MRI scanners, X-ray machines, etc., and transfer it into Orthanc, a specialized DICOM server for medical imaging. This wasn’t just about moving data; it was about moving data fast and error-free, ensuring no loss of crucial details.

But raw machine data isn’t human-friendly. So, Capital Numbers built custom extensions in Orthanc to decode this raw information and turn it into human-readable formats. These extensions were the secret sauce that prepared the data for analysis by clinical teams and radiologists. It was about making data actionable.

Once the data was made human-readable, it was transferred from Orthanc to the app’s MySQL database. This syncing had to be smooth as butter—structured, fast, and reliable. So, we entirely used advanced data syncing mechanisms and AI algorithms to transfer data between systems and lay the groundwork for analysis in editable reports.

These weren’t just one-size-fits-all reports—they were fully editable, allowing radiologists to fine-tune findings and add inputs as needed. Once finalized, the reports were saved as PDFs in AWS S3 buckets, ensuring secure and fast access at any time.

To ensure the app was highly secure, we integrated Keycloak for role-based access, ensuring radiologists, QA users, and super admins had customized permissions and the workflow was protected. Paired with that was a Node.js backend for speed and a sleek React frontend that ensured a seamless user experience.

By combining a robust backend, an intuitive frontend, advanced AI, and seamless data handling, we delivered a teleradiology app that’s fast, reliable, and built to scale. Capital Numbers didn’t just develop software—we engineered a cutting-edge solution that transforms how radiology workflows are managed.

The outcome is an app, excellently crafted by Capital Numbers, that lightens the load for radiologists by using AI to generate spot-on reports and scans instantly. Whether for fractures, spine scans, MRIs, or others, the app displays and interprets accurate medical images and lets radiologists fine-tune every report for flawless results.

Here’s a quick look at what the app offers: