The client develops software for medical diagnostics and patient monitoring systems. The company’s internal team outlined a new project but lacked specialized machine learning expertise. After an initial discussion, K-Softex assembled a group with direct experience in medical imaging and health data platforms.
The project required a diagnostic assistant to identify early cardiovascular disease indicators. Additionally, the system needed to analyze both echocardiogram images and structured patient records. Doctors would receive AI-generated notes with visual explanations and probability scores, all within an existing hospital software interface.
The client outlined several firm requirements for the collaboration:
A dedicated project lead available for scheduled technical discussions
Transparent documentation of all model limitations and validation results
Adherence to medical data security protocols throughout development
Direct integration paths for hospital databases and wearable device streams.
K-Softex proposed a modular Python architecture to separate the data ingestion, model inference, and result presentation layers. Using a modular design allows hospital IT staff to deploy components across different network zones based on security rules.
Our team selected PyTorch for model development because of its flexibility for custom visual explanation outputs. Data preprocessing routines standardized inputs, from various echocardiogram machines and wearable formats. All models underwent validation on a separate clinical dataset before integration.
One of our developers’ major technical decisions involved the cloud infrastructure. The client required that no patient data leave their private cloud. As a result, we had to design the system so the AI models could be containerized and deployed within the client's existing secure environment to avoid data transfer.
Python / PyTorch / TensorFlow / FastAPI
PostgreSQL / Docker / Kubernetes
React
AWS (for initial development and prototyping), deployed within the client's private cloud
OpenCV / PIL
The diagnostic assistant now operates in three regional clinics. It processes echocardiograms alongside patient history and generates reports that highlight areas of concern with color-coded overlays. Cardiologists use these AI-generated notes as a supplementary checkpoint during their review.
The client reports that the system identifies subtle anatomical patterns often missed in initial manual reviews. Since deployment, the platform has processed over fifteen thousand studies. The client's engineers have assumed maintenance of the codebase, and both teams are discussing a second phase to expand the model's diagnostic scope.
Get in touch
Connect with us
info@ksoftex.comWant to join our team?
careers@ksoftex.com