Oxagile took ChestPal's smart stethoscope software from alpha-stage code to a market-ready release, refactoring the mobile apps, rebuilding the backend into scalable microservices, and delivering the HIPAA and IEC 62304 documentation required for software as a medical device.
How we approach medical image analysis
Each medical imaging project draws on a set of established imaging techniques, selected against the diagnostic goal and the data available.
IMAGE QUALITY IMPROVEMENT
Different scanners, modalities, and capture conditions produce images that vary in character even when each is technically correct. A CT from one manufacturer, an MRI from another, a study captured under different contrast or positioning protocols, they all carry their own signal profile. Preprocessing normalizes these differences so the downstream analysis runs on consistent, comparable input regardless of where or how the image was acquired.
- Noise reduction
- Artefact removal
- Spatial aliasing compensation
- Contrast enhancement
How we build custom medical image analysis solutions
Step 1
Discovery and clinical assessment
We map clinical goals, existing workflows, available datasets, and the regulatory requirements that apply.
Step 2
Solution design and validation
Architecture, strategy, integration points, and success metrics are defined and pressure-tested.
Step 3
Development and model training
Image analysis components are built and customized using the approach that fits the clinical problem.
Step 4
Integration and compliance
The solution connects to PACS, RIS, and EHR/EMR systems through DICOM and standard interfaces with HIPAA alignment.
Step 5
Deployment and ongoing optimization
Launch is followed by continuous monitoring, support, and model improvement as clinical needs shift.
Where does your project start?
Most imaging builds begin with a discovery call to map clinical goals, available data, and the systems involved. Oxagile’s experts can help clear things up.
Why Oxagile
As a medical image analysis software development company, Oxagile shapes every imaging project around clinical workflows from the first sprint. That’s what keeps it working past the demo and into a production setting.
ML expertise→
Oxagile’s AI and machine learning research work pushes health image analysis past pattern matching into anomaly detection, surfacing deviations in cells and organs. The same signal processing foundations apply across visual and auditory diagnostic data.
AR and VR know-how→
A dedicated AR/VR team converts CT, MRI, and other scans into high-resolution 3D models. Care teams can examine every layer of a patient’s anatomy and rehearse the most complex procedures before stepping into the operating room.
Compliance coverage→
Delivery spans all major medical image formats, including DICOM, Analyze, MINC, NIfTI, and ECAT7. Work aligns with the standards that govern clinical software, among them HIPAA, FHIR, and IEC 62304, for security and interoperability.
Medical image analysis use cases
Apply medical image analysis across specialties where early, accurate reads change patient paths.
ONCOLOGYONCOLOGY
Models trained on pathology images identify tumor types and mark suspicious tissue areas for review. The same analytical layer extends to laboratory studies, supporting immunohistochemistry and cell-level mutation analysis alongside the visual read.
CARDIOLOGYCARDIOLOGY
Coronary artery segmentation supports early detection of vessel narrowing and hardening, giving cardiologists a window for intervention before a cardiac event.
PULMONOLOGYPULMONOLOGY
Two- and three-dimensional processing of the lung capillary system supports detection of respiratory conditions such as emphysema and pulmonary embolism. Tracked over time, it also flags early signs of disease progression and acute exacerbations before symptoms escalate.
ORTHOPEDICSORTHOPEDICS
3D bone alignment supports fracture surgery planning and provides digital guidance during the procedure.
OPHTHALMOLOGYOPHTHALMOLOGY
Analysis identifies retinopathy of prematurity, retinal vein occlusion, anterior segment disorders, and other eye conditions, and supports computer-vision-assisted cataract surgery.
Our tech stack
IMAGE FORMATS AND STANDARTS
DICOM • NIfTI • Analyze • MINC • ECAT7
ML AND DEEP LEARNING
TensorFlow • PyTorch • MONAI • Keras
COMPUTER VISION AND IMAGING
OpenCV • ITK • scikit-image • 3D Slicer
CLOUD
AWS • Azure • Google Cloud
IMTEROPERABILITY AND COMPLIANCE
HL7 • FHIR • HIPAA • IEC 62304
Key advantages of medical image analysis software
Built well, imaging software earns its place in the healthcare processes. Oxagile's work focuses on the gains that reach the provider and the patient: faster reads, fewer missed findings, and results inside the systems already in use.
Accelerated workflows
Automated analysis takes over repetitive review tasks and shortens the time between scan and read. Clinicians spend their attention on the studies that need judgment.
Regulatory-ready architecture
Compliance and security are designed in from the first sprint, aligned with HIPAA, IEC 62304, and the standards that govern software in clinical use.
Minimized diagnostic errors
Consistent, reproducible analysis gives clinicians a second read that does not tire or vary by shift. Subtle patterns the eye can miss get flagged for review.
Interoperability across systems
Imaging software earns its place by fitting the stack around it. Oxagile builds for integration with PACS, RIS, and EHR/EMR systems so results land where clinicians already work.
FAQ
Compliance is built into design, not bolted on after. That covers encryption of data at rest and in transit, role-based access control, audit logging, and data handling aligned with HIPAA and, where relevant, IEC 62304 for software in clinical use. Documentation is produced alongside the build so audits do not stall delivery.
Yes. Oxagile builds integration layers that connect custom models to PACS through DICOM and standard interfaces, so inference results return to the same viewer and worklist already in use. RIS and EHR/EMR connections follow the same approach.
Yes. For surgical navigation and other latency-sensitive cases, models can run at the edge, close to the imaging hardware, so guidance keeps pace with the procedure. Model size, hardware, and accuracy targets are set against the clinical requirements during design.
