Enhancing Healthcare with 3D Medical Imaging Integration

3D MEDICAL IMAGING SOFTWARE

3D Medical Imaging Software Development

Our team built a 3D medical imaging software for reconstruction of bones, skin, and various organs from X-rays and CT scans implementing machine learning.

#Healthcare

#IoT

#ComputerVision

Client*

A healthcare technology firm producing advanced devices and software that support healthcare professionals in their everyday tasks.

*We cannot provide any information about the client or specifics of the case study due to non-disclosure agreement (NDA) restrictions.

Project in numbers

duration

Ongoing project

team

12 specialists

Team involved in the project

industry

Healthcare

solution

Web

technologies

Python, FastAPI, PyQt, JavaScript, React, MS SQL Server, Weights and Biases, MLFlow, PyTorch, OpenCV, TensorFlow, Keras, ONNXRuntime, PyDICOM, Albumentations, AWS (S3, EC2, Lambda), AWS SageMaker (Studio, Model Monitoring, Inference endpoint), Qase, Postman, Swagger, TestFlight, Arduino, Thonny

Services

2 x Front-end developers

2 x Back-end developers

1 x Project manager

4 x ML engineers

2 x QA specialists

1 x UX/UI Designer

Challenge

Develop an ML-based tool that could do 3D medical imaging of bones, skin, and other body parts by converting flat scans into three-dimensional volumetric models from X-rays and CT scans.

Solution & functionality

We integrated medical imaging analysis into the customer’s system, ensuring compatibility with X-rays and CT scans from radiology, cardiology, and other labs. As a result, all the 3D medical images can be accessed across hospital workstations and personal laptops.

3D rendering for X-rays and CT scans

Conversion of black-and-white images into 3D medical models takes just a few clicks. Once the X-ray or CT scan is uploaded, clinicians can set threshold attenuation values to define 3D detail, and let the platform scan each piece and create voxels reconstructing denser body fragments. This results in volumetric 3D medical images.

After rendering, clinicians can use a toolbar to manage objects: zoom in/out, add/remove skin, tissue, muscles, bones, and cut away excess parts. The primary tool, a cube, allows the image rotation for a more accurate view of the pathology.

Compatibility and security for DICOM files

Initially, we made sure that the web platform effortlessly works with DICOM files, the standard format for medical imaging management. Next, we enhanced security to safeguard the confidential health information they carry.

Our developers built a secure space to store imported DICOM files, encompassing patient details, diagnoses, treatments, dates, and test results.

ROI manager for highlighting pathologies

Our team developed an advanced ROI manager for highlighting pathology. Doctors can easily identify and outline tumors in 3D reconstructions, and measure lesion sizes for informed surgical decisions.

For precise segmentation, our developers set thresholds, pixel values, and previews. This allows for detailed 3D customization in form of reports with anatomical annotations and organ distance measurements helping more accurate surgical planning. In addition, practitioners can export and share 3D images based on the user access.

Results and business value

The 3D rendering platform allows professionals to monitor organs, evaluate tissue composition, assess fractures and thus diagnose diseases accurately. The platform generates detailed 3D medical imaging models and reports with anatomical annotations, as well as measures tumors, pathologies, and distances between organs for precise and effective surgical planning.

3X

faster pre-operational preparation

30%

more accurate diagnoses

Related cases

We appreciate your interest in Timspark

    IoT & ML-based Energy Management Solution

    IOT FOR ENERGY MANAGEMENT

    IoT & ML-Based Energy Management Solution

    The team developed software for IoT energy management, specifically tailored to monitor wind turbines and manage energy production.

    #IoT

    #Energy

    #ML

    Client*

    A leading business in the renewable energy industry for over 20 years, specializing in wind energy and overseeing a vast network of wind turbines across multiple regions.

    *We cannot provide any information about the client or specifics of the case study due to non-disclosure agreement (NDA) restrictions.

    Project in numbers

    duration

    Ongoing project

    team

    14 specialists

    The team involved in the project

    industry

    Energy

    solution

    Data analytics

    technologies

    JavaScript, React, Redux, Python, FastAPI, Apache Spark, Kubernetes, Docker, AWS, PostgreSQL, Grafana

    Services

    1 x Business analyst

    1 x Project manager

    1 x Solution architect

    3 x Back-end developers

    1 x Front-end developer

    1 x Embedded developer

    1 x ML developer

    1 x DE developer

    1 x DevOps Specialist

    2 x QA engineers

    1 x Stakeholder’s SME

    Challenge

    Build an IoT energy management solution empowered with ML algorithms for real-time monitoring and predictive analysis of wind turbine performance. The main goal is to prevent system malfunctions that could cause power outages and costly repairs.

    Solution & functionality

    The team came up with an IoT & ML-driven energy management software solution that predicts energy production. An advanced platform provides real-time updates on the status of each wind turbine based on the information accumulated from meteorological sensors and turbines.

    Programmable logic controllers (PLC)

    We utilized programmable logic controllers (PLCs) to gather data from sensors placed across the wind turbines. They monitor various operational metrics, like wind speed, turbine rotation speed, temperature, vibration, and torque, process the data and provide a precise overview of the wind turbine’s current performance, identify faults, and energy production efficiency. Additionally, system detects deviations, like an unexpected temperature rise or increased vibration — to prevent damage, it triggers alarms or shuts down the turbine. Such timely maintenance and malfunction prevention ensures balanced energy production and extends equipment lifespan.

    Data visualization

    To visualize data, our project team chose Grafana dashboards. We created customized actionable charts for IoT energy management displaying data like daily power output, turbine locations, weather patterns, and predicting future trends. Thanks to these visualizations operational managers have access to a real-time overview of turbine performance, while maintenance teams can quickly address turbine issues.

    Data lake

    The client needed a robust data lake, as they operate wind turbines across various regions. Our developers created a central repository to collect and store data from all turbines, regardless of their location, including structured, unstructured, and semi-structured data such as logs, sensor readings, and images. Data is collected from the PLCs and then stored and processed using AWS IoT Core and Lambda functions. Large datasets can be processed simultaneously, which greatly supports predictive maintenance and accelerates analysis and reporting.

    Error prediction

    Leveraging data science and MLOps, we developed a predictive model that evaluates various factors influencing turbine health, such as vibration and temperature levels, and performance metrics. This model continually learns from incoming data and enables the operational managers to detect warning signs of failures early. Upon identifying them, the energy management control system sends alerts to the maintenance teams so that they proactively address the issues before they cause breakdowns.

    Analytical reports

    The energy management system can generate analytical reports based on the historical and real-time data to provide insights into wind turbine performance. This data helps identify well-operating turbines and those needing maintenance. Also, by analyzing performance trends and external factors like weather, the system suggests ways to optimize energy consumption, determine ideal times for energy harvesting, manage storage, reduce costs, and streamline maintenance.

    Results and business value

    The team successfully implemented IoT in energy management, providing the client with a scalable energy management control system.

    up to 6%

    increase in energy production

    18%

    reduction in maintenance and repair costs

    26

    critical threats prevented

    Benefits for client

    The solution helps prevent system malfunctions that could cause power outages and costly repairs. As a result, the client achieved 18% reduction in maintenance and repair costs and up to 6% increase in energy production.

    Related cases

    We appreciate your interest in Timspark

      Let’s build something great together

        Let’s build something great together

          Let’s build something great together