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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

Custom Software Development / Backend Development 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

hire the team

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

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    Bus fleet management software: 95% accuracy in real-time bus fleet tracking

    FLEET MANAGEMENT SYSTEM

    Bus Fleet Management Software: 95% Accuracy in Real-Time Bus Fleet Tracking

    The team created an interactive dashboard for a bus fleet management software, improving the accuracy of bus timetables and passenger counts.

    #IoT

    #Logistics

    #DataManagement

    Client*

    A leading bus operator distinguished for its extensive array of transportation services, particularly in Europe.

    *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

    14 months

    team

    5 specialists

    The team involved in the project

    industry

    Logistics

    solution

    GPS tracking software

    technologies

    Python, Flask, Pandas, Azure SQL, Microsoft Azure, Power BI, Apache, Nginx, Prometheus, Grafana

    1 x Full-stack developer

    1 x BI developer

    1 x Data analyst

    1 x Project manager

    1 x QA engineer

    hire the team

    Challenge

    Improve the overall performance and user experience of the bus fleet management software

    Objectives

    Implement tracking of bus movement and resource allocation

    Update the dashboard to achieve data accuracy

    Make the user interface more intuitive

    Solution & functionality

    The team developed an advanced interactive dashboard system for the bus fleet GPS tracking with a user-friendly interface and focus on precise real-time data tracking.

    Interactive dashboard with GPS tracking and alerts

    Our team created a dynamic dashboard using data from IoT sensors, integrated with an Azure SQL database. Due to their ability to process and manage large datasets, the dashboard allows real-time tracking of buses and sending timely updates on bus positions, arrival times, any delays or schedule deviations, as well as passenger numbers.

    Enhanced data accuracy

    The system is designed to handle and analyze complex datasets, making predictions and assessments. The predictive algorithms evaluate past and current traffic data to recommend optimal routes and assess trends in passenger numbers. Through analysis of traffic patterns, weather conditions, and previous delays, the system foresees potential disruptions for planning. Altogether, these features help refine the transport schedules and bus frequencies, helping to reduce expenses and nurturing clients’ loyalty.

    Viewer and administrator roles

    The dashboard holds distinct user roles to accommodate diverse levels of engagement and operational requirements.

    Viewer role

    With the viewer role, users can access an interactive map with current bus positions, route status, and estimated arrival times in real-time, and a flexible analytics dashboard giving predictions about possible disruptions, allowing for route and scheduling adjustments and sharing reports.

    Administrator role

    Users with the administrator role have full control and supervision over the dashboard’s configurations and processes with data. They can customize the dashboard’s interface and features to evolving requirements and manage user access levels.

    Results and business value

    The new interactive dashboard for the bus fleet management software contributed to a more efficient, timely, and reliable transport service.

    2X

    faster data analysis

    30%

    reduction in operational delays

    95%

    accuracy in arrival and departure time tracking

    Benefits for client

    With implementation of a dashboard system and a user-friendly interface the bus arrival and departure times reached near-perfect accuracy, reducing wait times for passengers. Better operational performance and customer satisfaction was the ultimate goal of the client.

    Related cases

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      IIoT platform for a manufacturing company: 20-30% boost in productivity

      IoT PLATFORM

      IIoT Platform for a Manufacturing Company: 20-30% Boost in Productivity

      The client turned to Timspark for IoT application development from scratch. Our team was supposed to build a smart web platform that would contribute to the whole production management ecosystem in one of the client’s factories, optimize its working processes, and improve productivity.
      #IoT
      #Manufacturing
      #WebDevelopment

      Client*

      The client is a large manufacturing enterprise in the EU, producing machine equipment for numerous partners worldwide.
      *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
      2020 — ongoing
      team
      10 specialists

      The team involved in the project

      industry
      Manufacturing
      solution
      Industrial IoT platform
      technologies
      C#, ASP. NET MVC, .NET Core 3, .Net 5, Web API, JavaScript, JQuery, TypeScript, Azure

      4 x Full-stack developers

      2 x QA engineers

      1 x UI/UX designer

      1 x Project manager

      1 x Solution architect

      1 x DevOps engineer

      hire the team

      Challenge

      Create an IoT monitoring platform that would manage all processes at the enterprise by gathering, analyzing, storing, and processing data.

      Related objectives

      Build a smart factory application
      Implement ML algorithms
      Increase the client’s production efficiency

      Solution & functionality

      The team built an IoT monitoring platform with several operational modules that help oversee processes and operations within the client’s enterprise.

      Predictive maintenance module

      Every piece of equipment is equipped with various sensors. They continuously monitor and transmit real-time data regarding the machinery’s temperature and vibration levels. The application sends notifications to operators on any unusual temperature rise, preventing automatic shutdowns. Afterwards, algorithms scrutinize the maintenance history and suggest an out-of-schedule maintenance check.

      Environmental control module

      Sensors identify diverse factors that influence the well-being and security of the factory workforce. Among them are humidity, temperature, and noise intensity. Additionally, the application supervises air quality and emission levels in industrial spaces. If these standards go over the limit, the system alerts operators and provides algorithms to address the issue.

      Manufacturing effectiveness module

      The platform gathers data from sensors for every production division and machine and analyzes their OEE (overall equipment effectiveness). Users can see what critical factors influence OEE levels, detect potential issues, and resolve them. What is crucial is that the application collects performance metrics over a specified timeframe and helps operators see how key indicators progress.

      Quality assurance module

      With the IIoT platform, users can monitor the quantity and quality of components produced during one shift or any specified period. Also, they can have access to comprehensive details about each item, like production line, the count of rejected pieces, statistics, and more. Leveraging this information, system operators can spot production trends and the reasons behind elevated scrap rates and implement suitable measures to enhance the quality performance metrics.

      Results and business value

      Timspark has leveraged IoT for manufacturing and delivered a web application that monitors all the production processes, thus enhancing efficiency and mitigating risks. Also, our team provided maintenance and support services for the IIot platform they built. The client is eager to introduce new features and scale IoT for enterprise by applying it to other facilities.

      Higher productivity

      Streamlined processes and management

      Related cases

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      We appreciate your interest in Timspark

        How to Use Computer Vision in Agriculture for Detecting Diseased Banana Leaves

        Computer Vision in Agriculture

        How to Use Computer Vision in Agriculture for Detecting Diseased Banana Leaves

        Our team developed a computer vision software that checks banana seedling leaves for damage all on its own. We also use advanced analytics to help our client reduce crop loss probability.

        #AI

        #WebDevelopment

        #ComputerVision

        Client*

        Top banana seeding supplier in the world

        *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

        4 months

        team

        5 specialists

        The team involved in the project

        industry

        Agriculture

        solution

        Computer vision software for plant disease detection

        technologies

        Python, Pandas, Numpy, Pytorch, Streamlit, Opencv

        1 x Lead Data Scientist

        1 x Data Scientist

        1 x Data Engineer

        1 x Project Manager

        1 x QA

        HIRE THE TEAM

        Challenge

        The client was concerned about the possibility of using computer vision in agriculture to detect plant disease and prevent crop loss automatically. On the tech side of things, our team faced a scarcity of data available to train AI-based computer vision solutions.

        Solution & functionality

        We came up with a solution to place cameras in the greenhouse, putting them up high and to the side. These cameras take periodic snapshots of banana seedlings. The client can adjust the frequency of these snapshots.

        Deep learning for object detection and classification

        Timspark made a computer vision software module that grabs pictures of the leaves spotted by the camera. These pictures later get sent to the deep learning classifier model, which has a closer look at images and tells if a banana leaf is healthy or damaged. It can even figure out what kind of damage it is.

        Plant condition reports and analytics

        Our computer vision software checks out the uploaded pictures and generates a PDF report with the results. It further gives recommendations on what to do next based on the analyzed data.

        Results and business value

        Timspark didn’t just create and tweak the classifier model; we did it thoroughly, making sure it fits the project’s special needs and industry standards. The client is satisfied with the outcome and still partners with us on more projects to facilitate computer vision in agriculture.

        Benefits for client

        Thanks to computer vision services, the customer has successfully put this technology to work and significantly cut down on banana seedling losses.

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          AI-based Web Solution: X2 Sales Rise for Custom Sports Clubs

          AI-based Web Solution for Maximizing Sales

          AI-based Web Solution: X2 Sales Rise for Custom Sports Clubs

          The team developed an AI-based solution to automate the analysis of golf players’ positions and strokes, to boost sales for custom golf clubs manufacturing business.

          #AI

          #WebDevelopment

          #ComputerVision

          Client*

          The client is a major manufacturer of custom golf clubs.

          *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

          3 months

          team

          6 specialists

          Team involved in the project

          industry

          Sport and Entertainment

          solution

          AI-powered analyzer for golf players

          technologies

          TensorFlow, Keras, Python, OpenCV, Mediapipe, LabelMe, MLFlow, NumPy, Colab, Matplotlib

          1 x Lead Data Scientist

          2 x Data Scientists

          1 x Data Engineer

          1 x Project Manager

          1 x QA

          HIRE THE TEAM

          Challenge

          Develop an AI-based product for analyzing golf players’ positions and strokes in order to design individually fitting golf clubs. The client was already using computer analysis software for analyzing the players’ movements and consistency of strokes and wanted to fully automate the process.

          Solution & functionality

          The team created an AI-powered solution capable of recognizing the golf club in a player’s hand and correctly estimating the angles of his joints (the posture).

          Detection of player’s positions and golf club

          Our team developed a model that successfully identifies the position of a person’s body, including their arms and legs, as well as the position of the golf club, using computer vision technology.

          Robust pose estimation

          Our developers enhanced the model to gather additional information by measuring the angles of specified body joints through computer vision technology. Data is captured either via a mobile device camera or via a pre-installed kiosk with a camera.

          Collection, analysis and processing of advanced metrics

          Timspark’s specialists developed and fine-tuned the classifier model to meet the project’s unique requirements. The model analyzes received images, determines the average class among all attributes of captured objects, and subsequently identifies the target audience for the advertisement.

          Results and business value

          The product was developed as an MVP. All the intended functionality operates with the help of computer vision and artificial intelligence technologies.

          Benefits for client

          The client remained highly content with the quality and speed of the team’s work. By successfully implementing the technology into their sales process, the client doubled their sales.

          Related cases

          see all case studies

          We appreciate your interest in Timspark

            Let’s build something great together

              Let’s build something great together

                Let’s build something great together

                  Let’s build something great together

                    Let’s build something great together

                      Let’s build something great together