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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

Related cases

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    Odoo Implementation for ERP Processes with a 16% Surge in Online Sales

    Odoo ERP for eCommerce

    Odoo ERP Implementation with a 16% Surge in Online Sales

    Timspark offered Odoo ERP services to replace the client’s outdated, bespoke ERP system, execute CRM integration, and develop an AI chatbot to foster the company’s digital transformation.

    #eCommerce

    #Odoo

    #ERP

    Client*

    Our client is a US-based industrial equipment and components retailer with 50 employees. It is strategically positioning itself for business expansion within the fiercely competitive realm of e-commerce.

    *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

    7 months

    team

    13 specialists

    The team involved in the project

    industry

    E-commerce

    solution

    Odoo ERP for Ecommerce

    technologies

    Odoo, JavaScript, React, Python, Google Ads API, PostgreSQL

    3 x Business analysts

    1 x Project manager

    1 x Back-end developer

    1 x Front-end developer

    2 x Odoo developers

    2 x QA engineers

    hire the team

    Challenge

    Our team aimed to solve the client’s outdated ERP issues like reporting problems, slow performance, and data silos with highly flexible and customizable software.

    Objectives

    Improve retail pipeline management

    Build smarter analytics and reporting

    Ensure advanced integration capabilities

    Solution & functionality

    We chose Odoo implementation to replace the client’s outdated ERP, leveraging its standard functions to meet 99% of their needs and adding custom features as required.

    Enterprise Resource Planning

    Our team’s Odoo ERP implementation automated critical business processes like inventory management, order processing, and financial reporting. We configured default features and added auxiliary functionality for online sales and POS terminals. Additionally, we implemented flexible pricing and compliant tax management and financial reporting modules.

    AI-powered Chatbot

    Using our AI expertise, we created an intelligent chatbot with advanced algorithms to automate rule-based tasks, reduce staff workload, and free up resources. Seamlessly integrated with the client’s website, resource planning, and customer management systems, our chatbot employs advanced AI to read user intentions and add human touch to automated customer support.

    Customer Relationship Management

    After the Odoo ERP implementation, we customized the CRM to integrate seamlessly and expand ERP capabilities. This centralized hub manages customer data, leads, and sales activities, automating processes like order generation and inventory updates. The CRM also streamlines customer management by centralizing contact details, purchase history, and communication logs for easy access and updates by sales reps.

    On top of that, we automated the e-commerce pipeline from lead capture to delivery. Pre-sale activities involve lead assignment and tracking, with additional features: 

    • Lead scoring
    • Automated emails
    • Reminders for managing quotes and feedback.

    Using Odoo-based CRM, sales reps easily handle customer support inquiries, service requests, and follow-ups to enhance customer loyalty.

    Paid Search Advertising & Branding

    We offered expert guidance on optimizing digital sales through Google Ads to drive increased visibility and targeted traffic. Our specialists also led a brand transformation, which resulted in heightened brand awareness and an influx of new customers to the client’s website.

    QR Codes

    As a tech-driven ERP Odoo implementation partner, Timspark improved inventory management using QR codes and enabled sales reps to track and label items accurately and streamline point-of-sale transactions. The unique QR codes printed on customer items facilitate quick and accurate identification, checkout, and access to discounts and promotions.

    Results and business value

    Timspark’s Odoo implementation liberates the client from outdated systems and fosters business growth. Our solution strategically enhances operational efficiency and profitability across all marketing activities.

    Benefits for client

    Timspark has successfully implemented a comprehensive Odoo system that replaces outdated ERP and boosts growth by integrating a custom CRM, QR codes for inventory, an AI chatbot, and optimized Google Ads. 

    38%

    more website visitors

    16%

    increase in online sales

    Related cases

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    We appreciate your interest in Timspark

      Call Center Monitoring Software That Automates 27% of Client Requests

      IoT PLATFORM

      Call Center Monitoring Software That Automates 27% of Client Requests

      Timspark crafted a tailored AI-powered call center monitoring software for a major telecom enterprise, seamlessly linking operators and customers within an integrated platform.

      #Telecom

      #AI

      #Python

      Client*

      The client operates as an internet provider and offers a diverse array of internet services to customers situated across the European Union.

      *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

      13 months

      team

      20 specialists

      The team involved in the project

      industry

      Telecommunications

      solution

      AI call center software

      technologies

      React, Redux, Typescript, .Net Core, C#, .Net WebAPI, FluentMigrations, RabbitMQ, MassTransit, Windows, Linux, Python

      1 x Project manager

      2 x Business analysts

      2 x Front-end developers

      4 x Back-end developers

      3 x Full-stack developers

      1 x DevOps engineer

      1 x VoIP engineer

      2 x QA engineers

      1 x UI/UX designer

      1 x Solutions architect

      2 x ML engineers

      hire the team

      Challenge

      Our ISP client aimed to enhance customer satisfaction and support by developing custom call center software for improved scheduling. They required seamless integration with their CRM and task-tracking system to help operators quickly access client information and automate issue resolution.

      Major objective

      Empower customer service interactions with AI

      Related objectives

      Automate request processing

      Build intuitive interface

      Ensure security compliance

      Smoothly integrate infrastructure

      Solution & functionality

      Timspark created advanced AI-driven call center monitoring software to direct client calls to appropriate operators through AI-powered voice recognition and request identification. Our solution utilizes a .Net-based microservices architecture for robust back-end implementation, including SIP and soft PBX servers. 

      The front end, developed with React and Electron, offers a user-friendly desktop application interface. The system efficiently handles call routing, adhering to predefined regulations, and maintains GDPR compliance throughout its interconnected components to keep operator communication and responses swift.

      AI-driven voice recognition and call routing

      Our team integrated artificial intelligence into contact center operations to automate client support processes. Interactive voice response, call routing, and smart voice recognition capabilities are now managed by AI. Using modern natural language processing techniques and BERT models, the system engages callers, identifies their requests, and performs tasks like changing tariffs or ordering additional services. 

      For complex inquiries, the AI routes call to the appropriate operator or department to provide specialized assistance. Intelligent routing optimizes customer interactions and allows operators to focus on exceptional service delivery and the resolution of complex issues.

      Client request cards

      Our AI call center software automated card creation and enables operators to access and update client details quickly. When a call is received, the system identifies the client and presents their card with personal information and interaction history. Each card includes an action plan tailored to the request to guide operators through the resolution process with step-by-step instructions.

      Call recordings and statistics

      The call center monitoring software software securely stores call recordings and provides customizable statistics and tools for analytics. Supervisors can monitor performance metrics, track interactions processed by AI, and optimize team efficiency. This part of call center software features enhances issue resolution and facilitates call operator training through comprehensive call logging and recording.

      Automated task setting

      Integrated with the company’s task management and CRM systems, our call center monitoring software enables efficient task creation and assignment. Operators can generate tasks from client interactions, transferring all relevant details automatically. Task progress can be tracked within the system to provide supervisors with insights into workload distribution and resolution status.

      Results and business value

      Our AI call center software boosts task management, optimizes workloads, and streamlines customer support. Its user-friendly interface enables quick responses, while its flexibility promotes collaboration across departments beyond telecom services.

      Benefits for client

      Timspark’s AI call center software is a significant improvement that has reduced the workload for staff to keep focus on more complex customer issues.

      41%

      faster customer success

      27%

      of requests automated

      Related cases

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      We appreciate your interest in Timspark

        How Computer Vision in Agriculture Helps Track Bee Population and Health

        Computer Vision in Agriculture

        How Computer Vision in Agriculture Helps Track Bee Population and Health

        Timspark used AI to make mobile computer vision software to track the beehive life. We also predict how the bee population will grow and how healthy it is overall.

        #AI

        #MobileDevelopment

        #ComputerVision

        Client*

        It was Timspark’s internal project. We were eager to independently grow expertise in Machine Learning and built an ML-powered app with prediction functionality for agriculture.

        Project in numbers

        duration

        6 months

        team

        4 specialists

        Team involved in the project

        industry

        Agriculture

        solution

        Mobile computer vision software for beehive tracking

        technologies

        Python, PyTorch, YOLOv3, Google Cloud Platform, DigitalOcean, Swift 5

        1 x Lead Data Scientist

        1 x iOS Developer

        1 x Data Engineer

        1 x Project Manager

        1 x QA

        HIRE THE TEAM

        Challenge

        Our team did it all for this project – from planning the product’s architecture to labeling the data and training the neural network. This hands-on approach to computer vision services helped us become experts in the field and led to a fully working product as the outcome. We tackled a set of machine learning challenges and industry-related standards of using computer vision in agriculture, too.

        Solution & functionality

        Our ready-made solution is a mobile app that uses computer vision in agriculture to count bees in the hive accurately. It also creates user-friendly charts and graphs, making it easy to monitor the hive’s growth and health.

        Object detection for beehive population count and health tracking

        We have harnessed object recognition technology, so now it is possible to scan and instantly figure out how many bees are in the hive. Also, the solution helps distinguish the regular bees from the queen bees.

        A modern user-friendly mobile app

        Our team built mobile computer vision software from the ground up with an easy-to-use interface. We also added a feature where a client can get automatic reports about the hive’s status using the information uploaded.

        Results and business value

        We’ve successfully crafted computer vision solutions for iOS, designed to assist beekeepers in caring for their beehives more effectively.

        Benefits 

        Our app offers a major time-saving advantage. With traditional manual inspections taking up to an hour per beehive, our computer vision services help beekeepers inspect a hive in just 10 minutes or even less.

        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.

          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