Our Germany-based client, a healthcare industry leader providing RIS, PACS, digital mammography diagnostics, and digital radiography solutions, enlisted our help to leverage their legacy solution for mammography workstations. Right from the start, our team impressed our client with their ability to instantly grasp the peculiarities of the healthcare industry and use their deep technology expertise to expand the solution’s technological capabilities.
Intellias was instrumental in bringing the mammography solution to life, assisting physicians in their daily diagnostic activities. Installed on a powerful standalone workstation, the solution is a handy tool for viewing, processing, and managing PACS mammography images.
After the project was handed over to us, we started by thoroughly analyzing the legacy solution. This resulted in determining a strong need to enhance the platform’s changeability and extensibility to guarantee the smooth and timely implementation of new features. Our team’s initial effort was devoted to code refactoring and quality improvements. Intellias guaranteed a high level of quality management services – essential for a product delivery and customer satisfaction. Using our vast experience in re-engineering legacy software, we studied thousands of lines of code to locate important issues and fix them by applying the best tools and techniques.When refactoring, we also contributed to code structuring, design, and readability.
To meet the requirements of customer-facing stakeholders, we introduced a major revamp of the client’s mammography tool. Based on PACS technology and the DICOM standard, the solution not only ensures high-level integration with third-party systems but also features outstanding grayscale image quality. Since mammography images are large, we took on the challenge of optimizing platform performance by implementing several complex algorithms including image processing optimization algorithms, algorithms to improve image reading and storage performance, and pattern recognition algorithms.
Our team also contributed to implementing vital functionality for breast cancer diagnostics. To boost the chance of taking an informative mammogram, an image should be captured in the correct position. We implemented a machine learning-based algorithm for nipple markers that facilitates proper breast positioning. Accompanied by an algorithm for automatic detection of abnormal areas, the platform largely eliminates the risk of missing breast abnormalities.
To facilitate the exchange of information within medical institutions, we implemented a handy form to record image interpretations and share digital reports of the findings.
Re-engineering the platform allowed us to deliver a mammography platform of choice that increased our client’s customer base and allowed our client to scale beyond their traditional market. Code refactoring and increased system performance ensures the flawless implementation of new features and system scalability.
Complex algorithms for ultraprecise breast positioning and accurate detection of abnormal areas make it possible for medical institutions to provide exceptional breast health services with comfort, confidence, and clarity.