Diatech Diabetes develops SMARTFUSION™, an algorithm for insulin pumps that detects various infusion set failures beyond simple occlusions. This technology analyzes infusion mechanism data using machine learning to identify leaks, kinks, and dislodgements in real time. By accurately characterizing infusion performance, the system helps users maintain better glycemic control by predicting site issues before blood sugar changes occur.
Funding
$430K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Insulin pump users frequently experience infusion set failures, including leaks, blockages, dislodgements, and damaged insertion sites, which can lead to inconsistent insulin delivery and poor glycemic control. Current insulin pumps primarily detect complete occlusions, often failing to identify partial failures or absorption issues until blood sugar levels are significantly affected. Delayed detection of these failures can result in hyperglycemia, increasing the risk of complications and hospitalizations.
Solution
Diatech Diabetes offers SmartFusion™, a software-based solution designed to improve the detection of insulin infusion set failures. SmartFusion™ analyzes data from the insulin pump's infusion mechanism using real-time data analysis and machine learning algorithms to identify various types of failures, such as leaks, kinks, partial occlusions, and issues related to damaged sites or dislodgements. By detecting these failures early, SmartFusion™ enables users to take corrective action before significant changes in blood sugar occur. The system integrates with existing insulin pumps and can be accessed via a smartphone or smartwatch, providing timely alerts and insights to optimize insulin delivery and maintain stable glucose levels.
Target Audience
The primary target audience includes individuals with diabetes who use insulin pumps and seek to improve the reliability and effectiveness of their insulin delivery, as well as healthcare providers looking to enhance patient outcomes through improved monitoring and management of insulin pump therapy.
Features
- Real-time analysis of insulin pump data to detect infusion set failures beyond complete occlusions.
- Machine learning algorithms to characterize and predict potential site failures.
- Smart Alerts™ to notify users of detected infusion issues.
- Site Map™ feature to track problematic infusion sites and optimize site selection.
- Bolus tracker integration with existing apps to monitor bolus delivery and identify potential issues.
- Compatibility with insulin pumps, smartphones (iOS and Android), and smartwatches.
- Detection of leaks, kinks/partial occlusions, infusion into damaged sites, and dislodgements.