EMBL Enhances Microbiology Methods with Deep Learning
Company Size
1,000+
Region
- Europe
Country
- France
- Germany
- Italy
- Spain
- United Kingdom
Product
- DeepCycle
- Neu.ro Platform
Tech Stack
- Deep Learning
- AI
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Digital Expertise
- Innovation Output
Technology Category
- Analytics & Modeling - Computer Vision Software
- Analytics & Modeling - Machine Learning
Applicable Industries
- Healthcare & Hospitals
- Life Sciences
Applicable Functions
- Product Research & Development
Use Cases
- Computer Vision
- Predictive Quality Analytics
Services
- Data Science Services
About The Customer
Founded in 1974, EMBL is Europe’s flagship laboratory for the life sciences. It is an intergovernmental organisation with more than 80 independent research groups covering the spectrum of molecular biology. EMBL operates across six sites: Heidelberg, Barcelona, Hamburg, Grenoble, Rome and EMBL-EBI Hinxton. The organization is renowned for its research in molecular biology and has a strong focus on developing new technology and methods in the field. EMBL's research is conducted by more than 1600 employees from more than 80 nations and its budget is funded by public research monies from its member states.
The Challenge
Researchers at EMBL, Europe’s flagship laboratory for the life sciences, were looking to enhance traditional microbiology methods with Deep Learning. Their goal was to reconstruct the complex biological phenomena that underpin the life cycle of cells. This was a significant challenge due to the complexity of cell life cycles and the limitations of traditional microbiology methods. EMBL operates across six sites in Europe and has more than 80 independent research groups covering the spectrum of molecular biology. The challenge was to develop a solution that could accurately model the lifecycle of cells and provide insights into complex biological processes.
The Solution
EMBL collaborated with global AI researchers, including Neuromation Chief Research Officer, Sergey Nikolenko, and Senior AI Researcher, Alexander Rakhlin, to develop DeepCycle. DeepCycle is an AI-driven technology that models the lifecycle of cells - how they grow and divide. The solution was developed on the Neu.ro Platform, a platform designed for developing and deploying AI and Machine Learning solutions. Using approximately 2.6 million microscopy images of canine kidney cells, the novel deep learning model was able to reconstruct complex biological phenomena based solely on visual data. This innovative approach has potential applications in cancer research and other areas of healthcare.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Hospital Inventory Management
The hospital supply chain team is responsible for ensuring that the right medical supplies are readily available to clinicians when and where needed, and to do so in the most efficient manner possible. However, many of the systems and processes in use at the cancer center for supply chain management were not best suited to support these goals. Barcoding technology, a commonly used method for inventory management of medical supplies, is labor intensive, time consuming, does not provide real-time visibility into inventory levels and can be prone to error. Consequently, the lack of accurate and real-time visibility into inventory levels across multiple supply rooms in multiple hospital facilities creates additional inefficiency in the system causing over-ordering, hoarding, and wasted supplies. Other sources of waste and cost were also identified as candidates for improvement. Existing systems and processes did not provide adequate security for high-cost inventory within the hospital, which was another driver of cost. A lack of visibility into expiration dates for supplies resulted in supplies being wasted due to past expiry dates. Storage of supplies was also a key consideration given the location of the cancer center’s facilities in a dense urban setting, where space is always at a premium. In order to address the challenges outlined above, the hospital sought a solution that would provide real-time inventory information with high levels of accuracy, reduce the level of manual effort required and enable data driven decision making to ensure that the right supplies were readily available to clinicians in the right location at the right time.
Case Study
Gas Pipeline Monitoring System for Hospitals
This system integrator focuses on providing centralized gas pipeline monitoring systems for hospitals. The service they provide makes it possible for hospitals to reduce both maintenance and labor costs. Since hospitals may not have an existing network suitable for this type of system, GPRS communication provides an easy and ready-to-use solution for remote, distributed monitoring systems System Requirements - GPRS communication - Seamless connection with SCADA software - Simple, front-end control capability - Expandable I/O channels - Combine AI, DI, and DO channels
Case Study
Driving Digital Transformations for Vitro Diagnostic Medical Devices
Diagnostic devices play a vital role in helping to improve healthcare delivery. In fact, an estimated 60 percent of the world’s medical decisions are made with support from in vitrodiagnostics (IVD) solutions, such as those provided by Roche Diagnostics, an industry leader. As the demand for medical diagnostic services grows rapidly in hospitals and clinics across China, so does the market for IVD solutions. In addition, the typically high cost of these diagnostic devices means that comprehensive post-sales services are needed. Wanteed to improve three portions of thr IVD:1. Remotely monitor and manage IVD devices as fixed assets.2. Optimizing device availability with predictive maintenance.3. Recommending the best IVD solution for a customer’s needs.
Case Study
HaemoCloud Global Blood Management System
1) Deliver a connected digital product system to protect and increase the differentiated value of Haemonetics blood and plasma solutions. 2) Improve patient outcomes by increasing the efficiency of blood supply flows. 3) Navigate and satisfy a complex web of global regulatory compliance requirements. 4) Reduce costly and labor-intensive maintenance procedures.
Case Study
Harnessing real-time data to give a holistic picture of patient health
Every day, vast quantities of data are collected about patients as they pass through health service organizations—from operational data such as treatment history and medications to physiological data captured by medical devices. The insights hidden within this treasure trove of data can be used to support more personalized treatments, more accurate diagnosis and more advanced preparative care. But since the information is generated faster than most organizations can consume it, unlocking the power of this big data can be a struggle. This type of predictive approach not only improves patient care—it also helps to reduce costs, because in the healthcare industry, prevention is almost always more cost-effective than treatment. However, collecting, analyzing and presenting these data-streams in a way that clinicians can easily understand can pose a significant technical challenge.
