The BIIP committee members have been performing scientific reviews for the 2018 IEEE International Symposium for Biomedical Imaging (IEEE-ISBI) over the past few months. IEEE-ISBI is a leading scientific conference dedicated to mathematical, algorithmic, and computational aspects of biological and biomedical imaging, and EMBS has been a technical co-sponsor of ISBI since 2002. 

The Leona M. and Harry B. Helmsley Charitable Trust Type 1 Diabetes (T1D) Program has launched the Future Technologies Initiative (FTI). The FTI is a new Request for Proposals with the goal of accelerating the development of novel, early-stage, disruptive technologies that have the potential to dramatically ease the burden of managing T1D, which we believe will lead to better outcomes for people living with T1D. 

Proposals should focus on one of the following two tracks:

 

(1) Novel insulin and glucagon delivery technologies

(2) Novel sensing technologies

 

We are seeking projects that are high-risk, high-impact, and have long-term vision to significantly improve glucose control while reducing the burden of living with T1D. We encourage bold, out-of-the-box thinking, novel approaches, and partnerships that incorporate technologies and expertise outside of the diabetes field.

 

Helmsley will consider supporting one or more projects up to 24 months and each project budget can be up to $750,000 USD in total.

Each year this unique competition for student innovations in primary-care technology has awarded major funding prizes to the most promising of the many projects submitted. This year's First-Place Prize of $100,000 goes to Robert Mannino and his team at Georgia Tech for their innovative work to develop Non-invasive Technology for Patients' Self-Management of Anemia. 

Second-Place Prize of $50,000 goes to Charles Rabolli and his team at Rutgers University for a Rapid Point-of-Care Diagnostic to Discriminate Viral From Bacterial Infections.

In addition, each of the five Finalists from the large field of entrants in this competition received Awards of $5,000 to further their work and careers. All five Finalists and the full titles of their projects are listed at the end of this announcement. With these results, the portfolio of student-led primary-care projects supported by Prize funds over its nineyear history expands to eighty-five. The objective of all these projects has been to improve primary-care delivery at the frontlines of medicine and to advance the careers of engineering students working on challenges in that domain. Support from Prize Funding has also helped teams advance their work toward commercialization and dissemination.

The Five Finalists and Their Projects:

• Robert Mannino, Georgia Tech; Noninvasive Inexpensive Smartphone App for Patient SelfManagementof Anemia (First Place)
• Charles Rabolli, Rutgers University; Point-of-Care Diagnostic for the Detection of Viral Versus Bacterial Infection (Second Place)
• Anarup Ganguli, University of Illinois at Urbana-Champaign; Molecular Testing on Your USB Stick
• Chris Idelson, University of Texas at Austin; Novel System to Mitigate Intrauterine Device Expulsion, with Focus During the Postpartum Period
• Ruchi Patel, The Cooper Union; QuickStitch: The Single squeeze Stitcher

Founded by successful entrepreneurs Martine Rothblatt and Dean Kamen, the Advanced Regenerative Manufacturing Institute (ARMI), is a multi-industry, multi-university nonprofit consortium with some US$300 million committed to its work. ARMI's mission is to develop practical, viable methods for producing everything from tissues to organs-lab-grown skin for grafts, replacement blood vessels, muscle, and even kidneys or livers, created through techniques such as three-dimensional (3-D) printing-at a scale sufficient to meet vast and growing medical needs. The key motivation that led to ARMI's creation, Kamen says, was bringing better engineering resources to research groups that didn't have easy access to such tools. "We very quickly realized we could use some of the technologies we had been developing for decades" at companies such as his. These technologies "are in high-volume production for things like dialysis equipment and drug delivery and sensing systems. And we quickly were able to take one of the laboratory-scale projects and demonstrate that we could get much better outcomes much more quickly with some of the engineering tools we have than they could in a bio lab." 

First, an efficient forward model is proposed, coupling the Mitchell-Schaeffer transmembrane potential model with a current dipole formulation. Then we estimate the main parameters of the cardiac model: activation onset location and tissue conductivity. A large patient-specific database of simulated BSPM is generated, from which specific features are extracted to train a machine learning algorithm. The activation onset location is computed from a Kernel Ridge Regression and a second regression calibrates the global ventricular conductivity. 

The evaluation of the results is done both on a benchmark dataset of a patient with premature ventricular contraction (PVC) and on 5 non-ischaemic implanted cardiac resynchonisation therapy (CRT) patients with a total of 21 different pacing conditions.  Good personalisation results were found in terms of the activation onset location for the PVC (mean distance error, MDE=20.3mm), for the pacing sites (MDE=21.7mm) and for the CRT patients (MDE=24.6mm). We tested the predictive power of the personalized model for biventricular pacing and showed that we could predict the new electrical activity patterns with a good accuracy in terms of BSPM signals. This is an encouraging first step towards a non-invasive pre-operative prediction of the response to different pacing conditions to assist clinicians for CRT patient selection and therapy planning.

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