Congratulations to the winners of the 2018 Innovation Discovery Grants

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The Innovation Discovery Grants (IDG) program focuses on strategic scientific advancement with milestones geared towards mitigating risks for follow-on investment. While the program has existed for just four years, the thirty projects that have been awarded a combined $2 million, together have raised an additional $21 million to further their development. Roughly $12 million of this additional investment has come from private sources.  Fifteen patent applications have been filed and three have been awarded. Four start-up companies have been formed around these technologies with another five new companies in the process of forming.

The program has been a success.

The first two IDG rounds focused on supporting a broad range of technology types designed to address unmet clinical needs across the full clinical spectrum.  In 2018, the third IDG program focused on a specific type of technology: the advancement and opportunities of artificial intelligence, data science, cognitive computation, and machine learning. Each award provides up to $50,000. Over 120 applications were submitted in response to the AI-focused solicitation. They were reviewed by external representatives from industry and the venture capital community. The 22 strongest proposals were then presented and discussed in mid-April.

Throughout the initial reviews, and with the guidance of the panel review leadership, the review criteria assessed:

  • Potential impact on patient care and healthcare delivery
  • Probability of meeting the articulated milestones and achieving the endpoints described in the proposals
  • Likelihood of attracting follow-on support to further develop these cutting-edge technologies

And now, we are pleased to congratulate the winners of the 2018 Innovation Discovery Grants:

Kevin Elias, MD (BWH, Obstetrics and Gynecology) – A web-based neural network calculator for ovarian cancer screening using serum miRNA

Brandon Westover, MD, PhD (MGH, Neurology) – Artificial Intelligence Based Seizure Detection and Classification

George L. Mutter, MD (BWH, Pathology) – Augmented Digital Microscopy for diagnosis of endometrial neoplasia.

Jochen Lennerz, MD, PhD (MGH, Pathology) – Breast Cancer Scanning Initiative (BCSI): Predicting unnecessary surgeries in high-risk breast lesions

Jinsong Ouyang, PhD (MGH, Radiology) – Deep-learning Facilitated Lesion Detection in Medical Images

Jayashree Kalpathy-Cramer, PhD (MGH, Radiology) – DeepROP – a point-of-care system for diagnosis of plus disease in retinopathy of prematurity

Christian A. Webb, PhD (McLean, Psychiatry) – Development of a machine learning algorithm-guided approach to treatment selection for depressed patients.

Alexandra Golby, MD (BWH, Neurosurgery) – Machine Learning Optimized Intraoperative Multiplexed Quantitative Optical Image Guidance for Brain Tumor Surgery

Martin H. Teicher, MD, PhD (McLean, Psychiatry) – Poly-Exposure Risk Scores for Psychiatric Disorders

Phillip Jason White, PhD (BWH, Radiology) – The identification of intracranial hemorrhage using machine-learning analysis of sparse transcranial ultrasound signals

Bruno Madore, PhD (BWH, Radiology) – Ultrasound-based sensors for physiological monitoring, and how they can make MRI and PET/CT scanners work better

Peter F Dunn, MD (MGH, Anesthesia) – Using Deep Learning to Predict Next-Day Patient Discharges to Optimize Hospital Capacity Management

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