MingDe Lin, PhD

Dr. Lin received the B.S. degree from Rensselaer Polytechnic Institute and the Ph.D. degree from Duke University, all in biomedical engineering. He is currently the Director for Clinical Research in North America for Visage Imaging and is stationed at Yale-New Haven Hospital where he oversees, coordinates, drives and directs research collaborations with high profile academic hospitals in North America to develop new solutions for diagnostic image analysis and guidance that improve clinical and operational outcomes while reducing cost of care. This includes Artificial Intelligence (AI) and Machine Learning (ML) applications in the Radiology enterprise diagnostic imaging solutions space. Dr. Lin identifies opportunities for academic-industry research partnerships, and acts as the liaison between Visage Imaging researchers and clinical collaborators to translate ideas to prototype for clinical validation, with the goal of technology transfer to product. A highlight is Ming coordinated the clinical data curation and ground-truth annotation for building a fully automatic breast density AI classifier that provides an ACR BI-RADS Atlas 5th Edition breast density category to aid radiologists in the assessment of breast tissue composition from full field digital mammography and digital breast tomosynthesis systems and drove the clinical validation with Yale radiologists and Visage developers that led to transfer to product and regulatory approvals in 21 months following IRB approval and has been in full clinical production use at Yale since April 2021. Moreover, Dr. Lin directed the study with Yale radiologists to assess the AI algorithm’s post-clinical deployment performance, and we found there was 99.35% agreement in classifying the breast density between the AI and the radiologist. This was the first FDA-cleared AI algorithm that reported having >1000 patients for validating the AI from two different clinical sites: Yale and New York University (NYU), and it was the first for a major PACS vendor to offer a self-developed, FDA-cleared AI algorithm natively into their PACS: Visage Breast Density, K201411, 510(k) clearance, January 2021, Health Canada Licensed, October 2020, Australian TGA approval, July 2020, CE Mark Cleared, May 2020.

Another effort Dr. Lin is working on is multi-institution AI research to develop robust deep learning methods for generating patient-specific virtual-high-count PET images from standard PET images, thereby saving imaging time, reducing radiation dose, and increasing scanner longevity. This work is being conducted in an NIH R01 academic-industry partnership grant where Dr. Lin is the Visage Imaging, Inc. Principal Investigator (PI), and the other partners are Yale New Haven, Massachusetts General Brigham, and University of California Davis hospitals.

Dr. Lin also is directly involved in research to develop better ways to treat patients with liver cancer using transcatheter arterial chemoembolization (TACE), and in this context, he is also the chief engineer and operations manager of the Yale Interventional Oncology Research Lab. Dr. Lin is Principal Investigator (PI) on two NIH R01 grants and the Industry PI on its renewal NIH R01 grant (three grants in total) to improve the diagnosis, treatment, and response assessment after transcatheter arterial chemoembolization for patients with liver cancer. Dr. Lin is the inventor of 3D quantification TACE therapy response tool (qEASL) and in collaboration with clinical partners, validated, and showed clinical relevance (ability to predict patient survival) that led to transfer to commercial product (FDA 510(k) cleared December 2016 - Multi-Modality Tumor Tracking (MMTT) application). Prior to Visage Imaging, Dr. Lin was the Philips research site manager and senior researcher stationed onsite at Yale where he managed the research portfolio and partnership Philips has with Yale.

Ming was inducted to the Council of Distinguished Investigators in the Academy for Radiology and Biomedical Imaging Research on October 10, 2024 for his outstanding contributions to medical imaging.