Research
AI for easier and faster dementia diagnosis
A press release highlighting our article on a multimodal deep learning model for differential diagnosis of dementia, published in Nature Medicine in June 2024, was featured on The Brink, a platform that showcases the latest news and research from Boston University. Click here for more details.
Research highlight in Nature
Our paper published in Nature Medicine in July 2024 on an AI model for differential dementia diagnosis was highlighted by the journal Nature. Click here for more details.
Podcast on AI tools for dementia
Check out our episode on the Research Renaissance podcast, where we discuss building AI tools for dementia diagnosis, hosted by the Karen Toffler Charitable Trust and released in July 2024. Click here for more details.
Briefing on our research in Nature Medicine
This research briefing, published in July 2024, accompanies our research paper, also published in July 2024, in Nature Medicine, which presents an AI model for differential diagnosis of dementia. Click here for more details.
Podcast on neurological research
Check out our episode on Newt’s World, the podcast hosted by former Speaker of the House Newt Gingrich, where we sat down in October 2023 to discuss the exciting possibilities of AI in neurological research and how these tools can be developed to improve efficiency in patient care. Click here for more details.
Clubhouse discussion on our latest work
Our research on a deep learning model that utilizes multimodal data to assess Alzheimer’s disease dementia, published in Nature Communications in June 2022, was featured in a discussion on Clubhouse. Click here for more details.
Technology for dementia diagnosis
A press release highlighting our article on a multimodal deep learning model for assessing Alzheimer’s disease dementia, published in Nature Communications in June 2022, was featured on The Brink, a platform that showcases the latest news and research from Boston University. Click here for more details.
Clinical grade machine learning tools for Alzheimer’s disease
Profile featuring our work with the Karen Toffler Charitable trust. Click here for more details.
Deep learning algorithm outperforms experts in making Alzheimer’s disease diagnosis
A press release highlighting our article on an interpretable deep learning model for assessing Alzheimer’s disease, published in Brain in June 2020, was featured on The Brink, a platform that showcases the latest news and research from Boston University. Click here for more details.
Invited talks and presentations (Since 2020)
– May 2024: “Digital nephropathology”, Renal Research Seminar, Division of Renal Medicine, Brigham & Women’s Hospital (Online).
– April 2024: “Leveraging AI for dementia clinical trials”, The Future of Digital Health, BU Questrom School of Business.
– March 2024: “AI-based tools for differential diagnosis of dementia”, Clinical Neuroscience Grand Rounds, Boston Medical Center (Online).
– February 2024: “Multimodal learning for dementia assessment”, 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP2024) (Online).
– December 2023: “Mining multimodal clinical and digital data for brain health”, Framingham Heart Study Research Conference Series (Online).
– November 2023: “Representation learning for digital pathology”, Reusable AI Tech Talks, AstraZeneca (Online).
– September 2023: “Multimodal machine learning for dementia assessment”, Cognitive Neurology Unit/Center for Noninvasive Brain Stimulation Grand Rounds, Beth Israel Deaconess Medical Center, Boston, MA (Online).
– June 2023: “Digital voice data”, Alzheimer’s Disease Data Initiative Summer Learning Series (Online).
– June 2023: “MIRA: A web-based tool for digital pathology”, NVIDIA MONAI Pathology WG Speaker Series (Online).
– May 2023: “Multimodal machine learning for dementia assessment”, CIHC Faculty Grand Rounds, University of Nebraska Medical Center, Omaha, NE (Online).
– May 2023: “Multimodal machine learning for dementia assessment”, ADRC meeting, Washington DC.
– April 2023: “Using deep learning models to augment clinical diagnosis in neurology”, AAN meeting, Boston, MA.
– November 2022: “Digital pathology for diabetic kidney disease”, ASN meeting, Orlando, FL.
– November 2022: “Pre-cancer phenotyping using transcriptomics and digital pathology”, National Cancer Institute CBIIT Data Science Seminar Series (Online).
– November 2022: “A graph-transformer for whole slide image classification”, AstraZeneca (Online).
– October 2022: “Machine learning approaches on audio recordings for dementia assessment”, ADRC meeting, Chicago, IL.
– October 2022: “Evaluation of machine learning readiness in Alzheimer’s disease data cohorts”, ADRC meeting, Chicago, IL.
– September 2022: “Multimodal deep learning for Alzheimer’s disease dementia assessment”, ECR workshop, DEMON Network (Online).
– October 2021: “Raw digital data streams and ‘handsfree’ approaches to data analysis: The promise of AI”, SMi’s Medical Wearables for Biosensors Virtual conference (Online).
– August 2021: “AI technology – the future of pathology?”, 33rd European Congress of Pathology (Online).
– April 2021: “Deep learning for Alzheimer’s disease assessment”, Massachusetts General Hospital Memory Disorders Unit & Movement Disorders Unit Conference, Boston, MA (Online).
– March 2021: “MRI data harmonization: A machine learning approach”, Alzheimer’s Disease Sequencing Project Consortium (Online).
– March 2021: “Quantification of renal fibrosis on pathology images using deep learning on local and global representations”, AI in Nephropathology Workshop, Amsterdam (Online).
– October 2020: “Detection of dementia from raw voice recordings using a recurrent neural network”, Alzheimer’s Drug Discovery Foundation, New York, NY (Online).