Publications & Patents

First author and co-author on peer-reviewed publications in npj Digital Medicine and other PubMed-indexed journals, with a focus on multimodal machine learning (video, audio, and language) for clinical measurement — and on rigorous validation of what these models can and cannot detect.

Metrics: h-index 6 · ~464 citations  |  Google Scholar · ORCID

Recent Publications

Multimodal machine learning for video based single question mental health assessment

Authors: B Grimm, P Yilmam, B Talbot, L Larsen
Journal: npj Digital Medicine, 2025

Machine learning approach for mental health assessment using video-based single question responses, combining multimodal signals for clinical screening.

Detecting Tardive Dyskinesia Using Video-Based Artificial Intelligence

Authors: AA Sterns, JW Hughes, B Grimm, L Larsen, F Ma, R Ranjan, C MacMillan, et al.
Journal: The Journal of Clinical Psychiatry 86 (3), 25m15792, 2025

Development and validation of a production AI system for screening tardive dyskinesia from clinician-labeled video data.

Cross-Dataset Evaluation of an Automated Video-Based Model for Detecting Tardive Dyskinesia Using the Clinician's Tardive Inventory: Validation Study

Authors: P Trosch, A Sterns, B Grimm, L Larsen, B Talbot, F Ma, R Ranjan, C MacMillan, JW Hughes, JH Friedman, OS Muir
Journal: JMIR Mental Health, 2026

External-dataset validation of the video-based vision-transformer tardive-dyskinesia model (TDtect) against the Clinician's Tardive Inventory — testing how the model generalizes beyond its training distribution.

Multimodal Acoustic–Linguistic Machine Learning for Postpartum Depression Screening

Journal: Women's Health Reports, 2026

Multimodal model fusing acoustic (voice) and linguistic signals to screen for postpartum depression.

PHQ-V/GAD-V: Assessments to identify signals of depression and anxiety from patient video responses

Authors: B Grimm, B Talbot, L Larsen
Journal: Applied Sciences 12 (18), 9150, 2022

Video-based assessment tools for detecting depression and anxiety signals from patient responses using multimodal machine learning.

Remote monitoring and AI for detecting tardive dyskinesia and improving patient outcomes

Authors: A Sterns, L Larsen, B Grimm, OS Muir
Journal: Gerontechnology 21, 2022

Remote monitoring systems using artificial intelligence for movement disorder detection and patient outcome improvement.

Patents

Machine learning classification of video for determination of movement disorder symptoms

Inventors: BC Grimm, LD Larsen, AA Sterns
Patent: US Patent App. 18/367,389, 2024

System and method for using machine learning to classify video data for automated detection and assessment of movement disorder symptoms.

Methods, devices, and systems for remotely controlling a communication device

Inventors: Scot L. Brooksby, Trevor Wagner, Tara Ault, Bradley Grimm, Jennifer Harris
Patent Number: US 9578585
Date: February 21, 2017
Assignee: Sorenson Communications, Inc.

Systems and methods for controlling communication systems for the hearing impaired. The invention includes a portable communication device that connects to and controls multiple communication devices, with a user interface that enables transferring calls between devices.

Earlier Research — Imaging & Connectomics

Image-analysis and large-scale scientific-visualization work from my time at the University of Utah's SCI Institute (the foundation of my image-processing background):

  • Anderson JR, Grimm B, et al. Exploring the retinal connectome. Molecular Vision, 2011.
  • Anderson JR, et al. The Viking viewer for connectomics: scalable multi-user annotation and summarization of large volume data sets. Journal of Microscopy, 2011.
  • Anderson JR, et al. Automatic mosaicking and volume assembly for high-throughput serial-section transmission electron microscopy. Journal of Neuroscience Methods, 2010.

Research Contributions

Contributed to NIH-funded research in movement disorder assessment and digital health applications. Lead and mentor data science and labeling teams, setting validation standards, safety checks, and quality gates across deployed systems.