Feasibility of Awake In-Office Transnasal Double Balloon Dilation of the Pharyngoesophageal Segment 🚀 Double balloon dilation of the PES is feasible in unsedated patients in the office setting: bit.ly/41IUYNJ
#BiomedicalInformatics #ArtificialIntelligence #AI #AISky #MLSky #MedSky #AcademicSky
A paper in The Laryngoscope presents the first successful use of a cervical plexus block for persistent globus pharyngeus, offering a new clinical pathway for chronic throat sensations.
bit.ly/4lOpOOh
#ArtificialIntelligence #BiomedicalInformatics #MedSky #MedAI #AISky #AcademicSky #MLSky
New Review 💬 Beyond convolutions and supervised learning with transformers and representation learning for retinal image analysis: www.sciencedirect.com/science/arti...
#ArtificialIntelligence #BiomedicalInformatics #MedSky #MedAI #AISky #AcademicSky #MLSky #OpenScience
ApplyPolygenicScore functionality. Application of ApplyPolygenicScore functions demonstrated in a case study of 1071 individuals from the TCGA database, diagnosed with bladder (BLCA), liver (LIHC), or uterine (UCEC) cancer. A. Recommended workflow when implementing functions provided by ApplyPolygenicScore . A set of preprocessing functions convert polygenic risk score model (PGM) weight files into BED-formatted genomic coordinate files for suggested use in filtering VCF genotype data to desired coordinates. PGM application functions facilitate genetic data importation and weighted sum computation. Visualization functions provide summary information on computed PGSs and phenotype data. Solid arrows indicate required inputs and dotted arrows indicate optional inputs. B. BMI PGS densities, cohort-wide and by categorical phenotypes, computed in the case study cohort and automatically plotted by the create.pgs.density.plot function. C. Correlations of PGSs from (B) with continuous phenotypes automatically plotted by the create.pgs.with.continuous.phenotype.plot function. D. Receiver-operator curves plotted by the analyze.pgs.binary.predictiveness function depicting the performance of the PGSs from (B) to predict obesity status as a sole predictor (top) and with covariates age at diagnosis, sex, and the first 10 principal components of genetic ancestry (bottom). Positive obesity status is defined as BMI ≥ 30. E. From top to bottom: percentile rank of PGSs from (B) for each individual in ascending order, decile and quartile covariate bars, categorical phenotype covariate bars, and continuous phenotype heatmaps. Nicole Zeltser; Rachel M.A. Dang; Rupert Hugh-White; Daniel Knight; Jaron Arbet; Paul C. Boutros
🚀 ApplyPolygenicScore encourages the research community to extend the findings of the statistical genetics niche, facilitating broader use of PGSs and subsequent novel discovery: bit.ly/41jRDnS
#ArtificialIntelligence #BiomedicalInformatics #MLSky #AISky #MedSky #AcademicSky #SciSky #OpenScience
New Review in @springernature.com 💬 Lithium offers a unique paradigm for understanding and potentially treating age-related decline in multiple organ systems at subclinical dosage and concentration.
bit.ly/4siRZqL
#ArtificialIntelligence #BiomedicalInformatics #AISky #MLSky #MedSky #MedAI
New Article: Bridge2AI researchers discover suppression of poor-quality ML predictions through an auditor model shows promise in improving collaborative human-AI performance and fairness
🔗 academic.oup.com/jamia/articl...
#ArtificialIntelligence #BiomedicalInformatics #AISky #MLSky #MedSky #MedAI
New Review 💬 Generative Artificial Intelligence Methodology Reporting in Otolaryngology
🔗 onlinelibrary.wiley.com/doi/10.1002/...
#BiomedicalInformatics #MedSky #MedAI #AISky #AI #ML #GenerativeAI #ChatGPT #GoogleGemini #Claude #ArtificialIntelligence #GenAI #LLMs
Bridge2AI March Events: Tuesday, March 17th (12:00 PM PT/3:00 PM ET): Bridge2AI Discussion Forum on Emerging ELSI Issues with Dr. Nicholas Evans: Conjoint Analysis of Perspectives on Ethical Tradeoffs in Data Generation Project Thursday, March 19th (12:00 PM PT/3:00 PM ET): Bridge2AI TRM Novel AI Technology Module Lecture with Dr. William Speier: Multimodal Deep Learning Models for Thyroid Cancer Risk Stratification
Join us for our upcoming seminars in #ArtificialIntelligence 🚀
1️⃣Conjoint Analysis of Perspectives on Ethical Tradeoffs in Data Generation Project
2️⃣Multimodal Deep Learning Models for Thyroid Cancer Risk Stratification
🔗 bridge2ai.org/events/
#BiomedicalInformatics #MedSky #MedAI #AISky #AIML
Catalyzing Health AI by Fixing Payment Systems
"In this article, we examine the reimbursement landscape for health #AI, focusing first on tools that fit existing regulatory pathways, outlining payment barriers and proposing policy reforms."
🔗 pubmed.ncbi.nlm.nih.gov/41695240/
#MedSky #MedAI
New Publication: Quantum Machine Learning and Data Re-Uploading: Evaluation on Benchmark and Laboratory Medicine Data Sets 🚀
🔗https://pubmed.ncbi.nlm.nih.gov/41728802/
#ArtificialIntelligence #BiomedicalInformatics #MedSky #AISky #MedAI #QML #AIML #QuantumMachineLearning
What is #humanintheloop? It's February's Term of the month! 💡
Watch the recording of this month's Discussion Forum on Emerging #ELSI Issues where Dr. Susannah Rose, PhD discussed the ethical issues of #AI systems for healthcare delivery: youtu.be/eH1pPCz4lyg
#biomedicalinformatics #MedSky #AISky
#YOLO isn't just a motto, it's a model! 🚀 Explore how You Only Look Once was used to develop a #deeplearning object detection system for identifying vocal fold polyps in stroboscopic video frames: onlinelibrary.wiley.com/doi/10.1002/...
#ArtificialIntelligence #BiomedicalInformatics #MedSky #MedAI
Want to learn more about the Bridge2AI program and our data generation projects addressing grand challenges? Visit: bridge2ai.org 🚀
#ArtificialIntelligence #BiomedicalInformatics #MedSky #AISky #MedAI #AI #ML
The AIREADI dataset uses data from participants with and without T2DM, collected via methods like surveys and clinical measurements at 3 sites. The data is hosted in Fairhub and accessible under controlled access.
🔗 aireadi.org/dataset
#ArtificialIntelligence #BiomedicalInformatics #MedSky #MedAI
The Voice dataset uses an app to collect acoustic tasks, surveys, and questionnaires from participants at 5 North American sites. The data is hosted in PhysioNet with registered and controlled access to preserve voice data.
🔗 b2ai-voice.org/bridge2ai-vo...
#BiomedicalInformatics #MedSky #MedAI
The CM4AI dataset uses mapping techniques to generate a library of large-scale cell maps. The data is stored in RO-Crate packaging and hosted in UVA Dataverse.
🔗 cm4ai.org/data-releases/
#ArtificialIntelligence #BiomedicalInformatics #MedSky #AISky #MedAI #AI #ML
The CHoRUS dataset uses data collected from patient admissions to ICUs across 14 hospitals in the U.S. The data is hosted in MGB Azure with access managed through registration. 🔗 chorus4ai.org/dataset/
#ArtificialIntelligence #BiomedicalInformatics #MedSky #AISky #MedAI #AI #ML
This week is #LoveDataWeek, an international celebration of data every year during the week of Valentine’s day. This year’s theme is “Where’s the Data?”, focused on data’s journey from collection through storage and preservation. Today, we're sharing the journey of our 4 flagship datasets.🧵
New publication in Wiley Laryngoscope: Exploring Real-Time Tracking of Vocal Fold Polyps in Video-Stroboscopy Using #DeepLearning 💡 onlinelibrary.wiley.com/doi/10.1002/...
#AI #ArtificialIntelligence #BiomedicalInformatics #MedSky #MedAI #MLSky #VoiceAI
Review by Wu Y, Lee CS, and Lee AY: Beyond convolutions and supervised learning with transformers and representation learning for retinal image analysis 🔗 pubmed.ncbi.nlm.nih.gov/41352580/
#ai #ml #aritificialintelligence #retinalimaging #medsky #biomedicalinformatics #medtwitter
We're excited to congratulate Clara Hu on being named to Forbes 30 Under 30 Science! Clara is a Biomedical Sciences PhD candidate at UCSD and Functional Genomics researcher for Bridge2AI 🌟 bridge2ai.org/2026/01/14/c...
#biomedicalinformatics #medsky #AI #ML #artificialintelligence #CM4AI
Listen to Bridge2AI's Yael Bensoussan, MD, talk hearing health and voice biomarker research on this episode of the JAMA Otolaryngology podcast 🌟
podcasts.apple.com/us/podcast/w...
#AI#ML#Informatics#VoiceAI
Please join us on Tuesday, January 20th 2025 at 12pm-1pm PST/3pm-4pm EST for the discussion forum: “Trust Starts Upstream: Recommendations for creating ethically sourced health data repositories for AI/ML,” by Dr. Camille Nebeker. Creating ethically sourced and trustworthy health data repositories is critical for building trustworthy biomedical and behavioral research infrastructure, especially when such repositories underpin machine learning and AI systems. In Ethical sourcing in the context of health data supply chain management: a value sensitive design approach (Nebeker et al., 2025), we integrate value sensitive design (VSD) with concepts from supply chain management to operationalize ethical values across the stages of developing health data repositories. The resulting framework identifies key actors, values (e.g., traceability, security, equity), and tensions that arise during repository creation and highlights practices such as documenting data provenance, articulating expectations for data stewards, and implementing comprehensive privacy and bias mitigation strategies. During this session, Dr. Nebeker will provide an overview of this framework with a focus on developing a companion checklist that is grounded in VSD and supply chain scaffolding and can be used as actionable guidance in dataset creation. Dr. Camille Nebeker is a professor of public health with appointments in the UC San Diego Design Lab and the Herbert Wertheim School of Public Health and Human Longevity Science. In 2018, Dr. Nebeker co-founded the ReCODE Health center, which is dedicated to conducting cutting edge research to inform ethical practices in digital/AI health research – including machine learning and the use of large language models. The ReCODE Health center supports education and consultation services to guide ethical practices in technology-supported health research across diverse research sectors including traditional academic research and, increasingly, the health technology …
Join us on Tuesday, January 20th for a Bridge2AI Discussion Forum on Emerging ELSI Issues to learn about a value sensitive design approach to creating ethically sourced health data repositories for AI/ML with Dr. Camille Nebeker. 🚀 #AI#ML#VSD#supplychain#heathdata#datascience
Ethical sourcing is an ongoing, value-driven process that ensures data use aligns with responsible governance and public benefit.
A health data repository is ethically sourced when data are collected, governed, and used through transparent and accountable practices that respect contributors’ rights and expectations, mitigate risks of harm, and document data provenance across the full data lifecycle.
What is an "ethically sourced" heath data repository? 🧵👇
P.S. Registered access is required for featurized versions of the datasets and access to audio data. Email daco@b2ai-voice.org for more information about controlled access data. 🚀
For the first time, the Voice team is releasing a pediatric dataset, with 22,620 recordings (24.45 hours total) from 300 pediatric participants ranging from 2-18 years old.
Adult v3 dataset: physionet.org/content/b2ai...
Pediatric v1 dataset: physionet.org/content/b2ai...
Dashboard of Bridge2AI Voice dataset V3.0. Dataset overview: 833 adult participants, 33,041 recordings and 157.5 hours of recordings. 300 pediatric participants, 22,620 recordings and 24.45 hours of recordings. Clinical metadata: demographics, severity, treatments, and confounders. Includes charts of acoustic tasks and validated questionnaires, as well of % of cohorts in dataset and clinically validated diagnoses within cohorts.
"This release builds on previous iterations, offering researchers a robust resource for developing and validating AI models that explore voice as a biomarker for health"
The Bridge2AI Voice team just hit 2 major milestones for voice AI research: (1) they just released their largest adult dataset to date and (2) their first pediatric dataset 🧵
🔗 bridge2ai.org/bridge2ai-voice-adult-pediatric-ai-dataset
#AI#VoiceAI#Science#ArtificialIntelligence#ML#MachineLearning