Multiple Instance Learning-Based Prediction of Blood-Brain Barrier Opening Outcomes Induced by Focused Ultrasound.

Journal: IEEE transactions on bio-medical engineering

PMID: 40030539

Abstract

OBJECTIVE: Targeted blood-brain barrier (BBB) opening using focused ultrasound (FUS) and micro/nanobubbles is a promising method for brain drug delivery. This study aims to explore the feasibility of multiple instance learning (MIL) in accurate and fast prediction of FUS BBB opening outcomes.

Authors

Haixin Dai
Wenjing Li

Department of Allergy, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China.
Qian Wang

Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China.
Bingbing Cheng

Keywords

Animals Blood-Brain Barrier Contrast Media Drug Delivery Systems Machine Learning Mice Microbubbles Multiple-Instance Learning Algorithms Phospholipids Signal Processing, Computer-Assisted Sulfur Hexafluoride Ultrasonography

External Resources

View on PubMed Access via DOI PubMed (40030539)

Multiple Instance Learning-Based Prediction of Blood-Brain Barrier Opening Outcomes Induced by Focused Ultrasound.

Abstract

Authors

Keywords

External Resources

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