# Data‐Driven Feedback Identifies Focused Ultrasound Exposure Regimens for Improved Nanotheranostic Targeting of the Brain

**Authors:** Hohyun Lee, Victor Menezes, Shiqin Zeng, Chulyong Kim, Cynthia M. Baseman, Jae Hyun Kim, Samhita Padmanabhan, Pranav Premdas, Naima Djeddar, Anton Bryksin, Nikhil Pandey, Pavlos Anastasiadis, Anthony J. Kim, Tobey J. MacDonald, Chetan Bettegowda, Graeme F. Woodworth, Felix J. Herrmann, Costas Arvanitis

PMC · DOI: 10.1002/advs.202517834 · Advanced Science · 2026-01-07

## TL;DR

This paper shows how machine learning can improve focused ultrasound to safely open the blood-brain barrier for better drug delivery and disease detection.

## Contribution

A machine learning-augmented closed-loop controller expands the safe treatment window for BBB opening during MB-FUS.

## Key findings

- ML-augmented control improves BBB permeability while preventing tissue damage.
- The system successfully scaled from mice to rats and healthy to diseased brains.
- It enhanced nanoparticle delivery and detection of tumor DNA in blood.

## Abstract

The blood‐brain barrier (BBB) renders the delivery of nanomedicine in the brain ineffective and the detection of circulating disease‐related DNA from the brain unreliable. Here, we demonstrate that microbubble‐enhanced focused ultrasound (MB‐FUS) mediated BBB opening, supported by large‐data models predict sonication regimens for safe and effective BBB opening. Importantly, a closed‐loop MB‐FUS controller augmented by machine learning (ML‐CL) expands the treatment window, as compared to conventional controllers, by persistently and proactively maximizing the BBB permeability while preventing tissue damage. By successfully scaling up from mice to rats and from healthy to diseased brains (glioma), ML‐CL rendered the BBB permeable to large nanoparticles and markedly improved the release and detection of reporter gene DNA from tumors in blood. Together, our findings reveal the potential of data‐driven feedback to support the development of next‐generation AI‐powered ultrasound systems for safe, robust, and efficient nanotheranostic targeting and treatment of brain diseases.

Machine learning models predict in real time the onset of harmful microbubble collapse during microbubble‐enhanced focused ultrasound (MB‐FUS) and enable dynamic adjustment of sonication to prevent cavitation‐induced damage. This predictive control expands the safe operating window for bloodbrain barrier opening, enhancing nanoparticle delivery and tumor biomarker detection in blood.

## Linked entities

- **Diseases:** glioma (MONDO:0021042)
- **Species:** Mus musculus (taxon 10090), Rattus norvegicus (taxon 10116)

## Full-text entities

- **Diseases:** glioma (MESH:D005910), tumors (MESH:D009369), brain diseases (MESH:D001927)
- **Chemicals:** ML-CL (-)
- **Species:** Rattus norvegicus (brown rat, species) [taxon 10116], Mus musculus (house mouse, species) [taxon 10090]

## Full text

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## Figures

7 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12955872/full.md

## References

99 references — full list in the complete paper: https://tomesphere.com/paper/PMC12955872/full.md

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Source: https://tomesphere.com/paper/PMC12955872