R. Rivkah Isseroff

This paper develops a wireless bioelectronic actuator for wound healing in pigs, showing reduced M1/M2 macrophage ratio and increased re-epithelialization in treated wounds.

This paper develops a deep reinforcement learning framework for real-time wound healing treatment optimization, showing 17.71% acceleration in in silico experiments and 40% in vivo.

This paper develops a remote-controlled wireless bioelectronic system for delivering fluoxetine to promote wound healing in pigs, showing improved re-epithelialization and reduced inflammation.

The paper presents a wearable imaging system for automated wound monitoring in pigs, validated to capture and analyze wound progression using machine learning.

This paper introduces a wearable bioelectronic bandage that delivers fluoxetine on-demand to wounds, reducing inflammation and accelerating healing in mice.

This study uses deep learning to quantify innervation changes during wound healing in mice, finding a strong correlation between nerve fiber density and re-epithelization.

This paper develops a deep learning pipeline for automatic wound detection and size estimation in lab mice images, showing high fidelity results with minimal human intervention.