AI-enabled, implantable, multichannel wireless telemetry for photodynamic therapy

Woo Seok Kim, M. Ibrahim Khot, Hyun-Myung Woo, Sungcheol Hong, Dong-Hyun Baek, Thomas Maisey, Brandon Daniels, P. Louise Coletta, Byung-Jun Yoon (), David G. Jayne () and Sung Il Park ()
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Woo Seok Kim: Texas A&M University
M. Ibrahim Khot: University of Leeds
Hyun-Myung Woo: Texas A&M University
Sungcheol Hong: Texas A&M University
Dong-Hyun Baek: Sun Moon University
Thomas Maisey: University of Leeds
Brandon Daniels: Texas A&M University
P. Louise Coletta: University of Leeds
Byung-Jun Yoon: Texas A&M University
David G. Jayne: University of Leeds
Sung Il Park: Texas A&M University

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract Photodynamic therapy (PDT) offers several advantages for treating cancers, but its efficacy is highly dependent on light delivery to activate a photosensitizer. Advances in wireless technologies enable remote delivery of light to tumors, but suffer from key limitations, including low levels of tissue penetration and photosensitizer activation. Here, we introduce DeepLabCut (DLC)-informed low-power wireless telemetry with an integrated thermal/light simulation platform that overcomes the above constraints. The simulator produces an optimized combination of wavelengths and light sources, and DLC-assisted wireless telemetry uses the parameters from the simulator to enable adequate illumination of tumors through high-throughput (

Date: 2022
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DOI: 10.1038/s41467-022-29878-1

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