首页|Rapid machine-learning enabled design and control of precise next-generation cryogenic surgery in dermatology
Rapid machine-learning enabled design and control of precise next-generation cryogenic surgery in dermatology
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NETL
NSTL
Elsevier
In the field of dermatology, the use of cryogenic processes, such as cryoablation, cryotherapy, etc., have grown dramatically over the last decade. This usually entails using a cryoprobe to freeze and destroy unwanted tissue, such as cancer cells. The focus of this work is to develop a digital-twin (a digital replica) of the performance of a cryogenic probe, which can be used to pre-plan and optimize surgical procedures, in order to maximize successful outcomes. Specifically, we model the optimal cryoprobe-induced cooling protocol needed to eliminate cells/tissue in specific regions, while minimizing damage to nearby tissue. The modeling approach is to develop mathematical surface point-source heat extraction kernels and then to create optimal surface patterns that the cryoprobe induces, by arranging the point-sources accordingly. Spatial and temporal control of the heat extraction is modeled. The entire subdermal thermal field is then constructed by superposing the solutions, enabling precise cryogenic treatment. Finally, a Machine Learning Algorithm (MLA) is then applied to optimize the set of parameters to deliver a precise response, making it an ideal real-time surgical tool.
CryogenicsDermatologyDigital-twinMachine-learning
Tarek I. Zohdi、Mona Zohdi-Mofid
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Department of Mechanical Engineering, University of California, Berkeley, CA, 94720-1740, USA
Sharp Community Medical Group, 8929 University Center Ln #202, San Diego, CA 92122, USA
NETL
NSTL
Elsevier
