Electrosurgical Tissue Temperature Estimation Without Thermal Sensors

The patent replaces physical temperature sensors with a machine learning-based estimation system that uses electrical parameters (voltage, current, impedance) to predict tissue temperature. This substitution eliminates the need for complex thermal sensing hardware while achieving comparable temperature monitoring functionality through computational algorithms trained on electrical measurement data.

The patent introduces machine learning algorithms as an intermediary between electrical parameter measurements and tissue temperature determination. Instead of directly measuring temperature with sensors, the system uses trained neural networks to infer temperature from electrical characteristics, acting as a computational mediator that translates readily available electrical data into meaningful thermal information.

The patent substitutes expensive temperature sensing hardware with cost-effective machine learning algorithms that process standard electrical measurements. This approach maintains treatment precision by accurately estimating tissue temperature through trained models while avoiding the high costs associated with integrating specialized thermal sensors into surgical instruments.

The patent creates a virtual model of tissue thermal response through machine learning algorithms that replicate the behavior of physical temperature sensors. The trained neural networks serve as computational copies of thermal sensing functionality, providing accurate temperature estimation without requiring actual physical contact with thermal measurement devices.

The patent performs preliminary training of machine learning algorithms using extensive datasets of electrical parameters and corresponding tissue temperature measurements before deployment. This pre-training phase establishes accurate predictive models that can then be used during surgical procedures, ensuring measurement precision is achieved through prior computational preparation rather than complex real-time hardware.

The patent implements feedback mechanisms where the machine learning algorithms continuously refine temperature estimates based on ongoing electrical parameter measurements during tissue treatment. The system monitors voltage, current, and impedance changes in real-time and adjusts temperature predictions accordingly, maintaining measurement accuracy through dynamic feedback from the actual tissue response.