Dual patch system for measuring tissue properties

1 . A medical patch system for post-operative monitoring of tissue properties in a patient, comprising:

one medical patch positionable on post-operable flap tissue on the patient and another medical patch positionable on adjacent healthy tissue, each of said medical patches comprising:

a first module configured to measure tissue oxygenation; and

a second module configured to measure skin temperature;

said medical patch positionable on flap tissue further comprising a third module configured to deliver a therapy effective to accelerate healing of tissue and to stimulate cell proliferation therein; and

each of said medical patches comprising a control module configured to:

power the medical patch;

receive wirelessly multimodal time-synchronized data comprising tissue oxygenation and skin temperature data;

calculate a differential physiological parameter ΔStO2 or ΔT or a combination thereof based on the time-synchronized data;

execute an AI algorithm trained to classify a tissue viability state and predict an ischemic risk based on a series of the multimodal time-synchronized data; said tissue viability state and said ischemic risk are determined based on the differential physiological parameter calculated between the medical patch on the flap tissue and the medical patch on the adjacent healthy tissue;

to issue alerts if the ischemic risk exceeds a defined threshold; and

adjust dynamically parameters of the therapy delivered by the third module based on said differential physiological parameters.

2 . The medical patch of claim 1 , wherein the first module comprises a plurality of LEDs that emit wavelengths in a visible spectrum and a near infrared spectrum.

3 . The medical patch of claim 1 , wherein the second module is further configured to measure ambient temperature.

4 . The medical patch of claim 1 , wherein the third module delivers a photobiomodulation therapy or an electrical stimulation therapy.

5 . The medical patch of claim 4 , wherein the third module comprises a plurality of LEDs that emit red light with at least one wavelength of about 600 nm to about 1100 nm effective for a controlled photobiomodulation and a driver circuit.

6 . The medical patch of claim 1 , wherein the third module comprises a cathode and an anode effective to deliver an electric current of about 0 mA to about 30 mA, a frequence of 0 KHz to about 1 KHz, and duty cycles of 0% to 100% with different shapes to pass through post-operative tissue.

7 . The medical patch of claim 1 , further comprising a fourth module configured to detect edema in tissue.

8 . The medical patch of claim 7 , wherein the fourth module comprises another plurality of LEDs that emit light with center wavelengths of about 900 nm to about 1000 nm, of about 1400 nm to about 1500 nm or about 1800 nm to about 2200 nm to detect the edema.

9 . A dual patch system for post-operative flap monitoring, comprising:

a flap patch removably attachable to a surgical flap and comprising:

a first tissue oxidation module configured to monitor and measure tissue oxygenation, said tissue oxidation module having a plurality of surface-mounted LEDs that emit at a combination of different wavelengths and a photodetector;

a first thermal sensing module comprising at least one skin-contact temperature sensor; and

a therapeutic excitation module comprising a photobiomodulation patch with a driver circuit or a cathode and anode, each of which are configured to accelerate healing and stimulate cell proliferation in the surgical flap;

another patch removably attachable to healthy tissue adjacent to the surgical flap and comprising:

a second tissue oxidation module configured to monitor and measure tissue oxygenation, said tissue oxidation module having a plurality of surface-mounted LEDs that emit at a combination of different wavelengths and a photodetector;

a second thermal sensing module comprising at least one skin-contact temperature sensor and an ambient temperature sensor; and

a control module configured to:

power the flap patch and the other patch;

receive wirelessly multimodal time-synchronized data from the flap patch and the other patch comprising tissue oxygenation and skin temperature data;

calculate a differential physiological parameter ΔStO2 or ΔT or a combination thereof based on the time-synchronized data:

execute an AI algorithm trained to classify a tissue viability state and predict an ischemic risk based on a series of the multimodal time-synchronized data from the flap patch; said tissue viability state and said ischemic risk determined based on the differential physiological parameter calculated between the flap patch on the flap tissue and the patch on the healthy tissue adjacent to the flap patch;

issue alerts locally and to a cloud-based dashboard if the ischemic risk exceeds a defined threshold; and

adjust dynamically parameters of the therapy delivered by the third module based on said differential physiological parameters.

10 . The dual patch system of claim 9 , said flap patch and said other patch further comprising an edema detection module that has a plurality of LEDs that emit light with center wavelengths of about 900 nm to about 1000 nm, of about 1400 nm to about 1500 nm or about 1800 nm to about 2200 nm.

11 . The dual patch system of claim 9 , wherein the plurality of surface-mounted LEDs in the first tissue oxidation module and the second tissue oxidation module emit at 625 nm, 660 nm, 805 nm, and 870 nm.

12 . The dual patch system of claim 9 , wherein the photobiomodulation patch in the flap patch comprises a plurality of LEDs that emit red light with at least one wavelength of about 600 nm to about 1100 nm effective to control photobiomodulation.

13 . The dual patch system of claim 9 , wherein the cathode and anode in the flap patch are effective to deliver an electric current of about 0 mA to about 30 mA, a frequence of 0 KHz to about 1 KHz, and duty cycles of 0% to 100% with different shapes to pass through post-operative tissue.

14 . The dual patch system of claim 9 , wherein the AI algorithm is a deep neural network trained on labeled flap outcome data to predict the probability of flap compromise within a 6-hour horizon.

15 . The dual patch system of claim 14 , said AI algorithm comprising attention-based temporal filtering configured to weigh recent versus historical measurements in a prediction process.

16 . A method for real-time monitoring and modulating tissue health in a surgical flap, comprising the steps of:

placing the flap patch and the other patch of the dual patch system of claim 12 onto the surgical flap and onto the healthy tissue adjacent to the flap patch;

acquiring continuously temperature data and tissue oxygenation data from the flap patch and the other patch;

computing physiological deltas between the surgical flap and the healthy tissue;

inputting multimodal time-series data into an AI algorithm to determine a predicted risk level of tissue viability in the surgical flap;

adjusting automatically parameters of the photobiomodulation patch in the flap patch in response to the predicted risk level; and

issuing wireless alerts to a health provider if a deterioration in flap health is detected or predicted.

17 . The method of claim 16 , wherein the AI algorithm is a deep neural network, the method comprising training the AI algorithm on labeled flap outcome data to predict the probability of flap compromise within a 6-hour horizon, said AI algorithm comprising attention-based temporal filtering configured to weigh recent versus historical measurements in a prediction process.