A thulium fiber laser system can be used to treat tissues based on the ability for quick changes between laser pulses. For example, to treat stones in a tissue, a long pulse having low peak power can be used to create bubbles in front of the stone (calculi), then follow a series of shorter pulses and higher peak power can be used to break the stone. The sequence can be repeated to maintain large bubble formation, with the long pulse characteristics changed to accommodate for the changes in the tissue. A fluorescent sensing assembly can be used to detect the tissue conditions for selecting the conditions of the thulium fiber laser.

An artificial intelligence assisted medical diagnosis method for a sepsis is proposed. A database reading step is performed to read a sepsis database and at least one database to be tested of a storing unit. The sepsis database includes a plurality of sepsis data, and the at least one database to be tested includes a plurality of data to be tested. A data table creating step is performed to create a sepsis data table according to the sepsis data, and create a data table to be tested according to the data to be tested. A model training step is performed to train the sepsis data table according to a K-fold cross-validation and a machine learning algorithm to generate a sepsis diagnosis model. A sepsis predicting step is performed to input the data table to be tested into the sepsis diagnosis model to calculate a sepsis prediction result.

An optical measuring apparatus includes first and second light-emitting elements that emit light and a controller. Upon detection of the presence of a body by light emitted from the first light-emitting element, the controller performs control so that the second light-emitting element will emit light with an amount for measuring the body.

A method of determining a duration of safe apnoea. Information is obtained relating to a respiratory indicator, which can include information relating to a potential respiratory equilibrium, and a duration of safe apnoea is determined from the obtained information.

Systems and methods for providing a user characteristic to a service provider for a virtual conference with a user are provided. In particular, a computing device may collect raw media data associated with the user during the virtual conference between the user and the service provider. During the virtual conference, the computing device may perform a first processing of the raw media data to extract intermediate user data, wherein the intermediate user data comprises one or more of a physiological signal and a behavioral signal associated the user. The computing device may further transform the raw media data into transformed media data and transmit the transformed media data with the intermediate user data to a server for second processing of the intermediate user data.

A prediction method and system of a low blood pressure is provided, including: obtaining a plurality of feature sequence values; selecting two of the feature sequence values from the feature sequence values according to a time ratio relationship; calculating a relation coefficient according to the selected two feature sequence values by a weighting process; repeating to select the new feature sequence values and the corresponding relation coefficient and to assign the new feature sequence values and the relation coefficient into the input group until the feature sequence values conforming to the time ratio relationship are traversed; and obtaining a training result by substituting the input group into a low blood pressure prediction model.

The present invention provides a method and apparatus for treating a subject suffering from obstructive sleep apnea (OSA). In one embodiment, the method of the present invention is configured to deliver negative airway pressure to a subject's airway to strengthen the airway muscle tone.

In some examples, a system is configured to determine a non-cerebral autoregulation status value of a patient using machine learning. In some examples, processing circuitry of the system is configured to determine, using a neural network algorithm that has been trained via machine learning training, an individualized adjustment value that is individualized for the patient, including inputting physiological data associated with the patient. The processing circuitry may determine a non-cerebral autoregulation status of the patient based on a cerebral autoregulation value of the patient based on the non-cerebral autoregulation status value of the patient and the adjustment value.

A photoacoustic apparatus according to the present invention is a photoacoustic apparatus for obtaining image data based on an acoustic wave generated by irradiating an object with light of a first wavelength and light of a second wavelength which is different from the first wavelength, the photoacoustic apparatus including a processing unit configured to: obtain a first image data group corresponding to the first wavelength generated based on an acoustic wave generated by irradiating the object with light of the first wavelength a plurality of times, obtain first positional deviation information corresponding to an irradiation timing with the light of the first wavelength on the basis of a first image data group corresponding to the first wavelength, and obtain second positional deviation information corresponding to an irradiation timing with the light of the second wavelength on the basis of the first positional deviation information.

Devices, systems, and methods can be used to suggest a discovery to an individual related to their health and wellness, including receiving data about the individual from a user interface regarding a goal for the individual, querying the individual regarding their perception of the goal, determining, a likely state of the individual (e.g., readiness to change), and selecting a subset of discoveries to display to the individual from a database that correspond to both the goal for the individual and the likely state of the individual. Displaying information may include receiving motion data including duration of motion, classifying a type of activity the individual is engaged in based on the motion data and likely intensity of the activity, and displaying a graphical user interface including a color spectrum, depending on one of the type of activity, intensity of the activity, or duration of the activity.