Abstract: An optical system is provided. The optical system is used for disposing on an electronic device. The optical system includes a movable portion, a fixed portion, a first driving assembly, and a support module. The movable portion is used for connecting to an optical module. The fixed portion is affixed on the electronic device, and the movable portion is movable relative to the fixed portion. The first driving assembly is used for driving the movable portion to move relative to the fixed portion. The movable portion is movably connected to the fixed portion through the support module.

Abstract: A meniscus tear assisted determination system includes an image capturing device and a processor. The image capturing device is for capturing a target protocol of a subject, and the target protocol includes a plurality of target knee joint image sequences. The processor is signally connected to the image capturing device and includes a data preprocessing module and a meniscus tear assisted determination program. The data preprocessing module is for grouping the plurality of target knee joint image sequences and extracting a plurality of target coronal plane image sequences and a plurality of target sagittal plane image sequences. The meniscus tear assisted determination program includes a meniscus location detector and a meniscus tear predictor.

Abstract: A sleep apnea assessment method includes the following steps. A sleep apnea assessment system is provided. A target ECG signal data of the subject is obtained. A data pre-processing step is performed so as to obtain a target ECG time-frequency data, and the target ECG time-frequency data is processed so as to obtain a plurality of target time-frequency segment data. An assessing step of apnea event is performed so as to output an assessing result of sleep apnea event of each of the plurality of target time-frequency segment data, and the assessing result of sleep apnea event is for assessing whether the subject has the sleep apnea event in any one of the plurality of target time-frequency segment data or not and predicting a probability of an occurrence of sleep apnea event of the subject.

Abstract: 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.

Abstract: An intelligent medical speech automatic recognition method includes performing a first model training step, a second model training step, a voice receiving step, a signal pre-treatment step and a transforming step. The first model training step is performed to train a generic statement data and a medical statement data of a database to establish a first model. The second model training step is performed to train a medical textbook data of the database to establish a second model. The voice receiving step is performed to receive a speech signal. The signal pre-treatment step is performed to receive the speech signal from the voice receiver and transform the speech signal into a to-be-recognized speech signal. The transforming step is performed to transform and recognize the to-be-recognized speech signal into a complete sentence writing character according to the first model and the second model.