Abstract:
A computer-assisted needle insertion system and a computer-assisted needle insertion method are provided. The computer-assisted needle insertion method includes: obtaining a first machine learning (ML) model and a second ML model; obtaining a computed tomography (CT) image and a needle insertion path, generating a suggested needle insertion path according to the first ML model, the CT image, and the needle insertion path, and instructing a needle to approach a needle insertion point on a skin of a target, wherein the needle insertion point is located on the suggested needle insertion path; obtaining a breath signal of the target, and estimating whether a future breath state of the target is normal according to the second ML model and the breath signal; and outputting a suggested needle insertion period according to the breath signal in response to determining that the future breath state is normal.
Abstract:
A computer-assisted needle insertion method is provided. The computer-assisted needle insertion method includes the following steps. A first machine learning model and a second machine learning model are obtained. A computed tomography image and a needle insertion path are obtained, a suggested needle insertion path is generated according to the first machine learning model, the computed tomography image, and the needle insertion path, and the needle is instructed to approach a needle insertion point on a skin of a target. The needle insertion point is located on the suggested needle insertion path. A breath signal of the target is obtained, and whether a future breath state of the target is normal is estimated according to the second machine learning model and the breath signal. A suggested needle insertion period is output according to the breath signal in response to determining that the future breath state is normal.
Abstract:
A memory device operable to provide multi-port functionality, which may comprise a single-port memory having a first operating frequency that is at least twice of a second operation frequency of a multi-port memory, a read synchronization module that synchronizes a set of read signals from the second operation frequency to the first operating frequency, a write synchronization module that synchronizes a set of write signals from the second operation frequency to the first operating frequency, a read/write signal selector that integrates a set of synchronized read signals and a set of synchronized write signals into a set of input control signals of the single-port memory, and a read out data synchronization module configured to synchronize a set of read out data from the single-port memory with the second operation frequency of the multi-port memory.
Abstract:
A state assessment system, a diagnosis and treatment system and a method for operating the diagnosis and treatment system are disclosed. An oscillator model converts a physiological signal of a subject into a defined feature image. A classification model analyzes state information of the subject based on the feature image. An analysis model outputs a treatment suggestion for the subject based on the state information of the subject. An AR projection device projects acupoint positions of a human body onto the subject, for the subject to be treated based on the treatment suggestion.
Abstract:
A state assessment system, a diagnosis and treatment system and a method for operating the diagnosis and treatment system are disclosed. An oscillator model converts a physiological signal of a subject into a defined feature image. A classification model analyzes state information of the subject based on the feature image. An analysis model outputs a treatment suggestion for the subject based on the state information of the subject. An AR projection device projects acupoint positions of a human body onto the subject, for the subject to be treated based on the treatment suggestion.
Abstract:
A computer-assisted needle insertion system and a computer-assisted needle insertion method are provided. The computer-assisted needle insertion method includes: obtaining a first machine learning (ML) model and a second ML model; obtaining a computed tomography (CT) image and a needle insertion path, generating a suggested needle insertion path according to the first ML model, the CT image, and the needle insertion path, and instructing a needle to approach a needle insertion point on a skin of a target, wherein the needle insertion point is located on the suggested needle insertion path; obtaining a breath signal of the target, and estimating whether a future breath state of the target is normal according to the second ML model and the breath signal; and outputting a suggested needle insertion period according to the breath signal in response to determining that the future breath state is normal.