Abstract: A method for assessing occurrence of heart failure includes the following steps. A heart failure assessment program established is provided. A target ECG signal data of the subject is provided, wherein the target ECG signal data includes a plurality of target heartbeat waveform data and a plurality of target heart rate data. A data pre-processing step is performed, wherein the target ECG signal data is pre-processed by the data processing module so as to obtain a processed target ECG signal data. An analyzing step is performed, wherein the processed target ECG signal data is analyzed by the heart failure assessment program so as to obtain a heart failure occurrence assessing result, and the heart failure occurrence assessing result presents a heart failure occurring condition and the severity of the heart failure of the subject.

Abstract: A method for assessing cardiothoracic ratio (CTR) includes following steps. A testing X-ray image database of a subject is provided. A first image data classifying step is performed, wherein the testing X-ray image database is classified by a first deep learning neural network classifier to obtain a testing chest X-ray image data. A second image data classifying step is performed, wherein the testing chest X-ray image data is classified by a second deep learning neural network classifier to obtain a target chest X-ray image data. A feature extracting step is performed, wherein a diameter of thoracic cavity and a diameter of cardiac silhouette of the target chest X-ray image data are captured automatically and then trained to achieve a convergence by a third deep learning neural network classifier. An assessing step is performed, wherein an assessing result of CTR is obtained according to a feature of CTR.

Abstract: A method for assessing fasting status includes the following steps. A fasting blood glucose database is provided, wherein the fasting blood glucose database includes a plurality of fasting blood glucose data and a plurality of non-fasting blood glucose data. A model establishing step is performed, wherein the plurality of fasting blood glucose data and the plurality of non-fasting blood glucose data are trained to achieve a convergence by a machine-learning model so as to obtain a fasting-status assessing classifier. An ontological data of a subject is provided, wherein the ontological data includes a blood glucose concentration data. An assessing step is performed, wherein the ontological data is analyzed by the fasting-status assessing classifier to obtain an assessing result of fasting status of the subject.

Abstract: An intelligent gait analyzing apparatus comprises a first camera and an electronic device. The first camera captures a first video in which the patient walks and a second video in which the patient changes a posture from standing up to sitting down. A second camera may be used to capture a three-dimensional point cloud diagram of the patient to generate a chest skeleton and an abdomen skeleton. A 3D skeleton model and a moving diagram may be created based on the first video. The memory stores a long short-term memory model, a transformer machine learning model and an algorithm of modified gravity center. The graphic processor calculates a gravity center deviation based on the moving diagram, the chest skeleton and the abdomen skeleton. The graphic processor acquires a stride, a pace, a humpbacked value and a risk of falling.

Abstract: A renal function assessment method includes following steps. A target kidney ultrasound image data of a subject is provided. An image pre-processing step is performed, wherein an image size of the target kidney ultrasound image data is adjusted, and the target kidney ultrasound image data is normalized according to an average and a standard deviation of a visual image database to obtain an after-processed target kidney ultrasound image data. A feature extracting step is performed, wherein the after-processed target kidney ultrasound image data is trained to achieve a convergence by a first deep-learning classifier to obtain an image feature of the after-processed target kidney ultrasound image data. A determining step is performed, wherein the image feature of the after-processed target kidney ultrasound image data is analyzed by the first deep-learning classifier to obtain an assessing result of an estimated glomerular filtration rate (eGFR).

Abstract: A non-invasive method of evaluating blood cell measurement and a non-invasive blood cell measurement evaluating system are provided. The non-invasive method of evaluating blood cell measurement includes providing of a dialysis tubing image datum of a subject, performing an image preprocessing step, performing a model predicting step and performing a determining and classifying step. The non-invasive blood cell measurement evaluating system includes an image capturing device and a processor electrically connected to the image capture device.

Abstract: A method for assessing cardiothoracic ratio (CTR) includes following steps. A testing X-ray image database of a subject is provided. A first image data classifying step is performed, wherein the testing X-ray image database is classified by a first deep learning neural network classifier to obtain a testing chest X-ray image data. A second image data classifying step is performed, wherein the testing chest X-ray image data is classified by a second deep learning neural network classifier to obtain a target chest X-ray image data. A feature extracting step is performed, wherein a diameter of thoracic cavity and a diameter of cardiac silhouette of the target chest X-ray image data are captured automatically and then trained to achieve a convergence by a third deep learning neural network classifier. An assessing step is performed, wherein an assessing result of CTR is obtained according to a feature of CTR.

Abstract: A method for assessing acute kidney injury includes following steps. An acute kidney injury assessing date of a subject is provided. A testing kidney function diagnostic dataset is provided, wherein the testing kidney function diagnostic dataset includes a plurality of serum creatinine concentration data and a plurality of glomerular filtration rate data, and a recording date of each of the serum creatinine concentration data and a recording date of each of the glomerular filtration rate data is on 0 to 180 days before the acute kidney injury assessing date. A preprocessing step is performed. A first classifying step is performed, wherein a fluctuation value of serum creatinine concentration is classified according to a first threshold or a fluctuation value of eGFR is classified according to a second threshold so as to obtain a result of AKI status of the subject.

Abstract: A renal function assessment method includes following steps. A target kidney ultrasound image data of a subject is provided. An image pre-processing step is performed, wherein an image size of the target kidney ultrasound image data is adjusted, and the target kidney ultrasound image data is normalized according to an average and a standard deviation of a visual image database to obtain an after-processed target kidney ultrasound image data. A feature extracting step is performed, wherein the after-processed target kidney ultrasound image data is trained to achieve a convergence by a first deep-learning classifier to obtain an image feature of the after-processed target kidney ultrasound image data. A determining step is performed, wherein the image feature of the after-processed target kidney ultrasound image data is analyzed by the first deep-learning classifier to obtain an assessing result of an estimated glomerular filtration rate (eGFR).

Abstract: The present invention relates to a surface-mount connector for electrically interconnecting a first circuit board and a second circuit board. The surface-mount connector includes a first connecting part, a second connecting part and a sidewall. The first connecting part is bonded onto the first circuit board. The second connecting part has a conductive surface bonded onto the second circuit board. The sidewall has a first end axially extended from a periphery of the second connecting part and a second end being formed as at least a portion of the first connecting part.

Abstract: A power module fabrication method and structure thereof is disclosed.

Abstract: A circuit board assembly includes a first circuit board, at least one surface mount connector, and a second circuit board. The first circuit board has at least a first contact. The surface mount connector is substantially a solid rod and includes a first conducting part coupled to the contact of the first circuit board and a second conducting part having a curvy raised portion at the top surface thereof. The second circuit board has at least a second contact coupled to the second conducting part of the surface mount connector.

Abstract: A circuit board assembly includes a first circuit board, at least one surface mount connector, and a second circuit board. The first circuit board has at least a first contact. The surface mount connector is substantially a solid rod and includes a first conducting part coupled to the contact of the first circuit board and a second conducting part having a curvy raised portion at the top surface thereof. The second circuit board has at least a second contact coupled to the second conducting part of the surface mount connector.