Abstract: An image processing system includes an ophthalmoscope device and a processor. The ophthalmoscope device is configured to obtain a color fundus image. The processor is configured to receive the color fundus image; generate a blood vessel segmentation image that corresponds to the color fundus image using a computer vision algorithm or a deep learning model; preprocess the color fundus image and the blood vessel segmentation image to obtain an initial input image; and input the initial input image into a convolutional neural network. The convolutional neural network outputs a value. In addition, the processor generates fundus image analysis information from the cup-to-disc ratio and the value.

Abstract: A blood vessel detecting apparatus and an image-based blood vessel detecting method are provided. In the method, first to-be-evaluated data is detected through a first detecting model to obtain a first detection result. Second to-be-evaluated data is detected through a second detecting model to obtain a second detection result. The first to-be-evaluated data includes one or more medical images obtained from photographing a blood vessel. The first detection result output by the first detecting model includes one or more pixels in the medical image belonging to the blood vessel. The first detecting model and the second detecting model are constructed based on a machine learning algorithm. The second to-be-evaluated data includes the first detection result. The second detection result output by the second detecting model includes one or more pixels in the medical image belonging to the blood vessel.

Abstract: A model training method and an electronic device are provided. The method includes the following steps: establishing a brain age prediction model according to a training set; adjusting a parameter in the brain age prediction model according to a validation set; inputting a test set into the brain age prediction model with the adjusted parameter to obtain a plurality of first predicted brain ages; determining whether the first predicted brain ages satisfy a first specific condition; and completing training of the brain age prediction model when the first predicted brain ages satisfy the first specific condition.

Abstract: An electronic device and a model updating method are provided. The method includes: inputting a plurality of files to a first model and outputting a predicted result of each of the plurality of files; receiving a corrected result for correcting the prediction result of at least one first file in the plurality of files, and generating a first label file corresponding to the at least one first file according to the corrected result and the first file; training a plurality of models according to the first label file to generate a plurality of a trained model; testing the plurality of trained models using at least one test set; and replacing the first model with a first trained model when the predicting accuracy of the first trained model of the plurality of trained models is higher than the predicting accuracy of the first model.

Abstract: Provided is a bioink set for printing a construct that is able to carry cells, including a bioink which contains a biodegradable polyurethane and a biopolymer, and a divalent metal ion solution. The biopolymer is gelatin, agar, alginate salts, hyaluronic acid and salts thereof, chitosan, and any combination thereof. Also provided are a method of preparing a construct for carrying cells by three-dimensional printing with the bioink set, and a method of three-dimensional printing of cells by using an ink composition.

Abstract: A blood vessel detecting apparatus and an image-based blood vessel detecting method are provided. In the method, first to-be-evaluated data is detected through a first detecting model to obtain a first detection result. Second to-be-evaluated data is detected through a second detecting model to obtain a second detection result. The first to-be-evaluated data includes one or more medical images obtained from photographing a blood vessel. The first detection result output by the first detecting model includes one or more pixels in the medical image belonging to the blood vessel. The first detecting model and the second detecting model are constructed based on a machine learning algorithm. The second to-be-evaluated data includes the first detection result. The second detection result output by the second detecting model includes one or more pixels in the medical image belonging to the blood vessel.

Abstract: An image processing system includes an ophthalmoscope device and a processor. The ophthalmoscope device is configured to obtain a color fundus image. The processor is configured to receive the color fundus image; generate a blood vessel segmentation image that corresponds to the color fundus image using a computer vision algorithm or a deep learning model; preprocess the color fundus image and the blood vessel segmentation image to obtain an initial input image; and input the initial input image into a convolutional neural network. The convolutional neural network outputs a value. In addition, the processor generates fundus image analysis information from the cup-to-disc ratio and the value.

Abstract: A blood vessel status evaluation method and a blood vessel status evaluation device are provided. The method includes: obtaining at least one angiography image corresponding to a target user; analyzing the angiography image by a first deep learning model to select a target image from the angiography image; analyzing the target image by at least one second deep learning model to determine a blood vessel type of the target user and divide a target blood vessel pattern in the target image into a plurality of scoring segments; and analyzing an output of the second deep learning model by a third deep learning model to obtain a blood vessel status of the target user.

Abstract: A model training method and an electronic device are provided. The method includes the following steps: establishing a brain age prediction model according to a training set; adjusting a parameter in the brain age prediction model according to a validation set; inputting a test set into the brain age prediction model with the adjusted parameter to obtain a plurality of first predicted brain ages; determining whether the first predicted brain ages satisfy a first specific condition; and completing training of the brain age prediction model when the first predicted brain ages satisfy the first specific condition.

Abstract: A blood vessel status evaluation method and a blood vessel status evaluation device are provided. The method includes: obtaining at least one angiography image corresponding to a target user; selecting a target image from the angiography image; determining a blood vessel type of the target user according to a distribution status of a target blood vessel pattern in the target image; establishing a blood vessel topology structure corresponding to the target blood vessel pattern which includes information of a width of a blood vessel in the target blood vessel pattern and information of an intersection of blood vessel in the target blood vessel pattern; and automatically analyzing a blood vessel status of the target user according to the blood vessel type and the blood vessel topology structure.

Abstract: A blood vessel status evaluation method and a blood vessel status evaluation device are provided. The method includes: obtaining at least one angiography image corresponding to a target user; analyzing the angiography image by a first deep learning model to select a target image from the angiography image; analyzing the target image by at least one second deep learning model to determine a blood vessel type of the target user and divide a target blood vessel pattern in the target image into a plurality of scoring segments; and analyzing an output of the second deep learning model by a third deep learning model to obtain a blood vessel status of the target user.

Abstract: A blood vessel status evaluation method and a blood vessel status evaluation device are provided. The method includes: obtaining at least one angiography image corresponding to a target user; selecting a target image from the angiography image; determining a blood vessel type of the target user according to a distribution status of a target blood vessel pattern in the target image; establishing a blood vessel topology structure corresponding to the target blood vessel pattern which includes information of a width of a blood vessel in the target blood vessel pattern and information of an intersection of blood vessel in the target blood vessel pattern; and automatically analyzing a blood vessel status of the target user according to the blood vessel type and the blood vessel topology structure.

Abstract: An electronic device and a model updating method are provided. The method includes: inputting a plurality of files to a first model and outputting a predicted result of each of the plurality of files; receiving a corrected result for correcting the prediction result of at least one first file in the plurality of files, and generating a first label file corresponding to the at least one first file according to the corrected result and the first file; training a plurality of models according to the first label file to generate a plurality of a trained model; testing the plurality of trained models using at least one test set; and replacing the first model with a first trained model when the predicting accuracy of the first trained model of the plurality of trained models is higher than the predicting accuracy of the first model.

Abstract: Provided is a bioink set for printing a construct that is able to carry cells, including a bioink which contains a biodegradable polyurethane and a biopolymer, and a divalent metal ion solution. The biopolymer is gelatin, agar, alginate salts, hyaluronic acid and salts thereof, chitosan, and any combination thereof. Also provided are a method of preparing a construct for carrying cells by three-dimensional printing with the bioink set, and a method of three-dimensional printing of cells by using an ink composition.

Abstract: An automatic vehicle power and headlight controlling device with delay effect, including a delay circuit, an in-car sensing circuit, an out-car sensing circuit and a power supply circuit. The delay circuit is connected to both the in-car sensing circuit and the power supply circuit, while the out-car sensing circuit is connected to the in-car sensing circuit and powered thereby. After the delay circuit works, the in-car sensing circuit and the power supply circuit are delayedly energized. Only when the in-car sensing circuit works, the out-car sensing circuit is energized. Accordingly, at the stage of initially starting the vehicle, the excessive load on the battery is avoided so that the damage of the battery resulting from the excessive load can be obviated.