Abstract: Method of determining insulin injection amount, computer storage medium, and devices, the method includes including: obtaining characteristic information and a blood glucose content at a current time of a target user; and determining an insulin injection amount at each time of the target user based on the characteristic information of the target user, the blood glucose content at the current time of the target user, a predetermined blood glucose prediction model, and a predetermined insulin injection amount prediction model. The method can facilitate the determination of the insulin injection amount at each time.

Abstract: A method of fundus oculi image analysis includes acquiring a target fundus oculi image; analyzing the target fundus oculi image by a fundus oculi image analysis model determined by training to acquire an image analysis result of the target fundus oculi image; and the fundus oculi image analysis model includes at least one of an image overall grade prediction sub-model and an image quality factor sub-model. The method performs quality analysis on the target fundus oculi image by the fundus oculi image analysis model, and when the model includes the overall grade prediction sub-model, a prediction result of whether the target fundus oculi image as a whole is gradable can be acquired; when the model includes the image quality factor sub-model, the analysis result of the fundus oculi image quality factor can be acquired and the image analysis model is determined by extensive image training, and the reliability of the result of whether the image is gradable determined based on the above model is high.

Abstract: Methods of segmenting an abdominal image, computer apparatuses and storage mediums. The method includes acquiring an abdominal image to be examined; and classifying pixels in the abdominal image to be examined based on a trained full convolution neural network, and determining a segmented image corresponding to the abdominal image to be examined, wherein the trained full convolution neural network is trained and determined based on a first training set and a second training set, the first training set includes first sample abdominal images and pixel classification label images corresponding to the first sample abdominal images, the second training set includes second sample abdominal images and the number of pixels of second sample abdominal images correspondingly belong to each class. Through the methods herein, the accuracy of the segmentation can be improved.

Abstract: A fundus image processing method comprising: receiving a collected fundus image; identifying the fundus image via a first neural network to generate a first feature set of the fundus image; identifying the fundus image via a second neural network to generate a second feature set of the fundus image, wherein the first feature set and the second feature set indicate different lesion attributes of the fundus image; combining the first feature set and the second feature set to obtain a combined feature set of the fundus image; and inputting the combined feature set into a classifier to obtain a classification result.

Abstract: Method of determining insulin injection amount, computer storage medium, and devices, the method includes including: obtaining characteristic information and a blood glucose content at a current time of a target user; and determining an insulin injection amount at each time of the target user based on the characteristic information of the target user, the blood glucose content at the current time of the target user, a predetermined blood glucose prediction model, and a predetermined insulin injection amount prediction model. The method can facilitate the determination of the insulin injection amount at each time.

Abstract: Methods of segmenting an abdominal image, computer apparatuses and storage mediums. The method includes acquiring an abdominal image to be examined; and classifying pixels in the abdominal image to be examined based on a trained full convolution neural network, and determining a segmented image corresponding to the abdominal image to be examined, wherein the trained full convolution neural network is trained and determined based on a first training set and a second training set, the first training set includes first sample abdominal images and pixel classification label images corresponding to the first sample abdominal images, the second training set includes second sample abdominal images and the number of pixels of second sample abdominal images correspondingly belong to each class. Through the methods herein, the accuracy of the segmentation can be improved.

Abstract: A method of fundus oculi image analysis includes acquiring a target fundus oculi image; analyzing the target fundus oculi image by a fundus oculi image analysis model determined by training to acquire an image analysis result of the target fundus oculi image; and the fundus oculi image analysis model includes at least one of an image overall grade prediction sub-model and an image quality factor sub-model. The method performs quality analysis on the target fundus oculi image by the fundus oculi image analysis model, and when the model includes the overall grade prediction sub-model, a prediction result of whether the target fundus oculi image as a whole is gradable can be acquired; when the model includes the image quality factor sub-model, the analysis result of the fundus oculi image quality factor can be acquired and the image analysis model is determined by extensive image training, and the reliability of the result of whether the image is gradable determined based on the above model is high.

Abstract: A fundus image processing method comprising: receiving a collected fundus image; identifying the fundus image via a first neural network to generate a first feature set of the fundus image; identifying the fundus image via a second neural network to generate a second feature set of the fundus image, wherein the first feature set and the second feature set indicate different lesion attributes of the fundus image; combining the first feature set and the second feature set to obtain a combined feature set of the fundus image; and inputting the combined feature set into a classifier to obtain a classification result.

Abstract: A method of automatically detecting microaneurysm based on multi-sieving convolutional neural network (CNN), includes the following steps of: A1), partitioning an image to be detected using random fern and obtaining an auxiliary channel image of the image according to a first partition result; and A2), inputting the auxiliary channel image obtained from step A1) and the image to a multi-sieving CNN training model to perform a detection and obtaining a microaneurysm detection result of the image. The process of establishing the training model includes: B1), using a current microaneurysm diagnostic report as samples and partitioning a lesion image in the microaneurysm diagnostic report using the random fern, and establishing the auxiliary channel image according to a second partition result; B2), comparing the obtained auxiliary channel image with a lesion-marked image of pixels, clarifying the samples according to a comparing result and performing the multi-sieving CNN training.

Abstract: A method of automatically detecting microaneurysm based on multi-sieving convolutional neural network (CNN), includes the following steps of: A1), partitioning an image to be detected using random fern and obtaining an auxiliary channel image of the image according to a first partition result; and A2), inputting the auxiliary channel image obtained from step A1) and the image to a multi-sieving CNN training model to perform a detection and obtaining a microaneurysm detection result of the image. The process of establishing the training model includes: B1), using a current microaneurysm diagnostic report as samples and partitioning a lesion image in the microaneurysm diagnostic report using the random fern, and establishing the auxiliary channel image according to a second partition result; B2), comparing the obtained auxiliary channel image with a lesion-marked image of pixels, clarifying the samples according to a comparing result and performing the multi-sieving CNN training.

Abstract: In one aspect, provided herein are set of primers and use of the same for the detection of SNPs associated with diabetes. In certain embodiments, the primers used to detect SNP sites associated with diabetes comprise Primer Set 1 to Primer Set 47. The experiments show: the genotyping results of the SNP sites associated with diabetes can be accurately detected by the primers disclosed herein, and the risk of individuals can be comprehensively evaluated and the result is more accurate than the single site analysis. In addition, SNPs disclosed herein are verified as associated with type 2 diabetes and its complications, which are especially suitable for the prevention and individualized treatment for type 2 diabetes in East Asian, for example, in China.