Abstract: An atrial fibrillation prediction system is provided. The atrial fibrillation prediction system includes an electrocardiogram obtaining unit and a non-transitory machine-readable medium. The non-transitory machine-readable medium is configured for storing a program which is executed by a processing unit to obtain a prediction result. The program includes a reference database obtaining module, a reference feature selecting module, a training module, a target feature selecting module and a comparing module.

Abstract: A method for training a chromosome recognition model includes: identifying objects on a karyotype image, obtaining a mask and a minimal bounding box of each of the chromosome objects, and obtaining an organized image that includes a set of organized chromosome objects; generating a simulated metaphase image in which the chromosome objects are randomly reorganized; detecting the plurality of chromosome objects on the simulated metaphase image; obtaining a recalibrated image in which the chromosome objects are separated from one another, so as to train the chromosome recognition model for identifying feature of chromosome objects included in an image.

Abstract: A method for building a heart rhythm classification model that is used to classify a heart rhythm of a person is provided. 12-lead ECG datasets are used to train a neural network model that includes multiple bidirectional LSTM layers. The bidirectional LSTM layers enable the neural network model to analyze the 12-lead ECG datasets in different aspects, so as to enhance classification accuracy.

Abstract: A system for real-time determination of the hand bone age using a personal device essentially include: a cloud computing platform storing a first marked database, an artificial neural network-based bone age model, and a comparison logic, wherein the first marked database at least has a hand bone image and the corresponding feature marking data; and a to-be-compared image providing device for downloading a to-be-compared hand bone image from a cloud-based to-be-compared image database. A personal device can be used to obtain a to-be-compared hand bone image from the to-be-compared image providing device and upload this image to the cloud computing platform in order for the cloud computing platform to find, through comparison, the hand bone image in the bone age model that is the most similar to the uploaded image and then transmit to the personal device the interpretation data corresponding to the hand bone image found.

Abstract: The present disclosure provides a bone age assessment and height prediction system including an image capturing unit and a non-transitory machine readable medium. The image capturing unit is for obtaining a target x-ray image data of a subject. The non-transitory machine-readable medium is for storing a program for assessing the development of the bones of a hand and the bone age of the subject, and predicting the adult height of the subject when executed by a processing unit. Therefore, the bone age assessment and height prediction system of the present disclosure can effectively improve the accuracy and sensitivity of the bone age assessment and the height prediction, and the time for assessing the bone age and predicting the height can be further shorten.

Abstract: A system for real-time determination of the hand bone age using a personal device essentially include: a cloud computing platform storing a first marked database, an artificial neural network-based bone age model, and a comparison logic, wherein the first marked database at least has a hand bone image and the corresponding feature marking data; and a to-be-compared image providing device for downloading a to-be-compared hand bone image from a cloud-based to-be-compared image database. A personal device can be used to obtain a to-be-compared hand bone image from the to-be-compared image providing device and upload this image to the cloud computing platform in order for the cloud computing platform to find, through comparison, the hand bone image in the bone age model that is the most similar to the uploaded image and then transmit to the personal device the interpretation data corresponding to the hand bone image found.

Abstract: A method for training a chromosome recognition model includes: identifying objects on a karyotype image, obtaining a mask and a minimal bounding box of each of the chromosome objects, and obtaining an organized image that includes a set of organized chromosome objects; generating a simulated metaphase image in which the chromosome objects are randomly reorganized; detecting the plurality of chromosome objects on the simulated metaphase image; obtaining a recalibrated image in which the chromosome objects are separated from one another, so as to train the chromosome recognition model for identifying feature of chromosome objects included in an image.

Abstract: A cloud-based transaction system and method capable of providing a neural network training model in a supervised state perform training with different training programs on a plurality of pieces of data and thus obtain different training models to not only allow a client to conduct transactions on a third-party transaction platform from a remote end but also allow the client to download the different training models according to transaction results under the supervision of a supervision unit to allow the client to compare the accuracy of the different training models, thereby enhancing the accuracy of the training models.

Abstract: A liver fibrosis assessment model includes following establishing steps. A reference database is obtained, wherein the reference database includes a plurality of reference blood test data. A preprocessing step of the blood test data is performed. A feature extracting step is performed, wherein the feature extracting step is for extracting at least one eigenvalue according to the reference database. A normalizing step of the blood test data is performed. A classifying step is performed, wherein the classifying step is for achieving a convergence of the normalized reference blood test data by using a gradient boosting algorithm so as to obtain the liver fibrosis assessment model. The liver fibrosis assessment model is used to assess whether a subject suffers from liver fibrosis and predict a degree of liver fibrosis of the subject.

Abstract: A chromosome abnormality detecting system includes an image capturing unit and a non-transitory machine readable medium. The image capturing unit is for obtaining a target metaphase chromosomes image of a subject. The non-transitory machine readable medium storing a program which, when executed by at least one processing unit, determines whether the subject has a chromosome abnormality when executed by a processing unit. The program includes a reference database obtaining module, a reference image transforming module, a reference preliminary classifying module, a reference feature selecting module, a training module, a target image transforming module, a target preliminary classifying module, a target feature selecting module and a comparing module.

Abstract: An assisted detection system of breast tumor includes an image capturing unit and a non-transitory machine readable medium. The non-transitory machine readable medium storing a program which, when executed by at least one processing unit, determines a breast tumor type of the subject and predicts a probability of a tumor location of the subject. The program includes a reference database obtaining module, a first image preprocessing module, an autoencoder module, a classifying module, a second image preprocessing module and a comparing module.

Abstract: An assisted detection system of breast tumor includes an image capturing unit and a non-transitory machine readable medium. The non-transitory machine readable medium storing a program which, when executed by at least one processing unit, determines a breast tumor type of the subject and predicts a probability of a tumor location of the subject. The program includes a reference database obtaining module, a first image preprocessing module, an autoencoder module, a classifying module, a second image preprocessing module and a comparing module.

Abstract: A prediction system for grouping hepatocellular carcinoma includes an image capturing unit and a non-transitory machine readable medium. The non-transitory machine readable medium storing a program which, when executed by at least one processing unit, predicts a hepatocellular carcinoma group of the subject patient with hepatocellular carcinoma. The program includes a reference database obtaining module, a first image preprocessing module, a feature selecting module, a classifying module, a second image preprocessing module and a comparing module.