Abstract: A method includes steps of: based on training sets of gastrointestinal images, using a predetermined machine learning algorithm to obtain a preliminary model; feeding preliminary validation sets of gastrointestinal images into the preliminary model to obtain estimation results; based on the estimation results, selecting, from the preliminary validation sets of gastrointestinal images, a series of successive images as a selected validation set of gastrointestinal images; based on the selected validation set of gastrointestinal images, tuning parameters of the preliminary model to result in a speed-determining model for determining a moving speed of an endoscope camera.

Abstract: Disclosed is a system comprising a dialysis machine for performing a dialysis treatment in a patient, and a computer system in communication with the dialysis machine. The computer system uses a trained ML model to generates a result that indicates the risk of the patient for developing heart failure during the dialysis treatment, and shows a warning sign on a display if the result indicates that the patient is at risk of heart failure.

Abstract: The present disclosure pertains to a method for diagnosing a middle ear disease comprising receiving one or more otoscopic image of a subject, pre-processing the one or more otoscopic image to obtain image data, inputting the image data to a trained network model, generating, using the trained network model, an output that characterizes the health of the subject with respect to the middle ear disease, and generating a diagnostic result based on the output, wherein the trained network model is based on a model selected from the group consisting of a MobileNetV2 model, a NASNetMobile model, and a DenseNet201 model.

Abstract: The present invention provides a computer-implanted method and system for diagnosing sudden sensorineural hearing loss (SSNHL) of an ear in a subject; comprising on one or more processors of the computer the steps of: testing the hearing of two ears of the subject, and calculating the difference between them; and comparing the difference with a threshold to diagnose the subject as SSNHL or not.