Abstract: An image pickup system includes an input/output modeling section 24, the input/output modeling section 24 creating, as a population, an image group obtained when a specific target is photographed, (access image), and generating an inference model by using, as teacher data, sequential images selected from the image group created as the population, based on whether the specific target can be accessed, wherein each image of the image group is associated with date and time information and/or position information, and the input/output modeling section 24 generates an inference model for determining based on the date and time information and/or the position information whether a process to the specific target is good or bad.

Abstract: A diagnostic system, method, and a computer-readable storage medium for determining a severity of a gastric condition, such as gastritis, in a subject are disclosed. The diagnostic system includes a processor that obtains an image of a stomach of the subject collected by an endoscope. The processor can compare the subject image with reference normal and abnormal stomach images. If the subject image is abnormal, the processor can generate a subject abnormality image indicative of the differences between the subject image and the normal image. By comparing the generated subject abnormality image with reference abnormality images representative of different severity levels of gastritis, the processor can diagnose the subject as having a particular severity level of gastritis.

Abstract: A diagnostic system, method, and a computer-readable storage medium for determining a severity of a gastric condition, such as gastritis, in a subject are disclosed. The diagnostic system includes a processor that obtains an image of a stomach of the subject collected by an endoscope. The processor can compare the subject image with reference normal and abnormal stomach images. If the subject image is abnormal, the processor can generate a subject abnormality image indicative of the differences between the subject image and the normal image. By comparing the generated subject abnormality image with reference abnormality images representative of different severity levels of gastritis, the processor can diagnose the subject as having a particular severity level of gastritis.

Abstract: A diagnostic system, method, and a computer-readable storage medium for determining a severity of a gastric condition, such as gastric cancer, in a subject are disclosed. The diagnostic system includes a processor that can obtain various images of a stomach of the subject including wavelength images and generate difference images from the wavelength images. The processor can compare the subject images with reference images representative of different severity levels of gastric cancer, or can input the subject images into a learned model trained using the reference images stored in the database to extract a feature pattern corresponding to a severity of gastric cancer to diagnose the subject as having a particular severity level of gastric cancer.

Abstract: A system that can include a processor that obtains voice data of a subject. The processor can compare the voice data of the subject with reference normal and abnormal voice data. If the voice data of the subject is abnormal, the processor can generate an abnormal subject voice data that is indicative of differences between the subject voice data and the normal voice data. By comparing the generated abnormal subject voice data with the reference normal and abnormal voice data, the processor can determine what condition the subject may be suffering from and the severity of that condition.

Abstract: An image pickup system includes an input/output modeling section 24, the input/output modeling section 24 creating, as a population, an image group obtained when a specific target is photographed, (access image), and generating an inference model by using, as teacher data, sequential images selected from the image group created as the population, based on whether the specific target can be accessed, wherein each image of the image group is associated with date and time information and/or position information, and the input/output modeling section 24 generates an inference model for determining based on the date and time information and/or the position information whether a process to the specific target is good or bad.