Information processing device and medical image system

Patent metadata

Field	Value
Publication number	US-12198797-B2
Application number	US-202117318309-A
Country	US
Kind code	B2
Filing date	May 12, 2021
Priority date	May 15, 2020
Publication date	Jan 14, 2025
Grant date	Jan 14, 2025

Abstract

Provided is an information processing device that is interposed between an imaging control device and an image management device in communication. The information processing device including an abnormality detector and a first hardware processor. The abnormality detector performs detection of an abnormality in a medical image received from the imaging control device. The first hardware processor that determines, based on a result of the detection by the abnormality detector, content or a destination of transmission of the medical image or the result of the detection.

First claim

What is claimed is:

1. A medical image system comprising:
an image management device;
an information processing device interposed between an imaging control device and the image management device in communication,
the information processing device including
an abnormality detector that performs detection of an abnormality in a medical image received from the imaging control device; and
a first hardware processor that determines, based on a result of the detection by the abnormality detector, content or a destination of transmission of the medical image or the result of the detection, wherein the first hardware processor determines a priority for displaying the medical image among a plurality of medical images based on the result of the detection, assigns the priority to the medical image, and transmits the medical image and the priority associated with the medical image to the image management device, and

wherein the image management device includes:
a storage that stores the plurality of medical images transmitted from the information processing device and priorities associated with the respective plurality of medical images; and
a second hardware processor that determines an order of the plurality of medical images stored in the storage on a list to be displayed on a display based on the priorities associated with the plurality of medical images stored in the storage.

2. The medical image system according to claim 1 ,
wherein the first hardware processor determines whether to send the medical image to the image management device based on the result of the detection.

3. The medical image system according to claim 1 ,
wherein the first hardware processor determines, based on the result of the detection, a device to which the medical image or the result of the detection is transmitted.

4. The medical image system according to claim 1 ,
wherein the first hardware processor determines a medical worker who is to be requested to interpret the medical image, based on the detection of the result and an attribute of the medical worker.

5. The medical image system according to claim 4 ,
wherein the attribute of the medical worker includes at least one of a specialty, an image interpretation ability, a fatigue level, and availability.

6. The medical image system according to claim 1 ,
wherein the abnormality detector generates data on at least one of a name of a lesion, a position of the lesion, a size of the lesion, a category of the lesion, a malignancy level, and a malignancy level map from the medical image.

7. The medical image system according to claim 1 further comprising:
an additional examination determiner that presents necessity of an additional examination or re-imaging, and an item to be examined in the additional examination or the re-imaging concerning the medical image.

8. The medical image system according to claim 7 ,
wherein the additional examination determiner presents at least one of a modality, a region to be imaged, and an imaging method.

Assignees

• Konica Minolta Inc

Inventors

• Minami Hirotake
• Katsuhara Shinsuke
• Ito Ryohei
• Iida Nodoka
• Shimizu Amai
• Watanabe Ryoichi

Classifications

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/0895

  Weakly supervised learning, e.g. semi-supervised or self-supervised learning · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N20/00

  Machine learning · CPC title

• G16H10/60

  for patient-specific data, e.g. for electronic patient records · CPC title