Diagnostic assistance method, diagnostic assistance system, diagnostic assistance program, and computer-readable recording medium storing therein diagnostic assistance program for disease based on endoscopic image of digestive organ

A diagnostic assistance system for a disease based on an endoscopic image of a digestive organ with use of a convolutional neural network system, a diagnostic assistance system comprising:

• training the convolutional neural network program using:
• a first endoscopic image of the digestive organ;
• and
• at least one final diagnosis result of the positivity or the negativity for the disease in the digestive organ, a severity level, or information corresponding to an invasion depth of the disease, the final diagnosis result being corresponding to the first endoscopic image, wherein
• the trained convolutional neural network program outputs at least one of a probability of the positivity or the negativity for the disease in the digestive organ, the severity level, or a probability corresponding to the invasion depth of the disease, based on a second endoscopic image of the digestive organ, wherein
• a site of the digestive organ is the small bowel, the endoscopic image is a wireless capsule endoscopic image, and the trained convolutional neural network program outputs a probability score of a protruding lesion in the wireless capsule endoscopic image inputted from an endoscopic image input unit.

A diagnostic assistance method for a disease based on an endoscopic image of a digestive organ with use of a convolutional neural network system, comprising:

• training the convolutional neural network system using
• a first endoscopic image of the digestive organ, and
• at least one final diagnosis result of the positivity or the negativity for the disease in the digestive organ, a severity level, or information corresponding to an invasion depth of the disease, the final diagnosis result being corresponding to the first endoscopic image, wherein
• the trained convolutional neural network system outputs at least one of a probability of the positivity or the negativity for the disease in the digestive organ, the severity level, or a probability corresponding to the invasion depth of the disease, based on a second endoscopic image of the digestive organ, wherein
• the digestive organ is the small bowel, the endoscopic image is a wireless capsule endoscopic image, and the disease is the presence/absence of bleeding.

A diagnostic assistance method for a disease based on an endoscopic image of a digestive organ with use of a convolutional neural network system, comprising:

• training the convolutional neural network system using:
• a first endoscopic image of the digestive organ;
• and
• at least one final diagnosis result of the positivity or the negativity for the disease in the digestive organ, a severity level, or information corresponding to an invasion depth of the disease, the final diagnosis result being corresponding to the first endoscopic image, wherein
• the trained convolutional neural network system outputs at least one of a probability of the positivity or the negativity for the disease in the digestive organ, the severity level, or a probability corresponding to the invasion depth of the disease, based on a second endoscopic image of the digestive organ, wherein
• the digestive organ is the esophagus, the endoscopic image is a non-magnification endoscopic image or a magnification endoscopic image, and the disease is an invasion depth of a squamous cell carcinoma.

A diagnostic assistance method for a disease based on an endoscopic image of a digestive organ with use of a convolutional neural network system, comprising:

• training the convolutional neural network system using:
• a first endoscopic image of the digestive organ;
• and
• at least one final diagnosis result of the positivity or the negativity for the disease in the digestive organ, a severity level, or information corresponding to an invasion depth of the disease, the final diagnosis result being corresponding to the first endoscopic image, wherein
• the trained convolutional neural network system outputs at least one of a probability of the positivity or the negativity for the disease in the digestive organ, the severity level, or a probability corresponding to the invasion depth of the disease, based on a second endoscopic image of the digestive organ, characterized in that
• a site of the digestive organ is the pharynx, the endoscopic image is an esophagogastroduodenoscopic examination image, and the disease is a pharyngeal cancer.

A diagnostic assistance system for a disease based on an endoscopic image of a digestive organ with use of a convolutional neural network system, comprising:

• an endoscopic image input unit;
• an output unit;
• and a computer, in the computer a convolutional neural network being incorporated, wherein
• the computer includes:
• a first storage area that stores therein a first endoscopic image of the digestive organ;
• a second storage area that stores therein at least one final diagnosis result of the positivity or the negativity for the disease in the digestive organ, a severity level, or information corresponding to an invasion depth of the disease, the final diagnosis result being corresponding to the first endoscopic image;
• and
• a third storage area that stores therein a convolutional neural network program, wherein
• the convolutional neural network program
• is trained based on the first endoscopic image stored in the first storage area, and the final diagnosis result stored in the second storage area, and
• outputs to the output unit, based on a second endoscopic image of the digestive organ, the second endoscopic image being inputted from the endoscopic image input unit, at least one of a probability of the positivity or the negativity for the disease in the digestive organ, the severity level, or a probability corresponding to the information corresponding to the invasion depth of the disease, for a second endoscopic image, wherein
• a site of the digestive organ is the small bowel, the endoscopic image is a wireless capsule endoscopic image, and the trained convolutional neural network program outputs a probability score of a protruding lesion in the wireless capsule endoscopic image inputted from the endoscopic image input unit.

A diagnostic assistance system for a disease based on an endoscopic image of a digestive organ, comprising an endoscopic image input unit;

• an output unit;
• and a computer, in the computer a convolutional neural network being incorporated, wherein
• the computer includes:
• a first storage area that stores therein a first endoscopic image of the digestive organ;
• a second storage area that stores therein at least one final diagnosis result of the positivity or the negativity for the disease in the digestive organ, a severity level, or information corresponding to an invasion depth of the disease, the final diagnosis result being corresponding to the first endoscopic image;
• and
• a third storage area that stores therein a convolutional neural network program, wherein
• the convolutional neural network program
• is trained based on the first endoscopic image stored in the first storage area, and the final diagnosis result stored in the second storage area, and
• outputs to the output unit, based on a second endoscopic image of the digestive organ, the second endoscopic image being inputted from the endoscopic image input unit, at least one of a probability of the positivity or the negativity for the disease in the digestive organ, the severity level, or a probability corresponding to the information corresponding to the invasion depth of the disease, for a second endoscopic image, wherein
• a site of the digestive organ is the small bowel, the endoscopic image is a wireless capsule endoscopic image, and the trained convolutional neural network program displays in the second endoscopic image a probability of the presence/absence of bleeding as the disease.

A diagnostic assistance system for a disease based on an endoscopic image of a digestive organ, comprising an endoscopic image input unit;

• an output unit;
• and a computer, in the computer a convolutional neural network being incorporated, wherein
• the computer includes:
• a first storage area that stores therein a first endoscopic image of the digestive organ;
• a second storage area that stores therein at least one final diagnosis result of the positivity or the negativity for the disease in the digestive organ, a severity level, or information corresponding to an invasion depth of the disease, the final diagnosis result being corresponding to the first endoscopic image;
• and
• a third storage area that stores therein a convolutional neural network program, wherein
• the convolutional neural network program
• is trained based on the first endoscopic image stored in the first storage area, and the final diagnosis result stored in the second storage area, and
• outputs to the output unit, based on a second endoscopic image of the digestive organ, the second endoscopic image being inputted from the endoscopic image input unit, at least one of a probability of the positivity or the negativity for the disease in the digestive organ, the severity level, or a probability corresponding to the information corresponding to the invasion depth of the disease, for a second endoscopic image, wherein
• a site of the digestive organ is the esophagus, the endoscopic image is a non-magnification endoscopic image or a magnification endoscopic image, and the trained convolutional neural network program displays in the second image an invasion depth of a squamous cell carcinoma as the disease.

A diagnostic assistance system for a disease based on an endoscopic image of a digestive organ with use of a convolutional neural network system, comprising an endoscopic image input unit;

• an output unit;
• and a computer, in the computer a convolutional neural network being incorporated, wherein
• the computer includes:
• a first storage area that stores therein a first endoscopic image of the digestive organ;
• a second storage area that stores therein at least one final diagnosis result of the positivity or the negativity for the disease in the digestive organ, a severity level, or information corresponding to an invasion depth of the disease, the final diagnosis result being corresponding to the first endoscopic image;
• and
• a third storage area that stores therein a convolutional neural network program, wherein
• the convolutional neural network program
• is trained based on the first endoscopic image stored in the first storage area, and the final diagnosis result stored in the second storage area, and
• outputs to the output unit, based on a second endoscopic image of the digestive organ, the second endoscopic image being inputted from the endoscopic image input unit, at least one of a probability of the positivity or the negativity for the disease in the digestive organ, the severity level, or a probability corresponding to the information corresponding to the invasion depth of the disease, for a second endoscopic image, wherein
• a site of the digestive organ is the pharynx, the endoscopic image is an esophagogastroduodenoscopic examination image, and the disease is a pharyngeal cancer.