(19)
(11) EP 3 951 794 A8

(12) CORRECTED EUROPEAN PATENT APPLICATION
published in accordance with Art. 153(4) EPC

(15) Correction information:
Corrected version no 1 (W1 A1)

(48) Corrigendum issued on:
23.03.2022 Bulletin 2022/12

(43) Date of publication:
09.02.2022 Bulletin 2022/06

(21) Application number: 20784555.3

(22) Date of filing: 26.03.2020
(51) International Patent Classification (IPC): 
G16H 50/20(2018.01)
A61B 10/00(2006.01)
 A61B 5/00(2006.01)
(52) Cooperative Patent Classification (CPC):
A61B 5/00; G16H 50/20
(86) International application number:
PCT/JP2020/013684
(87) International publication number:
WO 2020/203651 (08.10.2020 Gazette 2020/41)
(84) Designated Contracting States:
AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR
Designated Extension States:
BA ME
Designated Validation States:
KH MA MD TN

(30) Priority: 29.03.2019 JP 2019067229
15.11.2019 JP 2019206765

(71) Applicants:
  • University of Tsukuba
    Ibaraki 305-8577 (JP)
  • Soinn Inc.
    Tokyo 1940004 (JP)
  • Life Science Institute, Inc.
    Tokyo 100-8251 (JP)

(72) Inventors:
  • FUJISAWA Yasuhiro
    Tsukuba-shi, Ibaraki 305-8577 (JP)
  • IWASHITA Kodai
    Machida-shi, Tokyo 194-0004 (JP)
  • ISHIDA Mitsuyoshi
    Tokyo 101-0047 (JP)

(74) Representative: Müller-Boré & Partner Patentanwälte PartG mbB 
Friedenheimer Brücke 21
80639 München
80639 München (DE)

   


(54) SKIN DISEASE ANALYZING PROGRAM, SKIN DISEASE ANALYZING METHOD, SKIN DISEASE ANALYZING DEVICE, AND SKIN DISEASE ANALYZING SYSTEM


(57) Provided are a skin disease analysis program, a skin disease analysis method, a skin disease analyzer, and a skin disease analysis system, which can analyze skin disease more accurately. The program according to the present invention is executed by a computer to execute a second step of predicting a kind of skin tumor for an image to be analyzed of skin tumor by a first-learned model that has machine learned from images of affected parts of various skin diseases in advance, and either one or both of a first step and a third step, in which the first step determines whether or not the image to be analyzed is an image of skin tumor by a skin disease determination engine, prior to the second step, and in a case where the determination result of the second step has been one kind of skin tumors that are easily mistaken for each other, the third step re-predicts the kind of skin tumor for the image to be analyzed by a second-learned model that has machine learned from images of affected parts of specific skin diseases including the skin tumors that are easily mistaken for each other.