DEVICE AND METHOD FOR DIAGNOSING INFECTION FOR MICROSCOPIC AGGREGATION TEST

To accurately and quickly perform judgment in a microscopic aggregation test.SOLUTION: An infection diagnosis device includes: a regression unit which includes a first deep learning device, calculates a characteristic amount of a microscope image acquired from the outside by the first deep learning device, and regresses a free bacteria ratio from the characteristic amount; and a classification unit which includes a second deep learning device and classifies the presence or absence of infection from a plurality of sets of (dilution degree and free bacterial ratio) data in one test serum by the second deep learning device. The regression unit uses appropriate microscope image data as input data and executes learning to the first deep learning device using free bacteria ratio data attached to each of the microscope image data as output data. The plurality of sets of (dilution degree and free bacterial ratio) data in each of the plurality of test sera is used as input data and a label representing infection/non-infection of each test serum is used as output data to perform learning to the second deep learning device.SELECTED DRAWING: Figure 1 【課題】正確かつ迅速に、顕微鏡下凝集試験における判断を行う。【解決手段】感染診断装置は、第１の深層学習器を含み、当該第１の深層学習器により、外部より取得される顕微鏡画像の特徴量を計算し、それら特徴量からフリー菌割合を回帰する回帰部であって、適宜の顕微鏡画像データを入力データとし、それら顕微鏡画像データの夫々に付せられるフリー菌割合データを出力データとして、第１の深層学習器に対する学習を実行する回帰部と、第２の深層学習器を含み、当該第２の深層学習器により、一つの被検血清における複数組の（希釈度、フリー菌割合）データから、感染の有無を分類する分類部であって、複数の被検血清の各々における複数組の（希釈度、フリー菌割合）データを入力データとし、各々の被検血清についての感染／非感染を表すラベルを出力データとして、第２の深層学習器に対する学習を実行する分類部とを備える。【選択図】図１