Abstract:Objective To investigate the application value of breast X-ray intelligent detection system based on deep learning technology in clinical palpation negative breast tumor diagnosis. Methods A total of 322 patients with palpation negative breast cancer in Shaanxi cancer hospital from January 2014 to December 2016 were retrospectively collected.MammoWorksTM breast intelligent detection system was used to analyze the mammograms of all patients.The sensitivity,positive predictive value,the number of false positive markers in each image and the influence of clinicopathological characteristics on the detection efficiency of MammoWorksTM breast intelligent detection system before and after the algorithm update were analyzed,and the effects of different versions of MammoWorksTM breast intelligent detection system were compared. Results The diagnostic sensitivity of different versions of MammoWorksTM (previous version:3.5.2.6; updated version:3.5.4.43) in clinical palpation negative breast tumors were 78.57% (253/322) and 95.65% (308/322),respectively.The positive predictive value (calculated by the number of markers) were 55.32% (801/1 448) and 44.27% (1 120/2 530),respectively.The number of false-positive markers in each image were 0.50 (0.25，0.75) and 1.50 (0.25，0.75),1.00 (0.75，1.50),respectively.After deep learning,the algorithm was updated,the detection sensitivity increased (χ2=35.926,P<0.001),the positive predictive value decreased (χ2=45.02,P<0.001),and the false-positive markers in each image increased compared with the previous version (Z=-14.105,P<0.001).The consistency of the two versions was poor (kappa=0.234).In different breast density and lesion type groups,the sensitivity of the early version of the system was significantly different (χ2=12.198,P<0.05;χ2=16.235,P<0.05).After the system update,the sensitivity difference in different clinicopathological characteristics was eliminated,and there was no significant difference between the groups (P>0.05). Conclusion The detection sensitivity of MammoWorksTM system is higher than before through algorithm updating,which has certain application value in clinical palpation negative breast cancer diagnosis.However,due to more false-positive markers in each image,it still needs to be further optimized to improve the detection efficiency.