Objective: Artificial intelligence-based computer-aided diagnosis (AI-CAD) is increasingly used in mammography. While the continuous scores of AI-CAD have been related to malignancy risk, the understanding of how to interpret and apply these scores remains limited. We investigated the positive predictive values (PPVs) of the abnormality scores generated by a deep learning-based commercial AI-CAD system and analyzed them in relation to clinical and radiological findings. Materials and Methods: From March 2020 to May 2022, 656 breasts from 599 women (mean age 52.6 ± 11.5 years, including 0.6% [4/599] high-risk women) who underwent mammography and received positive AI-CAD results (Lunit Insight MMG, abnormality score ≥ 10) were retrospectively included in this study. Univariable and multivariable analyses were performed to evaluate the associations between the AI-CAD abnormality scores and clinical and radiological factors. The breasts were subdivided according to the abnormality scores into groups 1 (10–49), 2 (50–69), 3 (70–89), and 4 (90–100) using the optimal binning method. The PPVs were calculated for all breasts and subgroups. Results: Diagnostic indications and positive imaging findings by radiologists were associated with higher abnormality scores in the multivariable regression analysis. The overall PPV of AI-CAD was 32.5% (213/656) for all breasts, including 213 breast cancers, 129 breasts with benign biopsy results, and 314 breasts with benign outcomes in the follow-up or diagnostic studies. In the screening mammography subgroup, the PPVs were 18.6% (58/312) overall and 5.1% (12/235), 29.0% (9/31), 57.9% (11/19), and 96.3% (26/27) for score groups 1, 2, 3, and 4, respectively. The PPVs were significantly higher in women with diagnostic indications (45.1% [155/344]), palpability (51.9% [149/287]), fatty breasts (61.2% [60/98]), and certain imaging findings (masses with or without calcifications and distortion). Conclusion PPV increased with increasing AI-CAD abnormality scores. The PPVs of AI-CAD satisfied the acceptable PPV range according to Breast Imaging-Reporting and Data System for screening mammography and were higher for diagnostic mammography.

BACKGROUND: To evaluate risk factors of redisplacement and remind surgeons of key factors regarding conservative treatment of distal radius fracture. METHODS: A total of 132 patients who received conservative treatment for distal radius fractures between March 2008 and February 2011 were included in this study. Radial inclination, radial length, volar tilting angle, ulnar variance, fragment translation, and presence of dorsal metaphyseal comminution were measured on the X-rays taken immediately after reduction, one week after injury during the first follow-up outpatient clinic visit, and after the gain of radiological union. Secondary displacement was defined as a loss of reduction during the follow-up period, and was divided into 'early' and 'late' categories. We analyzed the influence of initial displacement radiologic variables, dorsal cortex comminution, and patient age on the development of secondary displacement. RESULTS: Development of secondary displacement was significantly associated only with initial displacement radiologic variables (p < 0.001), development of the late secondary displacement was significantly associated with age (p = 0.005), and initial displacement radiologic variables were associated significantly with a serial increase in ulnar variance (p = 0.003). CONCLUSIONS: Greater displacement on the initial radiographs indicates a higher possibility of development for secondary displacement, and older patients had a higher probability of late secondary displacement development. Furthermore, dorsal comminutions did not affect secondary displacement directly.
Adult ; Aged ; Aged, 80 and over ; Cohort Studies ; Female ; Fracture Fixation, Internal ; Fractures, Comminuted/radiography/therapy ; Humans ; Male ; Middle Aged ; Prognosis ; Radius Fractures/epidemiology/radiography/*therapy ; *Splints ; Young Adult