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Mammography screening supported by deep learning-based artificial intelligence (AI) solutions can potentially reduce workload without compromising breast cancer detection accuracy, but the site of deployment in the workflow might be crucial. This retrospective study compared three simulated AI-integrated screening scenarios with standard double reading with arbitration in a sample of 249 402 mammograms from a representative screening population. A commercial AI system replaced the first reader (scenario 1: integrated AI first ), the second reader (scenario 2: integrated AI second ), or both readers for triaging of low- and high-risk cases (scenario 3: integrated AI triage ). AI threshold values were chosen based partly on previous validation and setting the screen-read volume reduction at approximately 50% across scenarios. Detection accuracy measures were calculated. Compared with standard double reading, integrated AI first showed no evidence of a difference in accuracy metrics except for a higher arbitration rate (+0.99%, P < .001). Integrated AI second had lower sensitivity (-1.58%, P < .001), negative predictive value (NPV) (-0.01%, P < .001), and recall rate (-0.06%, P = .04) but a higher positive predictive value (PPV) (+0.03%, P < .001) and arbitration rate (+1.22%, P < .001). Integrated AI triage achieved higher sensitivity (+1.33%, P < .001), PPV (+0.36%, P = .03), and NPV (+0.01%, P < .001) but lower arbitration rate (-0.88%, P < .001). Replacing one or both readers with AI seems feasible; however, the site of application in the workflow can have clinically relevant effects on accuracy and workload. Keywords: Mammography, Breast, Neoplasms-Primary, Screening, Epidemiology, Diagnosis, Convolutional Neural Network (CNN) Supplemental material is available for this article. Published under a CC BY 4.0 license.

Objectives: To validate an AI system for standalone breast cancer detection on an entire screening population in comparison to first-reading breast radiologists., Materials and Methods: All mammography screenings performed between August 4, 2014, and August 15, 2018, in the Region of Southern Denmark with follow-up within 24 months were eligible. Screenings were assessed as normal or abnormal by breast radiologists through double reading with arbitration. For an AI decision of normal or abnormal, two AI-score cut-off points were applied by matching at mean sensitivity (AI sens ) and specificity (AI spec ) of first readers. Accuracy measures were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and recall rate (RR)., Results: The sample included 249,402 screenings (149,495 women) and 2033 breast cancers (72.6% screen-detected cancers, 27.4% interval cancers). AI sens had lower specificity (97.5% vs 97.7%; p < 0.0001) and PPV (17.5% vs 18.7%; p = 0.01) and a higher RR (3.0% vs 2.8%; p < 0.0001) than first readers. AI spec was comparable to first readers in terms of all accuracy measures. Both AI sens and AI spec detected significantly fewer screen-detected cancers (1166 (AI sens ), 1156 (AI spec ) vs 1252; p < 0.0001) but found more interval cancers compared to first readers (126 (AI sens ), 117 (AI spec ) vs 39; p < 0.0001) with varying types of cancers detected across multiple subgroups., Conclusion: Standalone AI can detect breast cancer at an accuracy level equivalent to the standard of first readers when the AI threshold point was matched at first reader specificity. However, AI and first readers detected a different composition of cancers., Clinical Relevance Statement: Replacing first readers with AI with an appropriate cut-off score could be feasible. AI-detected cancers not detected by radiologists suggest a potential increase in the number of cancers detected if AI is implemented to support double reading within screening, although the clinicopathological characteristics of detected cancers would not change significantly., Key Points: • Standalone AI cancer detection was compared to first readers in a double-read mammography screening population. • Standalone AI matched at first reader specificity showed no statistically significant difference in overall accuracy but detected different cancers. • With an appropriate threshold, AI-integrated screening can increase the number of detected cancers with similar clinicopathological characteristics., (© 2023. The Author(s).)

Objective: Focused assessment with sonography for trauma (FAST) is a valuable ultrasound procedure in emergency settings, and there is a need for evidence-based education in FAST to ensure competencies. Immersive virtual reality (IVR) is a progressive training modality gaining traction in the field of ultrasound training. IVR holds several economic and practical advantages to the common instructor-led FAST courses using screen-based simulation (SBS)., Methods: This prospective, interventional cohort study investigated whether training FAST using IVR unsupervised and out-of-hospital was non-inferior to a historical control group training at a 90 min SBS course in terms of developing FAST competencies in novices. Competencies were assessed in both groups using the same post-training simulation-based FAST test with validity evidence, and a non-inferiority margin of 2 points was chosen., Results: A total of 27 medical students attended the IVR course, and 27 junior doctors attended the SBS course. The IVR group trained for a median time of 117 min and scored a mean 14.2 ± 2.0 points, compared with a mean 13.7 ± 2.5 points in the SBS group. As the lower bound of the 95% confidence interval at 13.6 was within the range of the non-inferiority margin (11.7-13.7 points), training FAST in IVR for a median of 117 min was found non-inferior to training at a 90 min SBS course. No significant correlation was found between time spent in IVR and test scores., Conclusion: Within the limitations of the use of a historical control group, the results suggest that IVR could be an alternative to SBS FAST training and suitable for unsupervised, out-of-hospital courses in basic FAST competencies., Competing Interests: Conflict of interest K.J., J.D.L., R.O.J., and O.G. were all involved in development of the IVR module. R.O.J. is the chief executive officer and co-founder of VitaSim, which provided the virtual reality software used in this study. J.D.L. was previously employed by VitaSim as a part-time learning consultant. C.F. has voiced a previous VitaSim IVR module. Neither participated in the assessment of the participants’ US skills or data analysis. The remaining authors do not have any conflicts of interest to disclose., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Artificial intelligence (AI) systems are proposed as a replacement of the first reader in double reading within mammography screening. We aimed to assess cancer detection accuracy of an AI system in a Danish screening population., Methods: We retrieved a consecutive screening cohort from the Region of Southern Denmark including all participating women between Aug 4, 2014, and August 15, 2018. Screening mammograms were processed by a commercial AI system and detection accuracy was evaluated in two scenarios, Standalone AI and AI-integrated screening replacing first reader, with first reader and double reading with arbitration (combined reading) as comparators, respectively. Two AI-score cut-off points were applied by matching at mean first reader sensitivity (AI sens ) and specificity (AI spec ). Reference standard was histopathology-proven breast cancer or cancer-free follow-up within 24 months. Coprimary endpoints were sensitivity and specificity, and secondary endpoints were positive predictive value (PPV), negative predictive value (NPV), recall rate, and arbitration rate. Accuracy estimates were calculated using McNemar's test or exact binomial test., Results: Out of 272,008 screening mammograms from 158,732 women, 257,671 (94.7%) with adequate image data were included in the final analyses. Sensitivity and specificity were 63.7% (95% CI 61.6%-65.8%) and 97.8% (97.7-97.8%) for first reader, and 73.9% (72.0-75.8%) and 97.9% (97.9-98.0%) for combined reading, respectively. Standalone AI sens showed a lower specificity (-1.3%) and PPV (-6.1%), and a higher recall rate (+ 1.3%) compared to first reader (p < 0.0001 for all), while Standalone AI spec had a lower sensitivity (-5.1%; p < 0.0001), PPV (-1.3%; p = 0.01) and NPV (-0.04%; p = 0.0002). Compared to combined reading, Integrated AI sens achieved higher sensitivity (+ 2.3%; p = 0.0004), but lower specificity (-0.6%) and PPV (-3.9%) as well as higher recall rate (+ 0.6%) and arbitration rate (+ 2.2%; p < 0.0001 for all). Integrated AI spec showed no significant difference in any outcome measures apart from a slightly higher arbitration rate (p < 0.0001). Subgroup analyses showed higher detection of interval cancers by Standalone AI and Integrated AI at both thresholds (p < 0.0001 for all) with a varying composition of detected cancers across multiple subgroups of tumour characteristics., Conclusions: Replacing first reader in double reading with an AI could be feasible but choosing an appropriate AI threshold is crucial to maintaining cancer detection accuracy and workload., (© 2023. The Author(s).)

Purpose: Estimating the potential impact on infant mortality of increasing Bacille Calmette-Guérin (BCG) vaccination coverage and BCG scar prevalence., Methods: Guinea-Bissau Health and Demographic Surveillance System data on BCG vaccination coverage, scar status, and all-cause mortality were used for this study. Mortality risk (MR) by scar status was assessed in Cox models providing adjusted mortality rate ratios (aMRRs). Distributions were fitted for survival, vaccination coverage, and scar prevalence. Models for 12-month mortality were calculated. We utilized World Bank data on birth rates and mortality rates to assess the potential global impact of optimizing BCG vaccination programs., Results: BCG coverage was 81% and scar prevalence 42% among 2-month-old infants, and the 1- to 12-month scar/no scar aMRR was 0.40 (0.22, 0.76). Modeling 2-month 99% vaccination coverage with 95% developing scars would change the 1- to 12-month MR by -8% (-21%, +12%). Globally, the reduction in the MR between 1- and 12-month would be -14% (-14%, -15%), corresponding to -208,075 (-214,453, -204,023) fewer infant deaths/year., Conclusions: We confirmed previous observations: having a BCG scar markedly reduces infant MR. Increasing current global 2-month BCG vaccination coverage from 76% to 99%, and scar prevalence among vaccinated infants from 52% to 95% might reduce global infant mortality by >200,000 deaths/year. Thus, optimizing BCG vaccination programs to focus on increasing early BCG vaccination coverage and the overall scar prevalence would have major public health benefits., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Sarah W. Stougaard reports financial support was provided by Knud Højgaards Fond. Sarah W. Stougaard also reports financial support was provided by Augustinus Fonden., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)