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Introduction: Accurate tools to inform individual prognosis in patients with autosomal dominant polycystic kidney disease (ADPKD) are lacking. Here, we report an artificial intelligence (AI)-generated method for routinely measuring total kidney volume (TKV)., Methods: An ensemble U-net algorithm was created using the nnUNet approach. The training and internal cross-validation cohort consisted of all 1.5T magnetic resonance imaging (MRI) data acquired using 5 different MRI scanners (454 kidneys, 227 scans) in the CYSTic consortium, which was first manually segmented by a single human operator. As an independent validation cohort, we utilized 48 sequential clinical MRI scans with reference results of manual segmentation acquired by 6 individual analysts at a single center. The tool was then implemented for clinical use and its performance analyzed., Results: The training or internal validation cohort was younger (mean age 44.0 vs. 51.5 years) and the female-to-male ratio higher (1.2 vs. 0.94) compared to the clinical validation cohort. The majority of CYSTic patients had PKD1 mutations (79%) and typical disease (Mayo Imaging class 1, 86%). The median DICE score on the clinical validation data set between the algorithm and human analysts was 0.96 for left and right kidneys with a median TKV error of -1.8%. The time taken to manually segment kidneys in the CYSTic data set was 56 (±28) minutes, whereas manual corrections of the algorithm output took 8.5 (±9.2) minutes per scan., Conclusion: Our AI-based algorithm demonstrates performance comparable to manual segmentation. Its rapidity and precision in real-world clinical cases demonstrate its suitability for clinical application., (Crown Copyright © 2023 Published by Elsevier Inc. on behalf of the International Society of Nephrology.)

Objective: To test the performance of a novel machine learning-based breast density tool. The tool utilizes a convolutional neural network to predict the BI-RADS based density assessment of a study. The clinical density assessments of 33,000 mammographic examinations (164,000 images) from one academic medical center (Site A) were used for training., Materials and Methods: This was an IRB approved HIPAA compliant study performed at two academic medical centers. The validation data set was composed of 500 studies from one site (Site A) and 700 from another (Site B). At Site A, each study was assessed by three breast radiologists and the majority (consensus) assessment was used as truth. At Site B, if the tool agreed with the clinical reading, then it was considered to have correctly predicted the clinical reading. In cases where the tool and the clinical reading disagreed, then the study was evaluated by three radiologists and the consensus reading was used as the clinical reading., Results: For the classification into the four categories of the Breast Imaging Reporting and Data System (BI-RADS®), the AI classifier had an accuracy of 84.6% at Site A and 89.7% at Site B. For binary classification (dense vs. non-dense), the AI classifier had an accuracy of 94.4% at Site A and 97.4% at Site B. In no case did the classifier disagree with the consensus reading by more than one category., Conclusions: The automated breast density tool showed high agreement with radiologists' assessments of breast density., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Sven Schoenherr, Martin Seebass, MingDe Lin and Malte Westerhoff are employees of Visage Imaging, owner and potential distributor of the described software tool., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: SARS-CoV-2 entry in human cells depends on angiotensin-converting enzyme 2, which can be upregulated by inhibitors of the renin-angiotensin system (RAS). We aimed to test our hypothesis that discontinuation of chronic treatment with ACE-inhibitors (ACEIs) or angiotensin II receptor blockers (ARBs) mitigates the course o\f recent-onset COVID-19., Methods: ACEI-COVID was a parallel group, randomised, controlled, open-label trial done at 35 centres in Austria and Germany. Patients aged 18 years and older were enrolled if they presented with recent symptomatic SARS-CoV-2 infection and were chronically treated with ACEIs or ARBs. Patients were randomly assigned 1:1 to discontinuation or continuation of RAS inhibition for 30 days. Primary outcome was the maximum sequential organ failure assessment (SOFA) score within 30 days, where death was scored with the maximum achievable SOFA score. Secondary endpoints were area under the death-adjusted SOFA score (AUC SOFA ), mean SOFA score, admission to the intensive care unit, mechanical ventilation, and death. Analyses were done on a modified intention-to-treat basis. This trial is registered with ClinicalTrials.gov, NCT04353596., Findings: Between April 20, 2020, and Jan 20, 2021, 204 patients (median age 75 years [IQR 66-80], 37% females) were randomly assigned to discontinue (n=104) or continue (n=100) RAS inhibition. Within 30 days, eight (8%) of 104 died in the discontinuation group and 12 (12%) of 100 patients died in the continuation group (p=0·42). There was no significant difference in the primary endpoint between the discontinuation and continuation group (median [IQR] maximum SOFA score 0·00 (0·00-2·00) vs 1·00 (0·00-3·00); p=0·12). Discontinuation was associated with a significantly lower AUC SOFA (0·00 [0·00-9·25] vs 3·50 [0·00-23·50]; p=0·040), mean SOFA score (0·00 [0·00-0·31] vs 0·12 [0·00-0·78]; p=0·040), and 30-day SOFA score (0·00 [10-90th percentile, 0·00-1·20] vs 0·00 [0·00-24·00]; p=0·023). At 30 days, 11 (11%) in the discontinuation group and 23 (23%) in the continuation group had signs of organ dysfunction (SOFA score ≥1) or were dead (p=0·017). There were no significant differences for mechanical ventilation (10 (10%) vs 8 (8%), p=0·87) and admission to intensive care unit (20 [19%] vs 18 [18%], p=0·96) between the discontinuation and continuation group., Interpretation: Discontinuation of RAS-inhibition in COVID-19 had no significant effect on the maximum severity of COVID-19 but may lead to a faster and better recovery. The decision to continue or discontinue should be made on an individual basis, considering the risk profile, the indication for RAS inhibition, and the availability of alternative therapies and outpatient monitoring options., Funding: Austrian Science Fund and German Center for Cardiovascular Research., Competing Interests: Declaration of interests AB received research funding from Pfizer and Medtronic as well as speaker honoraria from Bayer, Boehringer Ingelheim, Edwards, Medtronic and Novartis. KDR received a research grant from Daiichi-Sankyo Europe GmbH. UM received fees for participation in a data and safety monitoring board from Thermosome. All other authors declare no competing interests., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Objective: Navigation systems enable neurosurgeons to guide operations with imaging data. Sensor-based neuronavigation uses an electromagnetic field and sensors to measure the positions of the patient's brain anatomy and the surgical instruments. The aim of this investigation was to determine the accuracy level of sensor-based tracking in a large patient collection., Methods: This study covers 250 patients operated upon during a continuous 5.5-year period. The patients had a wide range of indications and surgical procedures. The operations were performed with a direct current (DC) pulsed sensor-based electromagnetic navigation system. Four kinds of errors were measured: the fiducial registration error (FRE), the target registration error (TRE), brain shift, and the position error (PE). These errors were calculated for five subgroups of indications: target determination and trajectory guidance, functional navigation, skull base and neurocranium, determination of resection volume, and transnasal and transsphenoidal access., Results: The overall mean FRE was 1.66mm (+/-0.61mm). The overall mean TREs were 1.33mm (+/-0.51mm) centroid and 1.59mm (+/-0.57mm) lesional. The overall mean brain shift for applicable cases was 1.61mm (+/-1.14mm). The overall mean PE was 0.92mm (+/-0.54mm)., Conclusions: By and large, modern sensor-based neuronavigation operates within an acceptable and commonplace degree of error. However, the neurosurgeon must remain critical in cases of small lesions, and must exert caution not to introduce further interference from metal objects or electromagnetic devices.