ObjectivesTo evaluate the incidence, management, and outcome of visceral artery aneurysms (VAA) over one decade.Methods233 patients with 253 VAA were analyzed according to location, diameter, aneurysm type, aetiology, rupture, management, and outcome.ResultsVAA were localized at the splenic artery, coeliac trunk, renal artery, hepatic artery, superior mesenteric artery, and other locations. The aetiology was degenerative, iatrogenic after medical procedures, connective tissue disease, and others. The rate of rupture was much higher in pseudoaneurysms than true aneurysms (76.3 % vs.3.1 %). Fifty-nine VAA were treated by intervention (n = 45) or surgery (n = 14). Interventions included embolization with coils or glue, covered stents, or combinations of these. Thirty-five cases with ruptured VAA were treated on an emergency basis. There was no difference in size between ruptured and non-ruptured VAA. After interventional treatment, the 30-day mortality was 6.7 % in ruptured VAA compared to no mortality in non-ruptured cases. Follow-up included CT and/or MRI after a mean period of 18.0 ± 26.8 months. The current status of the patient was obtained by a structured telephone survey.ConclusionsPseudoaneurysms of visceral arteries have a high risk for rupture. Aneurysm size seems to be no reliable predictor for rupture. Interventional treatment is safe and effective for management of VAA.Key Points• Diagnosis of visceral artery aneurysms is increasing due to CT and MRI.• Diameter of visceral arterial aneurysms is no reliable predictor for rupture.• False aneurysms/pseudoaneurysms and symptomatic cases need emergency treatment.• Interventional treatment is safe and effective.

Background: performing minimally invasive surgery requires training and visual-spatial intelligence. the aim of our study was to examine the impact of visual-spatial perception and additional mental training on the simulated laparoscopic knot-tying task performed by surgical novices.Methods: a total of 40 medical students randomly assigned to two groups underwent two sessions of laparoscopic basic training on a vr simulator (simsurgery ® , oslo, norway). the variables time and tip trajectory (total path length of the instrument tip trajectory) were used to assess the performance of the intracorporeal knot-tying task using a laparoscopic nissen fundoplication model. the experimental group completed additional mental practice during the interval between the two training sessions. all performed a cube subtest of a standard intelligence test (I-s-t 2000 r) to evaluate visualspatial ability.Results: all participants achieved an improvement in time (t = 9.861; p < 0.001) and tip trajectory (t = 6.833; p < 0.001) in the second training session. High scores on the visualspatial test correlated with a faster performance (r = -0.557; p < 0.001) and more precise movements (r = -0.377; p = 0.016).comparison of the two groups did not show any statistical significant differences in the parameters time and tip trajectory.Conclusions: visual-spatial intelligence tested by a cube test correlated with simulated laparoscopic knot-tying skills in surgical novices. additional mental practice did not improve the overall knot-tying performance. further studies are therefore required to determine whether mental practice might be beneficial for experienced laparoscopic surgeons or for more complex tasks.

BackgroundHealth apps for the screening and diagnosis of mental disorders have emerged in recent years on various levels (eg, patients, practitioners, and public health system). However, the diagnostic quality of these apps has not been (sufficiently) tested so far.ObjectiveThe objective of this pilot study was to investigate the diagnostic quality of a health app for a broad spectrum of mental disorders and its dependency on expert knowledge.MethodsTwo psychotherapists, two psychology students, and two laypersons each read 20 case vignettes with a broad spectrum of mental disorders. They used a health app (Ada—Your Health Guide) to get a diagnosis by entering the symptoms. Interrater reliabilities were computed between the diagnoses of the case vignettes and the results of the app for each user group.ResultsOverall, there was a moderate diagnostic agreement (kappa=0.64) between the results of the app and the case vignettes for mental disorders in adulthood and a low diagnostic agreement (kappa=0.40) for mental disorders in childhood and adolescence. When psychotherapists applied the app, there was a good diagnostic agreement (kappa=0.78) regarding mental disorders in adulthood. The diagnostic agreement was moderate (kappa=0.55/0.60) for students and laypersons. For mental disorders in childhood and adolescence, a moderate diagnostic quality was found when psychotherapists (kappa=0.53) and students (kappa=0.41) used the app, whereas the quality was low for laypersons (kappa=0.29). On average, the app required 34 questions to be answered and 7 min to complete.ConclusionsThe health app investigated here can represent an efficient diagnostic screening or help function for mental disorders in adulthood and has the potential to support especially diagnosticians in their work in various ways. The results of this pilot study provide a first indication that the diagnostic accuracy is user dependent and improvements in the app are needed especially for mental disorders in childhood and adolescence.

No abstract

BackgroundClear-cell renal cell carcinoma (ccRCC) is common and associated with substantial mortality. TNM stage and histopathological grading have been the sole determinants of a patient’s prognosis for decades and there are few prognostic biomarkers used in clinical routine. Management of ccRCC involves multiple disciplines such as urology, radiology, oncology, and pathology and each of these specialties generates highly complex medical data. Here, artificial intelligence (AI) could prove extremely powerful to extract meaningful information to benefit patients.ObjectiveIn the study, we developed and evaluated a multimodal deep learning model (MMDLM) for prognosis prediction in ccRCC.Design, Setting, and ParticipantsTwo mixed cohorts of non-metastatic and metastatic ccRCC patients were used: (1) The Cancer Genome Atlas cohort including 230 patients and (2) the Mainz cohort including 18 patients with ccRCC. For each of these patients, we trained the MMDLM on multiscale histopathological images, CT/MRI scans, and genomic data from whole exome sequencing.Outcome Measurements and Statistical AnalysisOutcome measurements included Harrell’s concordance index (C-index) and also various performance parameters for predicting the 5-year survival status (5YSS). Different visualization techniques were used to make our model more transparent.ResultsThe MMDLM showed great performance in predicting the prognosis of ccRCC patients with a mean C-index of 0.7791 and a mean accuracy of 83.43%. Training on a combination of data from different sources yielded significantly better results compared to when only one source was used. Furthermore, the MMDLM’s prediction was an independent prognostic factor outperforming other clinical parameters.InterpretationMultimodal deep learning can contribute to prognosis prediction in ccRCC and potentially help to improve the clinical management of this disease.Patient SummaryAn AI-based computer program can analyze various medical data (microscopic images, CT/MRI scans, and genomic data) simultaneously and thereby predict the survival time of patients with renal cancer.