A reporting and analysis framework for structured evaluation of COVID-19 clinical and imaging data

The COVID-19 pandemic has worldwide individual and socioeconomic consequences. Chest computed tomography has been found to support diagnostics and disease monitoring. A standardized approach to generate, collect, analyze, and share clinical and imaging information in the highest quality possible is urgently needed. We developed systematic, computer-assisted and context-guided electronic data capture on the FDA-approved mint LesionTM software platform to enable cloud-based data collection and real-time analysis. The acquisition and annotation include radiological findings and radiomics performed directly on primary imaging data together with information from the patient history and clinical data. As proof of concept, anonymized data of 283 patients with either suspected or confirmed SARS-CoV-2 infection from eight European medical centers were aggregated in data analysis dashboards. Aggregated data were compared to key findings of landmark research literature. This concept has been chosen for use in the national COVID-19 response of the radiological departments of all university hospitals in Germany

Development of biotissue training models for anastomotic suturing in pancreatic surgery

Background: Anastomotic suturing is the Achilles heel of pancreatic surgery. Especially in laparoscopic and robotically assisted surgery, the pancreatic anastomosis should first be trained outside the operating room. Realistic training models are therefore needed. Methods: Models of the pancreas, small bowel, stomach, bile duct, and a realistic training torso were developed for training of anastomoses in pancreatic surgery. Pancreas models with soft and hard tex-tures, small and large ducts were incrementally developed and evaluated. Experienced pancreatic sur-geons (n = 44) evaluated haptic realism, rigidity, fragility of tissues, and realism of suturing and knot tying. Results: In the iterative development process the pancreas models showed high haptic realism and highest realism in suturing (4.6 & PLUSMN; 0.7 and 4.9 & PLUSMN; 0.5 on 1-5 Likert scale, soft pancreas). The small bowel model showed highest haptic realism (4.8 & PLUSMN; 0.4) and optimal wall thickness (0.1 & PLUSMN; 0.4 on -2 to +2 Likert scale) and suturing behavior (0.1 & PLUSMN; 0.4). The bile duct models showed optimal wall thickness (0.3 & PLUSMN; 0.8 and 0.4 & PLUSMN; 0.8 on -2 to +2 Likert scale) and optimal tissue fragility (0 & PLUSMN; 0.9 and 0.3 & PLUSMN; 0.7). Conclusion: The biotissue training models showed high haptic realism and realistic suturing behavior. They are suitable for realistic training of anastomoses in pancreatic surgery which may improve patient outcomes.Surgical oncolog

Development of biotissue training models for anastomotic suturing in pancreatic surgery

Background: Anastomotic suturing is the Achilles heel of pancreatic surgery. Especially in laparoscopic and robotically assisted surgery, the pancreatic anastomosis should first be trained outside the operating room. Realistic training models are therefore needed. Methods: Models of the pancreas, small bowel, stomach, bile duct, and a realistic training torso were developed for training of anastomoses in pancreatic surgery. Pancreas models with soft and hard textures, small and large ducts were incrementally developed and evaluated. Experienced pancreatic surgeons (n = 44) evaluated haptic realism, rigidity, fragility of tissues, and realism of suturing and knot tying. Results: In the iterative development process the pancreas models showed high haptic realism and highest realism in suturing (4.6 ± 0.7 and 4.9 ± 0.5 on 1–5 Likert scale, soft pancreas). The small bowel model showed highest haptic realism (4.8 ± 0.4) and optimal wall thickness (0.1 ± 0.4 on −2 to +2 Likert scale) and suturing behavior (0.1 ± 0.4). The bile duct models showed optimal wall thickness (0.3 ± 0.8 and 0.4 ± 0.8 on −2 to +2 Likert scale) and optimal tissue fragility (0 ± 0.9 and 0.3 ± 0.7). Conclusion: The biotissue training models showed high haptic realism and realistic suturing behavior. They are suitable for realistic training of anastomoses in pancreatic surgery which may improve patient outcomes