12 research outputs found

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

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    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

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    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

    Spectral characterization of intraoperative renal perfusion using hyperspectral imaging and artificial intelligence

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    Abstract Accurate intraoperative assessment of organ perfusion is a pivotal determinant in preserving organ function e.g. during kidney surgery including partial nephrectomy or kidney transplantation. Hyperspectral imaging (HSI) has great potential to objectively describe and quantify this perfusion as opposed to conventional surrogate techniques such as ultrasound flowmeter, indocyanine green or the subjective eye of the surgeon. An established live porcine model under general anesthesia received median laparotomy and renal mobilization. Different scenarios that were measured using HSI were (1) complete, (2) gradual and (3) partial malperfusion. The differences in spectral reflectance as well as HSI oxygenation (StO2) between different perfusion states were compelling and as high as 56.9% with 70.3% (± 11.0%) for “physiological” vs. 13.4% (± 3.1%) for “venous congestion”. A machine learning (ML) algorithm was able to distinguish between these perfusion states with a balanced prediction accuracy of 97.8%. Data from this porcine study including 1300 recordings across 57 individuals was compared to a human dataset of 104 recordings across 17 individuals suggesting clinical transferability. Therefore, HSI is a highly promising tool for intraoperative microvascular evaluation of perfusion states with great advantages over existing surrogate techniques. Clinical trials are required to prove patient benefit

    Development of biotissue training models for anastomotic suturing in pancreatic surgery

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    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

    Development of biotissue training models for anastomotic suturing in pancreatic surgery

    No full text
    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
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