eine histologische Analyse von chrom-kobalt Stentprothesen in der Schafsaorta

Abdominal and thoracic aneurysms have a rising prevalence due to demographic changes. Patients undergoing elective surgery often present with multiple comorbidities. Endovascular aneurysm repair using stent grafts is an attractive, minimally invasive, alternative to conventional open repair but regular radiological surveillance is necessary since long-term stent graft failure can occur years after implantation. Objectives: Test the safety and biocompatibility of a chrome-cobalt custom made stent graft at 6 months implantation in the thoracic and abdominal sheep aorta. Methods: Stent grafts were analyzed radiologically and macroscopically for patency, stenosis and migration in situ. Microscopic cross sections were stained with hematoxylin- eosin, Weigert hematoxylin-phloxine-saffron and immunohistochemically for CD3+ T-lymphocytes to assess the chronic foreign body reaction, neointima formation, media atrophy, angiogenesis and inflammatory reaction. Results: Radiologic and macroscopic evaluation showed no significant changes such as endoleakage, migration or occlusion and found only slight scalloping. Microscopy analysis showed significant neointima hyperplasia (mean± SD) in the abdominal (intima: 19.5 ±9.3 µm vs. neointima: 347.8 ±212.8) and the thoracic aorta (intima: 16.3 ±8.7 µm vs. neointima 359 ±117.8 µm) and a significant media reduction in the abdominal (352 ± 85 µm) and thoracic (1453± 519 µm) parts bearing the stent graft when compared to non-stented controls from the same sheep (abdominal: 581 ± 181 µm; thoracic: 2205± 391µm). Trend analysis showed that media changes could correlate with a higher rupture score, changes in the media lamella unit structure and higher CD3+ cell counts, while neointima hyperplasia could correlate with angiogenesis but not rupture. Interestingly, the inflammatory reaction pattern depended more on the animal- specific, healthy intima and media thickness than on the stent diameter or stent/ vessel ratio. This might demonstrate the importance of genetic and developmental factors on inflammatory response in every individual after stent grafting. In addition, all stents showed multinucleated foreign body giant cell granulomas and secondary Langhans-type granulomas covering the stent surface, demonstrating an ongoing, chronic inflammation driven by innate and adaptive immunity. This aortic animal model evaluated the safety and biocompatibility of a long-term chrome-cobalt stent graft and identified chronic inflammation of the foreign body type and atrophic media rupture due to chronic mechanical injury as the main risk factors for long-term stent failure.Die weltweite Prävalenz des abdominellen und thorakalen Aortenaneurysmas steigt durch zunehmendem demographischen Wandel sowie Veränderungen des Lebensstiles. Die betroffenen Patienten haben häufig mehrere Komorbiditäten und die endovaskuläre Aneurysmentherapie stellt daher eine attraktive, weil minimal invasive, Therapiealternative zur elektiven Laparotomie oder Thorakotomie dar. Allerdings ist eine regelmäßige radiologische Nachsorge notwendig, da auch Jahre nach Implantation ein spätes Stentgraftversagen möglich ist. Studienziel: Die Langzeituntersuchung von Sicherheit und Biokompatibilität eines chrome-kobalt Stentgrafts nach sechs Monaten Implantation in die thorakale und abdominale Schafaorta. Methoden: Sechs Monate nach Implantation wurden die Stentgrafts radiologisch und makroskopisch in situ auf Migrations- und Durchflussverhalten geprüft. Mikroskopische Schnitte wurden mit Hämatoxylin-Eosin, Weigert Hämatoxylin-Phloxin-Safran und anti- CD3 Antikörpern gefärbt um die Fremdkörperreaktion, die Neointimahyperplasie, Mediaatrophie, Angiogenese und die allgemeine Entzündungsreaktion nach der Stentimplantation zu studieren. Resultate Die radiologische und makroskopische Untersuchung zeigte bis auf ein leichtes Scalloping keine Veränderungen der Aorta nach Stentgraftimplantation. Die mikroskopische Untersuchung zeigte eine signifikante Neointimahyperplasie (Mittelwert± Standardabweichung) in der abdominellen (Intima: 19.5 ±9.3 µm vs. Neointima: 347.8 ±212.8) und thorakalen Aorta (Intima: 16.3 ±8.7 µm vs. Neointima 359 ±117.8 µm) sowie eine signifikante Reduktion des Mediadurchmessers in den abdominellen (352 ± 85 µm) und thorakalen (1453± 519 µm) Abschnitten der Aorta, in die das Stentgraft implantiert worden war, im Vergleich zu den Abschnitten ohne Stentgraft (abdominell: 581 ± 181 µm; thorakal: 2205± 391µm). Eine vorläufige Trendanalyse zeigte, dass die Mediaveränderungen mit einem höheren Rupturrisiko, Veränderungen in der Media- Lamellen Anordnung und stärkerer CD3+ Infiltration korrellierten, während Neointimahyperplasie zwar mit Angiongenese jedoch nicht mit einem höheren Rupturwert korreliert werden konnte. Interessanterweise zeigte sich, dass das individuelle Entzündungsprofil mehr von dem gesunden Intima- und Mediadurchmesser als vom Stentgraftdurchmesser oder dem Verhältnis von Stentgraftdurchmesser zum Gefäßdurchmesser abhing. Möglicherweise haben genetische und epigenetische Entwicklungsfaktoren einen entscheidenden Einfluss auf die individuelle Entzündungsreaktion nach Stentgraftimplantation. Die an allen Stentgrafts beschriebenen Fremdkörpergranulome und Langhans-typ Granulome zeigten, dass an der Stentgraftoberfläche eine andauernde, chronische Entzündungreaktion mit Anteilen des angeborenen und adaptiven Immunsystems stattfand. Schlussfolgerung: Die Langzeituntersuchung der Sicherheit und Biokompatibilität der Aortenveränderungen nach Implantation des untersuchten chrome-kobalt Stentgrafts identifizierte eine chronische Entzündung mit Fremdkörperreaktion und die Atrophie und Ruptur der Media durch chronische, mechanische Schädigung als Hauptrisikofaktoren für das Langzeitversagen von Stentgraftimplantaten in der Aorta

Climate Change and Infectious Diseases in Megacities of the Indian Subcontinent: A Literature Review

Khan MH, Krämer A, Prüfer-Krämer L. Climate Change and Infectious Diseases in Megacities of the Indian Subcontinent: A Literature Review. In: Krämer A, Khan MH, Kraas F, eds. Health In Megacities And Urban Areas. Contributions to Statistics. Heidelberg: Physica; 2011: 135-152

Superior skin cancer classification by the combination of human and artificial intelligence

Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification). Findings: Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5% Interpretation: Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems. (C) 2019 The Author(s). Published by Elsevier Ltd

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

Background: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. Methods: Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. Findings: Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). Interpretation: Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001). (C) 2019 The Author(s). Published by Elsevier Ltd

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists. Methods: We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%-100%) and 64.4% (range: 22.5%-92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%-86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%. Interpretation: For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

Background: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. Methods: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. Interpretation: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity. (C) 2019 The Author(s). Published by Elsevier Ltd