7 research outputs found
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 ”m vs. neointima: 347.8 ±212.8) and the thoracic
aorta (intima: 16.3 ±8.7 ”m vs. neointima 359 ±117.8 ”m) and a significant
media reduction in the abdominal (352 ± 85 ”m) and thoracic (1453± 519 ”m)
parts bearing the stent graft when compared to non-stented controls from the
same sheep (abdominal: 581 ± 181 ”m; thoracic: 2205± 391”m). 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 PrÀvalenz des abdominellen und thorakalen Aortenaneurysmas
steigt durch zunehmendem demographischen Wandel sowie VerÀnderungen des
Lebensstiles. Die betroffenen Patienten haben hÀufig mehrere KomorbiditÀten
und die endovaskulÀre Aneurysmentherapie stellt daher eine attraktive, weil
minimal invasive, Therapiealternative zur elektiven Laparotomie oder
Thorakotomie dar. Allerdings ist eine regelmĂ€Ăige radiologische Nachsorge
notwendig, da auch Jahre nach Implantation ein spÀtes Stentgraftversagen
möglich ist. Studienziel: Die Langzeituntersuchung von Sicherheit und
BiokompatibilitÀt 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 geprĂŒft. Mikroskopische
Schnitte wurden mit HĂ€matoxylin-Eosin, Weigert HĂ€matoxylin-Phloxin-Safran und
anti- CD3 Antikörpern gefÀrbt um die Fremdkörperreaktion, die
Neointimahyperplasie, Mediaatrophie, Angiogenese und die allgemeine
EntzĂŒndungsreaktion nach der Stentimplantation zu studieren. Resultate Die
radiologische und makroskopische Untersuchung zeigte bis auf ein leichtes
Scalloping keine VerÀnderungen der Aorta nach Stentgraftimplantation. Die
mikroskopische Untersuchung zeigte eine signifikante Neointimahyperplasie
(Mittelwert± Standardabweichung) in der abdominellen (Intima: 19.5 ±9.3 ”m vs.
Neointima: 347.8 ±212.8) und thorakalen Aorta (Intima: 16.3 ±8.7 ”m vs.
Neointima 359 ±117.8 ”m) sowie eine signifikante Reduktion des
Mediadurchmessers in den abdominellen (352 ± 85 ”m) und thorakalen (1453± 519
”m) Abschnitten der Aorta, in die das Stentgraft implantiert worden war, im
Vergleich zu den Abschnitten ohne Stentgraft (abdominell: 581 ± 181 ”m;
thorakal: 2205± 391”m). Eine vorlÀufige Trendanalyse zeigte, dass die
MediaverÀnderungen mit einem höheren Rupturrisiko, VerÀnderungen in der Media-
Lamellen Anordnung und stÀrkerer CD3+ Infiltration korrellierten, wÀhrend
Neointimahyperplasie zwar mit Angiongenese jedoch nicht mit einem höheren
Rupturwert korreliert werden konnte. Interessanterweise zeigte sich, dass das
individuelle EntzĂŒndungsprofil mehr von dem gesunden Intima- und
Mediadurchmesser als vom Stentgraftdurchmesser oder dem VerhÀltnis von
Stentgraftdurchmesser zum GefĂ€Ădurchmesser abhing. Möglicherweise haben
genetische und epigenetische Entwicklungsfaktoren einen entscheidenden
Einfluss auf die individuelle EntzĂŒndungsreaktion nach Stentgraftimplantation.
Die an allen Stentgrafts beschriebenen Fremdkörpergranulome und Langhans-typ
Granulome zeigten, dass an der StentgraftoberflÀche eine andauernde,
chronische EntzĂŒndungreaktion mit Anteilen des angeborenen und adaptiven
Immunsystems stattfand. Schlussfolgerung: Die Langzeituntersuchung der
Sicherheit und BiokompatibilitÀt der AortenverÀnderungen nach Implantation des
untersuchten chrome-kobalt Stentgrafts identifizierte eine chronische
EntzĂŒndung mit Fremdkörperreaktion und die Atrophie und Ruptur der Media durch
chronische, mechanische SchĂ€digung als Hauptrisikofaktoren fĂŒr das
Langzeitversagen von Stentgraftimplantaten in der Aorta
Climate Change and Infectious Diseases in Megacities of the Indian Subcontinent: A Literature Review
Khan MH, KrĂ€mer A, PrĂŒfer-KrĂ€mer L. Climate Change and Infectious Diseases in Megacities of the Indian Subcontinent: A Literature Review. In: KrĂ€mer 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