Patient‐dependent risk factors for wound infection after skin surgery: a systematic review and meta‐analysis

Postoperative wound infection in dermatologic surgery causes impaired wound healing, poor cosmetic outcome and increased morbidity. Patients with a high‐risk profile may benefit from perioperative antibiotic prophylaxis. The objective of this systematic review was to identify risk factors for surgical site infection after dermatologic surgery. In this article, we report findings on patient‐dependent risk factors. The literature search included MEDLINE, EMBASE, CENTRAL and trial registers. We performed meta‐analysis, if studies reported sufficient data to calculate risk ratios with 95% confidence intervals. Study quality was assessed according to the Newcastle‐Ottawa‐Scale. Seventeen observational studies that analysed 31213 surgical wounds were eligible for inclusion. Fourteen studies qualified for meta‐analysis. Nine studies showed good, three fair and five poor methodological quality. The reported incidence of surgical site infection ranged from 0.96% to 8.70%. Meta‐analysis yielded that male gender and immunosuppression were significantly associated with higher infection rates. There was a tendency towards a higher infection risk for patients with diabetes, without statistical significance. Meta‐analysis did not show different infection rates after excision of squamous cell carcinoma or basal cell carcinoma, but studies were substantially heterogenous. There was no significant association between risk for wound infection and smoking, age over 60 years, oral anti‐aggregation or anti‐coagulation or excision of malignant melanoma. In conclusion, the risk for surgical site infection in dermatologic surgery is low. Infection rates were increased significantly in male as well as immunosuppressed patients and non‐significantly in diabetics

tracking of adipose tissue grafts with cadmium-telluride quantum dots

Background Fat grafting, or lipofilling, represent frequent clinically used entities. The fate of these transplants is still not predictable, whereas only few animal models are available for further research. Quantum dots (QDs) are semiconductor nanocrystals which can be conveniently tracked in vivo due to photoluminescence. Methods Fat grafts in cluster form were labeled with cadmium-telluride (CdTe)-QD 770 and transplanted subcutaneously in a murine in vivo model. Photoluminescence levels were serially followed in vivo. Results Tracing of fat grafts was possible for 50 days with CdTe-QD 770. The remaining photoluminescence was 4.9%±2.5% for the QDs marked fat grafts after 30 days and 4.2%± 1.7% after 50 days. There was no significant correlation in the relative course of the tracking signal, when vital fat transplants were compared to non-vital graft controls. Conclusions For the first-time fat grafts were tracked in vivo with CdTe-QDs. CdTe-QDs could offer a new option for in vivo tracking of fat grafts for at least 50 days, but do not document vitality of the grafts

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