Does expert information affect citizens' attitudes toward Corona policies? Evidence from Germany.

Information provided by experts is believed to play a key role in shaping attitudes towards policy responses to the COVID-19 pandemic. This paper uses a survey experiment to assess whether providing citizens with expert information about the health risk of COVID-19 and the economic costs of lockdown measures affects their attitudes towards these policies. Our findings show that providing respondents with information about COVID-19 fatalities among the elderly raises support for lockdown measures, while information about their economic costs decreases support. However, different population subgroups react differently. Men and younger respondents react more sensitively to information about lockdown costs, while women and older respondents are more susceptible towards information regarding fatality rates. Strikingly, our results are entirely driven by respondents who underestimate the fatality of COVID-19, who represent a majority.

Deep Learning on Oral Squamous Cell Carcinoma Ex Vivo Fluorescent Confocal Microscopy Data: A Feasibility Study.

BACKGROUND: Ex vivo fluorescent confocal microscopy (FCM) is a novel and effective method for a fast-automatized histological tissue examination. In contrast, conventional diagnostic methods are primarily based on the skills of the histopathologist. In this study, we investigated the potential of convolutional neural networks (CNNs) for automatized classification of oral squamous cell carcinoma via ex vivo FCM imaging for the first time. MATERIAL AND METHODS: Tissue samples from 20 patients were collected, scanned with an ex vivo confocal microscope immediately after resection, and investigated histopathologically. A CNN architecture (MobileNet) was trained and tested for accuracy. RESULTS: The model achieved a sensitivity of 0.47 and specificity of 0.96 in the automated classification of cancerous tissue in our study. CONCLUSION: In this preliminary work, we trained a CNN model on a limited number of ex vivo FCM images and obtained promising results in the automated classification of cancerous tissue. Further studies using large sample sizes are warranted to introduce this technology into clinics.

Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy.

Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the forefront of artificial intelligence (AI) revolution, conventional pathology, which commonly relies on examination of tissue samples on a glass slide, is falling behind in leveraging this technology. On the other hand, ex vivo confocal laser scanning microscopy (ex vivo CLSM), owing to its digital workflow features, has a high potential to benefit from integrating AI tools into the assessment and decision-making process. Aim of this work was to explore a preliminary application of CNN in digitally stained ex vivo CLSM images of cutaneous squamous cell carcinoma (cSCC) for automated detection of tumor tissue. Thirty-four freshly excised tissue samples were prospectively collected and examined immediately after resection. After the histologically confirmed ex vivo CLSM diagnosis, the tumor tissue was annotated for segmentation by experts, in order to train the MobileNet CNN. The model was then trained and evaluated using cross validation. The overall sensitivity and specificity of the deep neural network for detecting cSCC and tumor free areas on ex vivo CLSM slides compared to expert evaluation were 0.76 and 0.91, respectively. The area under the ROC curve was equal to 0.90 and the area under the precision-recall curve was 0.85. The results demonstrate a high potential of deep learning models to detect cSCC regions on digitally stained ex vivo CLSM slides and to distinguish them from tumor-free skin.

In vivo reflectance confocal microscopy of wounds: feasibility of intraoperative basal cell carcinoma margin assessment.

BACKGROUND: In vivo reflectance confocal microscopy (RCM) is well established in non-melanoma skin cancer detection and screening. However, there is no sufficient validation regarding intraoperatively obtained images of wound margins. A reliable and fast resection margin detection is of high clinical relevance. Hence, we aimed to investigate feasibility and validity of in vivo RCM imaging for wound margins assessment compared with standard skin surface imaging and the gold standard histopathology. METHODS: A surgical incision through the center of a large basal cell carcinoma (BCC) affected area in the head and face region was performed. After removing half of the tumor, the wound margins of the remaining half as well as the corresponding skin surface were scanned with an in vivo RCM. A total of 50 wound margin images with BCC, 50 images of BCC-free margins and the corresponding skin surface images from 50 patients were compared with each other and with histopathological findings. Presence of confocal diagnostic criteria for BCC in images was analyzed. RESULTS: An overall sensitivity and specificity in detection of BCC in wound margins was 88.5%, and 91.7% compared to skin surface imaging and 97.8% and 90.7%, respectively, compared to histopathology. We identified all known confocal patterns of healthy skin and BCC in wound margin scans: damage of the epidermal layer above the lesion and cellular pleomorphism, elongated and monomorphic basaloid nuclei, nuclear polarization, an increased number of dilated blood vessels with high leukocyte traffic, inflammatory cells. CONCLUSIONS: The accuracy of in vivo RCM imaging of wound margins is comparable with a standard skin surface imaging. The intraoperative detection of BCC areas in wound margins is as precise as the standard skin imaging and may be supportive for surgical interventions.