Citizens in the Lab: Performance and Validation of eDNA Results

Citizen Science has traditionally been applied in biodiversity monitoring, as the approach holds the potential for conducting large-scale data collections. However, involving citizens in more than data collection is still in its infancy. In this paper, we present the results of an ongoing citizen science project that expands the partnership between citizens and researchers by involving citizens in several parts of the scientific process. In the project, citizens first conduct sampling in the field, followed by analysis of their samples in our university laboratory. Finally, participants are interpreting the results of the laboratory analyses in collaboration with the researcher. The project aims to evaluate the presence of marine animals by monitoring the DNA left behind by the organisms in the environment (eDNA), using samples from the years 2017 and 2018. We found that citizens can carry out eDNA surveillance with an average success rate of 72% (where the success rate is defined as passing both the negative and positive control test) and that their data is of similar quality as a trained researcher and concur with known species distributions. Engaging and training citizen scientists in advanced laboratory analysis, such as the monitoring of eDNA in water samples, has promising applications for large-scale national monitoring of marine species that can be used in governmental mapping and monitoring efforts

The added effect of artificial intelligence on physicians’ performance in detecting thoracic pathologies on CT and chest X-ray:A systematic review

Our systematic review investigated the additional effect of artificial intelligence-based devices on human observers when diagnosing and/or detecting thoracic pathologies using different diagnostic imaging modalities, such as chest X-ray and CT. Peer-reviewed, original research articles from EMBASE, PubMed, Cochrane library, SCOPUS, and Web of Science were retrieved. Included articles were published within the last 20 years and used a device based on artificial intelligence (AI) technology to detect or diagnose pulmonary findings. The AI-based device had to be used in an observer test where the performance of human observers with and without addition of the device was measured as sensitivity, specificity, accuracy, AUC, or time spent on image reading. A total of 38 studies were included for final assessment. The quality assessment tool for diagnostic accuracy studies (QUADAS-2) was used for bias assessment. The average sensitivity increased from 67.8% to 74.6%; specificity from 82.2% to 85.4%; accuracy from 75.4% to 81.7%; and Area Under the ROC Curve (AUC) from 0.75 to 0.80. Generally, a faster reading time was reported when radiologists were aided by AI-based devices. Our systematic review showed that performance generally improved for the physicians when assisted by AI-based devices compared to unaided interpretation