Skin cancer is currently the most common type of cancer among Caucasians. The increase in life expectancy, along with new diagnostic tools and treatments for skin cancer, has resulted in unprecedented changes in patient care and has generated a great burden on healthcare systems. Early detection of skin tumors is expected to reduce this burden. Artificial intelligence (AI) algorithms that support skin cancer diagnoses have been shown to perform at least as well as dermatologists’ diagnoses. Recognizing the need for clinically and economically efficient means of diagnosing skin cancers at early stages in the primary care attention, we developed an efficient computer-aided diagnosis (CAD) system to be used by primary care physicians (PCP). Additionally, we developed a smartphone application with a protocol for data acquisition (i.e., photographs, demographic data and short clinical histories) and AI algorithms for clinical and dermoscopic image classification. For each lesion analyzed, a report is generated, showing the image of the suspected lesion and its respective Heat Map; the predicted probability of the suspected lesion being melanoma or malignant; the probable diagnosis based on that probability; and a suggestion on how the lesion should be managed. The accuracy of the dermoscopy model for melanoma was 89.3%, and for the clinical model, 84.7% with 0.91 and 0.89 sensitivity and 0.89 and 0.83 specificity, respectively. Both models achieved an area under the curve (AUC) above 0.9. Our CAD system can screen skin cancers to guide lesion management by PCPs, especially in the contexts where the access to the dermatologist can be difficult or time consuming. Its use can enable risk stratification of lesions and/or patients and dramatically improve timely access to specialist care for those requiring urgent attention.

Artificial intelligence (AI) based on convolutional neural networks (CNNs) has recently made great advances in dermatology with respect to the classification and malignancy prediction of skin diseases. In this article, we demonstrate how we have used a similar technique to build a mobile application to classify skin diseases captured by patients with their personal smartphone cameras. We used a CNN classifier to distinguish four subtypes of dermatological diseases the patients might have (“pigmentation changes and superficial infections”, “inflammatory diseases and eczemas”, “benign tumors, cysts, scars and callous”, and “suspected lesions”) and their severity in terms of morbidity and mortality risks, as well as the kind of medical consultation the patient should seek. The dataset used in this research was collected by the Department of Telemedicine of Albert Einstein Hospital in Sao Paulo and consisted of 146.277 skin images. In this paper, we show that our CNN models with an overall average classification accuracy of 79% and a sensibility of above 80% implemented in personal smartphones have the potential to lower the frequency of skin diseases and serve as an advanced tracking tool for a patient’s skin-lesion history.

Important initiatives, such as the Convention on Biological Diversity's (CBD) Aichi targets, the United Nations' 2030 Agenda for Sustainable Development (and its Sustainable Development Goals) highlight the urgent need to stop the continuous and increasing loss of biodiversity. That requires an increase in the knowledge that will allow for sustainable use of natural resources. To accomplish that, detailed studies are needed to evaluate multiple species and regions. These studies demand great effort from professionals, searching for species and/or observing their behavior. In this case, the use of new monitoring devices could be beneficial in data collection and identification, optimizing the specialist effort to detect and observe species in-situ. With the advance of technology platforms for developing connected devices and sensors, associated with the evolution of the Internet of Things (IoT) concepts, and the advances of unmanned aerial vehicles (UAVs) and Wireless sensor networks (WSN), new scenarios in biodiversity studies are possible. The technology available now could allow studies applying relatively cheaper sensors with long-range (approx. 15 km), low power, low bit rate communication and up to 10-year battery life, using a Low Power Wide Area Network (LPWAN) and with capacity to run bio-acoustic or image processing detection. Platforms like Raspberry Pi or any other with signal processing capabilities can be applied (Hodgkinson and Young 2016). Sensor technologies protocols applied in IoT networks are usually simple and flexible. Common semantics and metadata definitions are necessary to extract information and representations to construct complex networks. Some of these metadata definitions can be adopted from the current Darwin Core schema. However, Darwin Core evolved based on enterprise technologies (i.e. XML) and relational database definitions, that usually need machines with significant bandwidth to transmit data. Today the technology scenario is taking another route, going from centralized to distributed architectures, occasionally applying non-relational and distributed databases, ready to deal with synchronization and eventual consistency problems. These distributed databases are usually employed to construct complex networks, where relation restrictions are not mandatory or, sometimes, even desired (Baggio et al. 2016). With these new techniques becoming a reality in biodiversity conservation studies, new metadata definitions are necessary. Those new metadata need to standardize and create a shared vocabulary that includes requirements for devices information exchange, data analytics, and model generation. Also, these new definitions could aggregate the Essential Biodiversity Variables (EBVs) concepts, that aim to identify the minimum of variables that can be used to inform scientists, managers and decision makers (Haase et al. 2018). For this reason, we propose the insertion of EBV definitions in the construction of sensor integration metadata and models characterization inside the Darwin Core metadata definitions (Fig. 1).