Evidence-Based Drone Innovation & Research for Healthcare

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 6276

Special Issue Editors


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Guest Editor
Institute of Pharmaceutical Science, Department of Pharmacy, Faculty of Life Sciences & Medicine, King’s College London, 150 Stamford Street, London SE1 9NH, UK
Interests: delivery of medicines by drone; the maintenance of quality of medicines and medical products by drone; exploration of the relationship between solid state properties and the performance of pharmaceutical materials and medicines
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Tec-Connection, Oberlohnstrasse 3, D78467 Konstanz, Germany
Interests: laboratory robotics; materials logistics in the laboratory; system engineering; technology transfer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Drone operations associated with the delivery of medicines, medical products and healthcare-related samples are increasing at an exponential rate worldwide. However, full uptake of such services for the benefit of mankind is restricted by a number of barriers that the research community are seeking to alleviate. Thus, the scope of this Special Issue is to report novel research findings which tackle and remove the obstacles that prevent the use of drones for the improvement of human health. These barriers are associated with meeting aviation and healthcare regulatory standards, the maintenance of the quality of the medicine, the integration of the drone service within business-as-usual medical logistics, the engagement of the community, the development of robust use cases and the generation and sharing of relevant data.

Therefore, the editors of this Special Issue seek papers which cover successful drone operations in hard-to-reach communities, developments in safety and aircraft technologies which are focused on meeting the requirements of medical logistics between hospitals, clinics and, where possible, directly to the point-of-care and the patient. Novel studies which are associated with maintenance of the medicine, medical product or patient sample quality and stability are welcome. Additionally, the submission of papers which evaluate how patients and communities interact with drone medical logistics services is encouraged. Research showing how drone platforms may be instrumented to allow the testing of patients at the point-of-care, within their communities, are also welcome.

All contributions to the Special Issue must include thorough experimental design and the appropriate sampling to allow the statistical evaluation of the research findings. All papers are encouraged to adhere to the FAIR principles of generating experimental data, these are findability, accessibility, interoperability and reusability. In particular, all papers reporting results based on the analysis of captured data are urged to include the DOI (Digital Object Identifier) for where these data can be accessed. Useful links concerning the generation and curation of data: FAIR Principles and FAIR principles for software DOI 10.15497/RDA00068.

We look forward to receiving your contributions.

Dr. Paul Royall
Dr. Patrick Courtney
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • evidence based
  • FAIR data
  • point of care
  • healthcare drone ontologies
  • medicine drone delivery
  • healthcare logistics
  • UAV for human health
  • medicine quality
  • medicine stability
  • drug packaging
  • medicine compliance
  • patient samples

Published Papers (2 papers)

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Research

15 pages, 2398 KiB  
Article
Mapping the Urban Environments of Aedes aegypti Using Drone Technology
by Kenia Mayela Valdez-Delgado, Octavio Garcia-Salazar, David A. Moo-Llanes, Cecilia Izcapa-Treviño, Miguel A. Cruz-Pliego, Gustavo Y. Domínguez-Posadas, Moisés O. Armendáriz-Valdez, Fabián Correa-Morales, Luis Alberto Cisneros-Vázquez, José Genaro Ordóñez-González, Ildefonso Fernández-Salas and Rogelio Danis-Lozano
Drones 2023, 7(9), 581; https://doi.org/10.3390/drones7090581 - 15 Sep 2023
Cited by 3 | Viewed by 3062
Abstract
Aedes aegypti is widely distributed worldwide and is the main vector mosquito for dengue, one of the most important infectious diseases in middle- and low-income countries. The landscape composition and vegetation cover determine appropriate environments for this mosquito to breed, and it is [...] Read more.
Aedes aegypti is widely distributed worldwide and is the main vector mosquito for dengue, one of the most important infectious diseases in middle- and low-income countries. The landscape composition and vegetation cover determine appropriate environments for this mosquito to breed, and it is fundamental to define the most affordable methodology to understand these landscape variables in urban environments. The proposed methodology integrated drone technologies and traditional entomological surveillance to strengthen our knowledge about areas suitable for Ae. aegypti infestation. We included an analysis using the vegetation indexes, NDVI and NDVIRe, and their association with Ae. aegypti larvae and adults in houses from the El Vergel neighborhood Tapachula, Chiapas, Mexico. We used drone technology to obtain high-resolution photos and performed multispectral orthomosaic constructions for the data of vegetation indexes with a kernel density analysis. A negative binomial regression was performed to determine the association between the numbers of Ae. aegypti larvae and adults with the kernel density based on NDVI and NDVIRe. Medium and high values of kernel density of NDVIRe (both p-value < 0.05) and NDVI (both p-value < 0.05) were associated with a higher amount of mosquito adults per houses. The density of Ae. aegypti larvae per house did not show an association with medium and high values of NDVIRe (both p-value > 0.05) and NDVI (both p-value > 0.05). The vegetation indexes, NDVI and NDVIRe, have potential as precise predictors of Ae. aegypti adult mosquito circulation in urban environments. Drone technology can be used to map and obtain landscape characteristics associated with mosquito abundance in urban environments. Full article
(This article belongs to the Special Issue Evidence-Based Drone Innovation & Research for Healthcare)
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30 pages, 2876 KiB  
Article
Fight against Future Pandemics: UAV-Based Data-Centric Social Distancing, Sanitizing, and Monitoring Scheme
by Rajesh Gupta, Pronaya Bhattacharya, Sudeep Tanwar, Ravi Sharma, Fayez Alqahtani, Amr Tolba, Florin-Emilian Țurcanu and Maria Simona Raboaca
Drones 2022, 6(12), 381; https://doi.org/10.3390/drones6120381 - 27 Nov 2022
Cited by 1 | Viewed by 2605
Abstract
The novel coronavirus disease-2019 (COVID-19) has transformed into a global health concern, which resulted in human containment and isolation to flatten the curve of mortality rates of infected patients. To leverage the massive containment strategy, fifth-generation (5G)-envisioned unmanned aerial vehicles (UAVs) are used [...] Read more.
The novel coronavirus disease-2019 (COVID-19) has transformed into a global health concern, which resulted in human containment and isolation to flatten the curve of mortality rates of infected patients. To leverage the massive containment strategy, fifth-generation (5G)-envisioned unmanned aerial vehicles (UAVs) are used to minimize human intervention with the key benefits of ultra-low latency, high bandwidth, and reliability. This allows phased treatment of infected patients via threefold functionalities (3FFs) such as social distancing, proper sanitization, and inspection and monitoring. However, UAVs have to send massive recorded data back to ground stations (GS), which requires a real-time device connection density of 107/km2, which forms huge bottlenecks on 5G ecosystems. A sixth-generation (6G) ecosystem can provide terahertz (THz) frequency bands with massive short beamforming cells, intelligent deep connectivity, and physical- and link-level protocol virtualization. The UAVs form a swarm network to assure 3FFs which requires high-end computations and are data-intensive; thus, these computational tasks can be offloaded to nearby edge servers, which employ local federated learning to train the global models. It synchronizes the UAV task formations and optimizes the network functions. Task optimization of UAV swarms in 6G-assisted channels allows better management and ubiquitous and energy-efficient seamless communication over ground, space, and underwater channels. Thus, a data-centric 3FF approach is essential to fight against future pandemics, with a 6G backdrop channel. The proposed scheme is compared with traditional fourth-generation (4G) and 5G-networks-based schemes to indicate its efficiency in traffic density, processing latency, spectral efficiency, UAV mobility, radio loss, and device connection density. Full article
(This article belongs to the Special Issue Evidence-Based Drone Innovation & Research for Healthcare)
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