{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T11:39:46Z","timestamp":1792409986635,"version":"4.4.0"},"reference-count":53,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T00:00:00Z","timestamp":1792368000000},"content-version":"unspecified","delay-in-days":291,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Prehosp. Disaster med."],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"S1049023X26109121_as1\">\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>Triage and data transfer are critical to the medical management of mass-casualty incidents (MCIs). However, the chaotic nature of such events makes it difficult to classify casualties according to structured triage protocols. Furthermore, even when triage is implemented effectively, communication disruptions between the incident scene and dispatch centers can hinder response operations. The aim of this study was to present a drone-integrated system that enables non-contact vital sign measurement, remote triage, and real-time data transmission during MCIs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1049023X26109121_as2\">\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>\n                      The system development process involved three steps: (1) developing the Vital-Sign Measurement Module; (2) developing the Triage Decision Support Module; and (3) developing the User Interfaces and Data Transmission Module to enable information transmission between dispatch centers and response teams. Within the Vital-Sign Measurement Module, heart rate (HR), respiratory rate (RR), oxygen saturation (SpO\n                      <jats:sub>2<\/jats:sub>\n                      ), and body temperature (BT) measurement algorithms were developed using the following techniques, respectively: video-based photoplethysmography (vPPG), analysis of thermal temperature fluctuations around the nose, a regression-based neural network approach, and heat maps and thermal thresholding. A pilot study was conducted under controlled indoor conditions to evaluate the accuracy of these algorithms.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1049023X26109121_as3\">\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The system can measure vital signs by processing drone-captured images, can provide triage decision support, and can transmit victims\u2019 locations, vital signs, and triage categories to response teams and dispatch centers via dedicated user interfaces. A pilot evaluation of the Vital-Sign Measurement Module showed that the relative accuracy rate of the non-contact vital-sign measurement algorithms ranged from 86% to 99%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1049023X26109121_as4\">\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>The system provides an innovative approach to improving medical response management in MCIs by enabling remote triage and monitoring. Its non-contact vital sign measurement, triage decision support, and near real-time data transmission capabilities provide an infrastructure that can contribute to faster response and improved operational coordination. However, further field trials and development are required to improve the robustness of the vital-sign measurement algorithms to motion, to improve their adaptability to changing environmental conditions, and to maintain high accuracy despite individual differences.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1017\/s1049023x26109121","type":"journal-article","created":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T10:43:58Z","timestamp":1792406638000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":0,"title":["A Drone-Integrated Artificial Intelligence-Based System for Mass-Casualty Triage and Remote Monitoring"],"prefix":"10.1017","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4852-7915","authenticated-orcid":false,"given":"\u0130smail","family":"Tayfur","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03k7bde87","id-type":"ROR","asserted-by":"publisher"}],"name":"University of 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