The use of drones and a machine-learning model for recognition of simulated drowning victims—A feasibility study. (November 2020)

• Record Type: Journal Article
• Authors: Claesson, A.; Schierbeck, S.; Hollenberg, J.; Forsberg, S.; Nordberg, P.; Ringh, M.; Olausson, M.; Jansson, A.; Nord, A.
• Is Part Of: Resuscitation. Volume 156(2020)
• Page Start: 196
• Page End: 201
• Publication Date: 2020-11
• Subjects: Drowning -- Drone -- OHCA -- Machine-learning; Resuscitation -- Periodicals; Resuscitation -- Periodicals; Réanimation -- Périodiques; Electronic journals; 616.025
• DOI: 10.1016/j.resuscitation.2020.09.022
• Languages: English
• ISSNs: 0300-9572

Abstract

Abstract: Background: Submersion time is a strong predictor for death in drowning, already 10 min after submersion, survival is poor. Traditional search efforts are time-consuming and demand a large number of rescuers and resources. We aim to investigate the feasibility and effectiveness of using drones combined with an online machine learning (ML) model for automated recognition of simulated drowning victims. Methods: This feasibility study used photos taken by a drone hovering at 40 m altitude over an estimated 3000 m 2 surf area with individuals simulating drowning. Photos from 2 ocean beaches in the south of Sweden were used to (a) train an online ML model (b) test the model for recognition of a drowning victim. Results: The model was tested for recognition on n = 100 photos with one victim and n = 100 photos with no victims. In drone photos containing one victim (n = 100) the ML model sensitivity for drowning victim recognition was 91% (95%CI 84.9%–96.2%) with a median probability score that the finding was human of 66% (IQR 52−71). In photos with no victim (n = 100) the ML model specificity was 90% (95%CI: 83.9%–95.6%). False positives were present in 17.5% of all n = 200 photos but could all be ruled out manually as false objects. Conclusions: The use of a drone and a ML model was feasible and showed satisfying effectiveness in identifying a submerged static human simulating drowning in open water and favorable environmental conditions. The ML algorithm and methodology should be further optimized, again tested and validated in a real-life clinical study.