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Assessment of a smartphone-based application for diabetic foot ulcer measurement.

Beatrice KuangGuilherme PenaZygmunt SzpakSuzanne EdwardsRuth BattersbyPrue CowledJoseph DawsonRobert Fitridge
Published in: Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society (2021)
The accurate measurement of diabetic foot ulcer (DFU) wound size is essential as the rate of wound healing is a significant prognostic indicator of the likelihood of complete wound healing. Mobile phone photography is often used for surveillance and to aid in telemedicine consultations. However, there remains no accurate and objective measurement of wound size integrated into these photos. The NDKare mobile phone application has been developed to address this need and our study evaluates its accuracy and practicality for DFU wound size assessment. The NDKare mobile phone application was evaluated for its accuracy in two- (2D) and three-dimensional (3D) wound measurement. One hundred and fifteen diabetic foot wounds were assessed for wound surface area, depth and volume accuracy in comparison to Visitrak and the WoundVue camera. Thirty five wounds had two assessors with different mobiles phones utilizing both applications to assess the reproducibility of the measurements. The 2D surface area measurements by NDKare showed excellent concordance with Visitrak and WoundVue measurements (ICC: 0.991 [95% CI: 0.988, 0.993]) and between different users (ICC: 0.98 [95% CI: 0.96, 0.99)]. The 3D NDKare measurements had good agreement for depth and fair agreement for volume with the WoundVue camera. The NDKare phone application can consistently and accurately obtain 2D measurements of diabetic foot wounds with mobile phone photography. This is a quick and readily accessible tool which can be integrated into comprehensive diabetic wound care.
Keyphrases
  • wound healing
  • surgical site infection
  • high resolution
  • public health
  • palliative care
  • high speed
  • quality improvement
  • mass spectrometry
  • chronic pain
  • primary care
  • deep learning
  • low cost