Smartphone-based wound infection screener and care recommender by combining thermal images and photographs using deep learning methods - I. PROJECT SUMMARY: Smartphone-based wound infection risk screener and care recommender by combining thermal images and photographs using deep learning methods Chronic wounds, which affect 6.5 million patients in the US12 severely affect their quality of life, can take up to a year to heal and re-occur in 60-70% of patients. Wounds often get infected (bacteria in wound), resulting in limb amputations if not treated properly and on time1. In current practice, at the Point of Care (POC) (e.g., nurses visiting patients’ homes and trauma sites), caregivers who are not wound experts have no way to diagnose infections. Thus, they cautiously refer wounds suspected to be infected to clinics for debridement of dead tissues, blood tests and infection diagnoses by experts57-60. However, referrals increase time before infected wounds are treated, and the chances of limb amputation. Moreover, some referred wounds end up not being infected, wasting patient and expert time and expenses (e.g., transportation)15-16. What is needed is a digital health solution that enables non-expert wound caregivers to accurately detect infected wounds at the POC even without debridement and provide standardized recommendations on evidence-based care and when to refer. Smartphones equipped with high resolution cameras and the processing power to run machine/deep learning methods are owned by most wound caregivers in the US56. Prior work by Goyal et al1 reported preliminary results that show that infection can be detected from visual attributes such as increased redness in/around the wound in a photograph using deep learning (accuracy 0.727± 0.025, sensitivity 0.709 ± 0.044, specificity 0.744 ± 0.05). While promising, their results need to be improved and validated before clinical applications. Moreover, their dataset included already debrided wounds with easily discernable infection cases, and they did not recommend evidence based best care and decide when referrals to wound clinics were the best course of action. Certain thermal image patterns are reliable indicators of wound infection20, and some models of smartphones are now equipped with thermal cameras55. Our hypotheses are that 1) the accuracy of smartphone wound infection detection can be improved by combining thermal images with photographs jointly analyzed using a deep learning method 2) recommendations for actionable, evidence-based wound care and when to refer can be generated using machine learning to standardize care provided by non-experts. In response to NOT-EB-19-018, we propose research to investigate the capability and accuracy of detecting infected wounds before debridement using deep learning methods applied to combinations of wound photographs and thermal images and generating care and referral recommendations. We also propose integration of the smartphone-based infection screener into our group’s existing wound assessment system7-9, 21-29 and validating it on new patients (N=100). Success on our proposed aims will increase the number and objectivity of wound infections detected outside the wound clinic and fast-tracked to the clinic for treatment, reducing the number of patients who require amputations. Our findings will apply to diverse wound types including diabetic, pressure, arterial, venous, surgical61 and trauma wounds62, which all get infected.
