Multicenter Study Protocol: Research on Evaluation and Detection of Surgical Wound Complications with AI-Based Recognition (REDSCAR-Trial)

<b>Background:</b> The increasing use of telemedicine in surgical care has shown promise in improving patient outcomes and optimizing healthcare resources. Surgical site infections (SSIs) are a major cause of healthcare-associated infections (HAIs), leading to significant economic and health burdens. A pilot study already demonstrated that RedScar© achieved 100% sensitivity and 83.13% specificity in detecting SSIs. Patients reported high satisfaction regarding comfort, cost-effectiveness, and reduced absenteeism. <b>Methods:</b> This multicenter prospective study will include 168 patients undergoing abdominal surgery. RedScar© utilizes smartphone-based automated infection risk assessments without clinician input. App-based detection will be compared with in-person evaluations. Sensitivity and specificity will be analyzed using receiver operating characteristic (ROC) analysis, while secondary objectives include assessing patient satisfaction and standardizing telematic follow-up. <b>Results:</b> This study aims to evaluate the efficacy of the RedScar© app, sensitivity, specificity in detecting SSIs. Satisfaction regarding comfort, cost-effectiveness, and absenteeism due to telematic detection and the monitoring of SSIs will be recorded too. <b>Conclusions:</b> This study seeks to validate RedScar© as a reliable and scalable tool for postoperative monitoring. By improving early SSI detection, it has the potential to enhance surgical recovery, reduce healthcare costs, and optimize resource utilization.