Wearable Devices
Conditions
Keywords
Wearable biosensors, postoperative infection, artificial intelligence
Brief summary
This systematic review aims to evaluate the efficacy, accuracy, and clinical applicability of wearable infection detection wristbands in postoperative patients across ophthalmology, orthopaedic surgery, and general surgery. The review focuses on devices capable of monitoring inflammatory biomarkers-particularly white blood cell (WBC) counts and C-reactive protein (CRP)-and examines the added value of artificial intelligence (AI) algorithms for early infection detection. The study synthesizes available evidence on clinical outcomes, predictive accuracy, usability, and feasibility of biosensor-based infection surveillance in postoperative care. It is expected to provide an evidence-based framework for integrating wearable biosensors into perioperative management protocols and to guide future multicenter clinical validation studies.
Detailed description
Postoperative infection remains one of the most common and serious complications following surgical procedures. Early detection of infection is critical for optimizing outcomes and reducing morbidity. Conventional laboratory monitoring using intermittent WBC and CRP testing is invasive and time-dependent, often delaying timely clinical intervention. Recent advances in wearable biosensor technology have enabled continuous, non-invasive monitoring of physiological and biochemical parameters. Several wearable platforms are now capable of detecting early inflammatory changes through electrochemical or optical sensing, with CRP being the most validated biomarker. Integration of AI algorithms further enhances predictive performance by analyzing complex data patterns and providing early alerts to clinicians. This systematic review adheres to PRISMA 2020 guidelines and aims to consolidate available clinical and experimental evidence on wearable biosensors capable of postoperative infection detection, emphasizing WBC and CRP monitoring wristbands and AI-assisted analysis. By synthesizing data from ophthalmology, orthopaedics, and general surgery, the review will assess diagnostic accuracy, clinical outcomes, and feasibility of these technologies in diverse healthcare contexts. The findings are expected to inform future research directions, highlight existing technological gaps, and propose recommendations for clinical implementation and regulatory validation.
Interventions
DIAGNOSTIC_TESTWhite Blood Cell (WBC)
Detected by wearable device
DIAGNOSTIC_TESTC-reactive Protein (CRP)
detected by wearable device
Sponsors
Benha University
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
Adults aged 18 years or older. Patients undergoing ophthalmologic, orthopedic, or general surgical procedures. Postoperative patients monitored using a wearable infection detection device or biosensor capable of continuous or intermittent assessment of inflammatory biomarkers, including: White blood cell (WBC) count and/or C-reactive protein (CRP) levels. Wearable devices may incorporate artificial intelligence or machine-learning algorithms for infection prediction. Patients receiving standard postoperative care, including conventional laboratory testing and/or clinical monitoring, for comparison. Ability to provide written informed consent.
Exclusion criteria
Patients aged \<18 years. Non-human studies (animal or in-vitro). Use of wearable devices that monitor only physiological parameters (e.g., temperature, heart rate, oxygen saturation) without inflammatory biomarker assessment (WBC or CRP). Patients who are hemodynamically unstable at the time of enrollment. Inability or unwillingness to provide informed consent. Duplicate enrollment or participation in another interventional study that may interfere with outcomes.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic Accuracy of Wearable Devices for Detection of Postoperative Infection | 1 week | Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of wearable devices for detecting postoperative infection, using standard clinical diagnosis as the reference standard. |
| Time to Postoperative Infection Detection Using Wearable Devices Compared With Standard Care | 1 week | Time interval between clinical onset of postoperative infection and detection by wearable devices compared with detection by standard postoperative care protocols. |
| Predictive Accuracy of AI-Integrated Wearable Monitoring for Early Postoperative Infection | 1 week | Improvement in early postoperative infection prediction accuracy achieved by AI-integrated wearable monitoring compared with traditional laboratory-based monitoring methods. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Secondary outcomes | 1 week | Patient recovery metrics (readmission rate, wound healing time). Feasibility and usability of wearable devices in postoperative settings. Device validation quality and risk of bias across included studies. |
| Methodological Quality and Risk of Bias of Wearable Device Validation | 1 week | Quality of wearable device validation and risk of bias assessed using standardized evaluation tools appropriate for diagnostic accuracy studies. |
| Time to Wound Healing | 1 week | Duration from surgery to clinically confirmed wound healing based on standardized postoperative assessment criteria. |
| Feasibility and Usability of Wearable Devices in Postoperative Monitoring | 1 week | Assessment of feasibility and usability of wearable devices in the postoperative setting, including adherence rates, device-related issues, and patient-reported usability scores. |
Countries
Egypt
Outcome results
None listed