Wearable Devices for Early Detection of Postoperative Infection (Wearable Wrist)

A Systematic Review of Wearable Infection Detection Wristbands for Postoperative Patients: Evaluating the Efficacy of WBC and CRP Monitoring and AI-Assisted Early Detection

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.

Study Overview

• Status: Completed
• Conditions: Wearable Devices
• Intervention / Treatment: Diagnostic test: White Blood Cell (WBC); Diagnostic test: C-reactive Protein (CRP)

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.

• Study Type: Observational
• Enrollment (Actual): 1284

Contacts and Locations

Study Locations

• Egypt
  • Benha
    • Banhā, Benha, Egypt, 13111
      • Benha University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

The study population will consist of adult patients (≥18 years) undergoing elective or semi-elective surgical procedures in ophthalmology, orthopedics, and general surgery departments at participating tertiary hospitals. Eligible procedures include surgeries associated with moderate to high postoperative infection risk, such as cataract surgery, corneal transplantation, total joint replacement, and abdominal surgery.

Participants must be clinically stable at enrollment and capable of providing informed consent. Both male and female patients will be included without restriction based on race or ethnicity. This population reflects typical postoperative clinical settings in which early infection detection using wearable technology may improve outcomes and reduce morbidity.

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Group Monitoring; Wearable infection detection wristbands / biosensors	Diagnostic test: White Blood Cell (WBC); Detected by wearable device; Diagnostic test: C-reactive Protein (CRP); detected by wearable device

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Diagnostic Accuracy of Wearable Devices for Detection of Postoperative Infection	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.	1 week
Time to Postoperative Infection Detection Using Wearable Devices Compared With Standard Care	Time interval between clinical onset of postoperative infection and detection by wearable devices compared with detection by standard postoperative care protocols.	1 week
Predictive Accuracy of AI-Integrated Wearable Monitoring for Early Postoperative Infection	Improvement in early postoperative infection prediction accuracy achieved by AI-integrated wearable monitoring compared with traditional laboratory-based monitoring methods.	1 week

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Secondary outcomes	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.	1 week
Time to Wound Healing	Duration from surgery to clinically confirmed wound healing based on standardized postoperative assessment criteria.	1 week
Feasibility and Usability of Wearable Devices in Postoperative Monitoring	Assessment of feasibility and usability of wearable devices in the postoperative setting, including adherence rates, device-related issues, and patient-reported usability scores.	1 week
Methodological Quality and Risk of Bias of Wearable Device Validation	Quality of wearable device validation and risk of bias assessed using standardized evaluation tools appropriate for diagnostic accuracy studies.	1 week

Collaborators and Investigators

• Sponsor: Benha University

Study record dates

Study Major Dates

• Study Start (Actual): July 1, 2025
• Primary Completion (Actual): October 26, 2025
• Study Completion (Actual): October 27, 2025

Study Registration Dates

• First Submitted: December 21, 2025
• First Submitted That Met QC Criteria: January 6, 2026
• First Posted (Estimated): January 12, 2026

Study Record Updates

• Last Update Posted (Estimated): January 12, 2026
• Last Update Submitted That Met QC Criteria: January 6, 2026
• Last Verified: July 1, 2025

More Information

Terms related to this study

• Keywords: artificial intelligence; postoperative infection; Wearable biosensors
• Additional Relevant MeSH Terms: Investigative Techniques; Clinical Laboratory Techniques; Diagnostic Techniques and Procedures; Diagnosis; Cell Count; Cytological Techniques; Hematologic Tests; Cell Physiological Phenomena; Blood Physiological Phenomena; Circulatory and Respiratory Physiological Phenomena; Blood Cell Count; Leukocyte Count
• Other Study ID Numbers: AI-Assisted Detect Infection

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No