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.

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.

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.

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.