After surgery, a patient’s internal healing remains largely invisible to doctors until a complication—like an infection or a leaking join—announces itself with fever or pain, often too late for easy treatment. Current monitoring relies on periodic checks and lab tests that can miss the early signs of surgical site infections, catheter-related sepsis, wound breakdown, or anastomotic leakage. These complications are common, poorly understood, and a major cause of death and illness after surgery. The project aims to close this gap by developing smart sensors that integrate directly into surgical drains, catheters, or implants placed near the surgical site. These tiny devices would continuously monitor local conditions—such as inflammation or bacterial activity—and wirelessly transmit data to clinicians, flagging problems before they become emergencies. If successful, the technology could transform post-surgical care. Patients might be discharged earlier and recover safely at home, with fewer unplanned readmissions. Surgeons would gain real-time, personalised insight into each patient’s healing, rather than relying on generic timelines. The sensors are designed to be eliminated naturally or removed with the drain, leaving nothing behind. The work could also influence healthcare policy by shifting surgical recovery from hospital-centred to community-based models.
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Recent advances in surgery have made a significant impact on the management of major acute diseases, prolonging life and continuously pushing the boundaries of survival. Despite increasing sophistication of surgical intervention, complications remain common and poorly understood, contributing significantly to mortality and morbidity. Surgical site infections, catheter related sepsis, wound dehiscence and gastrointestinal anastomotic leakage are recognised complications following surgical interventions or invasive monitoring of critically ill surgical patients. Current methods for detecting these complications rely on episodic clinical examination with 'snap shot' laboratory testing. There is therefore a pressing need to develop new sensing technologies that can be seamlessly integrated with existing surgical appliances to provide continuous sensing and early detection of these adverse events, thus minimising post-operative infection, complication, and readmission. All these will also have a direct impact on healthcare economics, and more importantly the prognosis and quality-of-life of patients after surgery. The proposed project is organised into three research themes: 1) Multimodal Sensing and Miniaturised Embodiment; 2) Active Sensing with Low Power Microelectronics; and 3) Data Inferencing and Stratified Patient Management. These research themes address key technical issues related to sensor design, miniaturisation, and self-calibration, as well as low-power on-node processing, inferencing, and clinical decision support. These research themes are connected by three clinical exemplars in surgical sensing with increasing levels of technical complexity. The vision is to develop smart sensors integrated with surgical appliances and to be inserted in close proximity to the surgical site, encased within surgical drains/catheters, or placed in locations to more seamlessly monitor the systemic inflammatory response. The devices will be implanted during elective surgery or at biopsy, interrogated wirelessly, and eliminated by natural processes, or routine removal of 'hosts' such as the drains or catheters. The research programme is underpinned by extensive experience of the team in body sensor networks and bio-photonics in healthcare. Through an integrated programme of engineering research and development of a novel real-time active sensing paradigm, the project aims to transform the care pathways for surgery with greater consideration on personalised treatment, system level impact, real-time response to complications, patient concordance and quality of life. We expect that the outcome of the research will help improve surgical workflows, support safe discharge and home/community-based recovery, reduce unplanned readmissions, and influence the future of healthcare policy.
Ara Darzi (Co-Investigator)Benny Lo (Co-Investigator)Daniel Elson (Co-Investigator)Daniel Leff (Co-Investigator)Guang-Zhong Yang (Principal Investigator)Mikael Sodergren (Co-Investigator)
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