Use of Near-infrared Spectroscopy and Implantable Doppler for Postoperative Monitoring of Free Tissue Transfer for Breast Reconstruction: A Systematic Review and Meta-analysis.

Background: Failure to accurately assess the perfusion of free tissue transfer (FTT) in the early postoperative period may contribute to failure, which is a source of major patient morbidity and healthcare costs. This systematic review and meta-analysis aim to evaluate and appraise current evidence for the use of near-infrared spectroscopy (NIRS) and/or implantable Doppler (ID) devices compared with conventional clinical assessment (CCA) for postoperative monitoring of FTT in reconstructive breast surgery.

Methods: A systematic literature search was performed in accordance with the preferred reporting items for systematic reviews guidelines.

Pervasive wearable device for free tissue transfer monitoring based on advanced data analysis: clinical study report.

Free tissue transfer (FTT) surgery for breast reconstruction following mastectomy has become a routine operation with high success rates. Although failure is low, it can have a devastating impact on patient recovery, prognosis, and psychological well-being. Continuous and objective monitoring of tissue oxygen saturation (StO2) has been shown to reduce failure rates through rapid detection time of postoperative vascular complications.

A Self-Calibrated Tissue Viability Sensor for Free Flap Monitoring.

In fasciocutaneous free flap surgery, close postoperative monitoring is crucial for detecting flap failure, as around 10% of cases require additional surgery due to compromised anastomosis. Different biochemical and biophysical techniques have been developed for continuous flap monitoring, however, they all have shortcoming in terms of reliability, elevated cost, potential risks to the patient, and inability to adapt to the patient's phenotype. A wearable wireless device based on near infrared spectroscopy has been developed for continuous blood flow and perfusion monitoring by quantifying tissue oxygen saturation ().

Deep Learning for Health Informatics.

With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence.