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. Studies in human subjects published within the last decade relevant to the review question were identified. Meta-analysis using random-effects models of FTT failure rate and STARD scoring was then performed on the retrieved publications. RESULTS: Nineteen studies met the inclusions criteria. For NIRS and ID, the mean sensitivity for the detection of FTT failure is 99.36% and 100% respectively, with average specificity of 99.36% and 97.63%, respectively. From studies with sufficient reported data, meta-analysis results demonstrated that both NIRS [OR = 0.09 (0.02-0.36); P < 0.001] and ID [OR = 0.39 (0.27-0.95); P = 0.04] were associated with significant reduction of FTT failure rates compared with CCA. CONCLUSIONS: The use of ID and NIRS provided equivalent outcomes in detecting FTT failure and were superior to CCA. The ability to acquire continuous objective physiological data regarding tissue perfusion is a perceived advantage of these techniques. Reduced clinical staff workload and minimized hospital costs are also perceived as positive consequences of their use.

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. We have developed a pervasive wearable wireless device that employs near-infrared spectroscopy (NIRS) to continuously monitor FTT via StO2 measurement. Previously tested on different models, the results of a clinical study are introduced. Our goal for the study is to demonstrate that the developed device can reliably detect StO2 variations in a clinical setting: 14 patients were recruited. Advanced data analysis was performed on the StO2 variations, the relative StO2 gradient change, and the classification of the StO2 within different clusters of blood occlusion level (from 0% to 100% at 25% step) based on previous studies made on a vascular phantom and animals. The outcomes of the clinical study concur with previous experimental results and the expected biological responses. This suggests that the device is able to correctly detect perfusion changes and provide real-time assessment on the viability of the FTT in a clinical setting.

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 (). This miniaturized and low-cost device is designed for postoperative monitoring of flap viability. With self-calibration, the device can adapt itself to the characteristics of the patients' skin such as tone and thickness. An extensive study was conducted with 32 volunteers. The experimental results show that the device can obtain reliable measurements across different phenotypes (age, sex, skin tone, and thickness). To assess its ability to detect flap failure, the sensor was tested in a pilot animal study. Free groin flaps were performed on 16 Sprague Dawley rats. Results demonstrate the accuracy of the sensor in assessing flap viability and identifying the origin of failure (venous or arterial thrombosis).

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. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This article presents a comprehensive up-to-date review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.