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.

Abstract-Blood flow and perfusion monitoring are critical appraisal to ensure survival of tissue flap after reconstructive surgery. Many techniques have been developed over the years: from optical to chemical, invasive or not, they all have limitations in their price, risks and adaptiveness to the patient. A wireless wearable self-calibrated device, based on near infrared spectroscopy (NIRS) was developed for blood flow and perfusion monitoring contingent on tissue oxygen saturation (StO 2 ). The use of such device is particularly relevant in the case of free flap myocutaneous reconstructive surgery; postoperative monitoring of the flap is crucial for a prompt intervention in case of thrombosis. Although failure rate is low, the rate of additional surgery following anastomosis problem is about 50%. NIRS has shown promising results for the monitoring of free flap, however lack of adaptation to its environment (ambient light) and users (body mass index (BMI), skin tone, alcohol and smoking habits or physical activity level) hinders the practical use of this technique. To overcome those limitations, a self-calibrated approach is introduced. Tested with ischaemia and cold water experiments on healthy subjects of different skin tones, its ability to personalize its calibration is demonstrated. Furthermore, using a vascular phantom, it is also able to detect pulses, differentiate venous and arterial colouredlike fluids with distinct clusters and detect significant changes in simulated partial venous occlusion. Placed in the trained classifier, partial occlusion data showed similar results between predicted and true classification. Further analysis from partial occlusion data showed that distinct clusters for 75% and 100% occlusion emerged.

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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).

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 (StO 2) 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 StO 2 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 StO 2 variations in a clinical setting: 14 patients were recruited. Advanced data analysis was performed on the StO 2 variations, the relative StO 2 gradient change, and the classification of the StO 2 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.