Objective: This paper discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorized into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multiomics data integration, and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm toward preventative, predictive, personalized, and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realizing the goal of sustainable healthcare systems.

The aim of this study was to conduct a systematic review and meta-analysis to clarify the diagnostic accuracy of intraoperative breast margin assessment (IMA) techniques against which the performance of emerging IMA technologies may be compared. IMA techniques have failed to penetrate routine practice due to limitations, including slow reporting times, technical demands, and logistics. Emerging IMA technologies are being developed to reduce positive margin and re-excision rates and will be compared with the diagnostic accuracy of existing techniques. Studies were identified using electronic bibliographic searches up to January 2016. MESH terms and all-field search terms included “Breast Cancer*” AND “Intraoperative*” AND “Margin*.” Only clinical studies with raw diagnostic accuracy data as compared with final permanent section histopathology were included. A bivariate model for diagnostic meta-analysis was used to attain overall pooled sensitivity and specificity. Eight hundred thirty-eight unique studies revealed 35 studies for meta-analysis. Pooled sensitivity (Sens), specificity (Spec), and area under the receiver operating characteristic curve (AUROC) values were calculated per group (Sens, Spec, AUROC): frozen section = 86%, 96%, 0.96 (n = 9); cytology = 91%, 95%, 0.98 (n = 11); intraoperative ultrasound = 59%, 81%, 0.78 (n = 4); specimen radiography = 53%, 84%, 0.73 (n = 9); optical spectroscopy = 85%, 87%, 0.88 (n = 3). Pooled data suggest that frozen section and cytology have the greatest diagnostic accuracy. However, these methods are resource intensive and turnaround times for results have prevented widespread international adoption. Emerging technologies need to compete with the diagnostic accuracy of existing techniques while offering advantages in terms of speed, cost, and reliability.

Re-operation for positive resection margins following breast-conserving surgery occurs frequently (average = 20–25%), is cost-inefficient, and leads to physical and psychological morbidity. Current margin assessment techniques are slow and labour intensive. Rapid evaporative ionisation mass spectrometry (REIMS) rapidly identifies dissected tissues by determination of tissue structural lipid profiles through on-line chemical analysis of electrosurgical aerosol toward real-time margin assessment. Electrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples. Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos. A classification model using histologically validated spectral data acquired from 932 sampling points in normal tissue and 226 in tumour tissue provided 93.4% sensitivity and 94.9% specificity. Tandem MS identified 63 phospholipids and 6 triglyceride species responsible for 24 spectral differences between tissue types. iKnife recognition accuracy with 260 newly acquired fresh and frozen breast tissue specimens (normal n = 161, tumour n = 99) provided sensitivity of 90.9% and specificity of 98.8%. The ex-vivo and intra-operative method produced visually comparable high intensity spectra. iKnife interpretation of intra-operative electrosurgical vapours, including data acquisition and analysis was possible within a mean of 1.80 seconds (SD ±0.40). The REIMS method has been optimised for real-time iKnife analysis of heterogeneous breast tissues based on subtle changes in lipid metabolism, and the results suggest spectral analysis is both accurate and rapid. Proof-of-concept data demonstrate the iKnife method is capable of online intraoperative data collection and analysis. Further validation studies are required to determine the accuracy of intra-operative REIMS for oncological margin assessment.

Objective: To evaluate the impact of axillary lymph node dissection (ALND) and sentinel lymph node biopsy (SLNB) on upper limb (UL) morbidity in breast cancer patients. Background: Axillary de-escalation is motivated by a desire to reduce harm of ALND. Understanding the impact of axillary surgery and disparities in operative procedures on postoperative arm morbidity would better direct resources to the point of need and cement the need for de-escalation strategies. Methods: Embase, MEDLINE, CINAHL, and PsychINFO were searched from 1990 until March 2020. Included studies were randomized-controlled and observational studies focusing on UL morbidities, in breast surgery patients. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The prevalence of UL morbidity comparing SLNB and ALND at <12 months, 12 to 24 months, and beyond 24 months were analyzed. Results: Sixty-seven studies were included. All studies reported a higher rate of lymphedema and pain after ALND compared with SLNB. The difference in lymphedema and pain prevalence between SLNB and ALND was 13.7% (95% confidence interval: 10.5–16.8, P<0.005) and 24.2% (95% confidence interval: 12.1–36.3, P<0.005), respectively. Pooled estimates for prevalence of reduced strength and range of motion after SLNB and ALND were 15.2% versus 30.9% and 17.1% versus 29.8%, respectively. Type of axillary surgery, greater body mass index, and radiotherapy were some of the predictors for UL morbidities. Conclusions: Prevalence of lymphedema after ALND was higher than previously estimated. ALND patients experienced greater rates of lymphedema, pain, reduced strength, and range of motion compared with SLNB. The findings support the continued drive to de-escalate axillary surgery.