An introduction to the Cyrcadia Breast Monitor: A wearable breast health monitoring device

Portable noninvasive technologies for early breast cancer detection: A systematic review

Breast cancer remains a leading cause of cancer mortality worldwide, with early detection crucial for improving outcomes. This systematic review evaluates recent advances in portable non-invasive technologies for early breast cancer detection, assessing their methods, performance, and potential for clinical implementation. A comprehensive literature search was conducted across major databases for relevant studies published between 2015 and 2024. Data on technology types, detection methods, and diagnostic performance were extracted and synthesized from 41 included studies. The review examined microwave imaging, electrical impedance tomography (EIT), thermography, bioimpedance spectroscopy (BIS), and pressure sensing technologies. Microwave imaging and EIT showed the most promise, with some studies reporting sensitivities and specificities over 90 %. However, most technologies are still in early stages of development with limited large-scale clinical validation. These innovations could complement existing gold standards, potentially improving screening rates and outcomes, especially in underserved populations, whiles decreasing screening waiting times in developed countries. Further research is therefore needed to validate their clinical efficacy, address implementation challenges, and assess their impact on patient outcomes before widespread adoption can be recommended.

Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care

BACKGROUND

Wearable sensors are increasingly being explored in health care, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. Moreover, preprocessing pipelines to clean, transform, normalize, and standardize raw data have not yet been fully optimized.

OBJECTIVE

This study aims to conduct a scoping review of preprocessing techniques used on raw wearable sensor data in cancer care, specifically focusing on methods implemented to ensure their readiness for artificial intelligence and machine learning (AI/ML) applications. We sought to understand the current landscape of approaches for handling issues, such as noise, missing values, normalization or standardization, and transformation, as well as techniques for extracting meaningful features from raw sensor outputs and converting them into usable formats for subsequent AI/ML analysis.

METHODS

We systematically searched IEEE Xplore, PubMed, Embase, and Scopus to identify potentially relevant studies for this review. The eligibility criteria included (1) mobile health and wearable sensor studies in cancer, (2) written and published in English, (3) published between January 2018 and December 2023, (4) full text available rather than abstracts, and (5) original studies published in peer-reviewed journals or conferences.

RESULTS

The initial search yielded 2147 articles, of which 20 (0.93%) met the inclusion criteria. Three major categories of preprocessing techniques were identified: data transformation (used in 12/20, 60% of selected studies), data normalization and standardization (used in 8/20, 40% of the selected studies), and data cleaning (used in 8/20, 40% of the selected studies). Transformation methods aimed to convert raw data into more informative formats for analysis, such as by segmenting sensor streams or extracting statistical features. Normalization and standardization techniques usually normalize the range of features to improve comparability and model convergence. Cleaning methods focused on enhancing data reliability by handling artifacts like missing values, outliers, and inconsistencies.

CONCLUSIONS

While wearable sensors are gaining traction in cancer care, realizing their full potential hinges on the ability to reliably translate raw outputs into high-quality data suitable for AI/ML applications. This review found that researchers are using various preprocessing techniques to address this challenge, but there remains a lack of standardized best practices. Our findings suggest a pressing need to develop and adopt uniform data quality and preprocessing workflows of wearable sensor data that can support the breadth of cancer research and varied patient populations. Given the diverse preprocessing techniques identified in the literature, there is an urgency for a framework that can guide researchers and clinicians in preparing wearable sensor data for AI/ML applications. For the scoping review as well as our research, we propose a general framework for preprocessing wearable sensor data, designed to be adaptable across different disease settings, moving beyond cancer care.

Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare

The field of multi-omics has witnessed unprecedented growth, converging multiple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in multi-omics scientific publications within just two years (2022-2023) since its first referenced mention in 2002, as indexed by the National Library of Medicine. This emerging field has demonstrated its capability to provide comprehensive insights into complex biological systems, representing a transformative force in health diagnostics and therapeutic strategies. However, several challenges are evident when merging varied omics data sets and methodologies, interpreting vast data dimensions, streamlining longitudinal sampling and analysis, and addressing the ethical implications of managing sensitive health information. This review evaluates these challenges while spotlighting pivotal milestones: the development of targeted sampling methods, the use of artificial intelligence in formulating health indices, the integration of sophisticated n-of-1 statistical models such as digital twins, and the incorporation of blockchain technology for heightened data security. For multi-omics to truly revolutionize healthcare, it demands rigorous validation, tangible real-world applications, and smooth integration into existing healthcare infrastructures. It is imperative to address ethical dilemmas, paving the way for the realization of a future steered by omics-informed personalized medicine.

Lessons Learned from Natural Disasters around Digital Health Technologies and Delivering Quality Healthcare

As climate change drives increased intensity, duration and severity of weather-related events that can lead to natural disasters and mass casualties, innovative approaches are needed to develop climate-resilient healthcare systems that can deliver safe, quality healthcare under non-optimal conditions, especially in remote or underserved areas. Digital health technologies are touted as a potential contributor to healthcare climate change adaptation and mitigation, through improved access to healthcare, reduced inefficiencies, reduced costs, and increased portability of patient information. Under normal operating conditions, these systems are employed to deliver personalised healthcare and better patient and consumer involvement in their health and well-being. During the COVID-19 pandemic, digital health technologies were rapidly implemented on a mass scale in many settings to deliver healthcare in compliance with public health interventions, including lockdowns. However, the resilience and effectiveness of digital health technologies in the face of the increasing frequency and severity of natural disasters remain to be determined. In this review, using the mixed-methods review methodology, we seek to map what is known about digital health resilience in the context of natural disasters using case studies to demonstrate what works and what does not and to propose future directions to build climate-resilient digital health interventions.

Integrative Analysis of Rhythmicity: From Biology to Urban Environments and Sustainability

From biological to socio-technical systems, rhythmic processes are pervasive in our environment. However, methods for their comprehensive analysis are prevalent only in specific fields that limit the transfer of knowledge across scientific disciplines. This hinders interdisciplinary research and integrative analyses of rhythms across different domains and datasets. In this paper, we review recent developments in cross-disciplinary rhythmicity research, with a focus on the importance of rhythmic analyses in urban planning and biomedical research. Furthermore, we describe the current state of the art of (integrative) computational methods for the investigation of rhythmic data. Finally, we discuss the further potential and propose necessary future developments for cross-disciplinary rhythmicity analysis to foster integration of heterogeneous datasets across different domains, as well as guide data-driven decision making beyond the boundaries of traditional intradisciplinary research, especially in the context of sustainable and healthy cities.

Advanced thermal sensing techniques for characterizing the physical properties of skin

Measurements of the thermal properties of the skin can serve as the basis for a noninvasive, quantitative characterization of dermatological health and physiological status. Applications range from the detection of subtle spatiotemporal changes in skin temperature associated with thermoregulatory processes, to the evaluation of depth-dependent compositional properties and hydration levels, to the assessment of various features of microvascular/macrovascular blood flow. Examples of recent advances for performing such measurements include thin, skin-interfaced systems that enable continuous, real-time monitoring of the intrinsic thermal properties of the skin beyond its superficial layers, with a path to reliable, inexpensive instruments that offer potential for widespread use as diagnostic tools in clinical settings or in the home. This paper reviews the foundational aspects of the latest thermal sensing techniques with applicability to the skin, summarizes the various devices that exploit these concepts, and provides an overview of specific areas of application in the context of skin health. A concluding section presents an outlook on the challenges and prospects for research in this field.

Shape Memory Alloy-Based Wearables: A Review, and Conceptual Frameworks on HCI and HRI in Industry 4.0

Ever since its discovery, the applications of Shape Memory Alloys (SMA) can be found across a range of application domains, from structural design to medical technology. This is based upon the unique and inherent characteristics such as thermal Shape Memory Effect (SME) and Superelasticity (or Pseudoelasticity). While thermal SME is used for shape morphing applications wherein temperature change can govern the shape and dimension of the SMA, Superelasticity allows the alloy to withstand a comparatively very high magnitude of loads without undergoing plastic deformation at higher temperatures. These unique properties in wearables have revolutionized the field, and from fabrics to exoskeletons, SMA has found its place in robotics and cobotics. This review article focuses on the most recent research work in the field of SMA-based smart wearables paired with robotic applications for human-robot interaction. The literature is categorized based on SMA property incorporated and on actuator or sensor-based concept. Further, use-cases or conceptual frameworks for SMA fiber in fabric for 'Smart Jacket' and SMA springs in the shoe soles for 'Smart Shoes' are proposed. The conceptual frameworks are built upon existing technologies; however, their utility in a smart factory concept is emphasized, and algorithms to achieve the same are proposed. The integration of the two concepts with the Industrial Internet of Things (IIoT) is discussed, specifically regarding minimizing hazards for the worker/user in Industry 5.0. The article aims to propel a discussion regarding the multi-faceted applications of SMAs in human-robot interaction and Industry 5.0. Furthermore, the challenges and the limitations of the smart alloy and the technological barriers restricting the growth of SMA applications in the field of smart wearables are observed and elaborated.

[Connected bras for breast cancer detection in 2021: Analysis and perspectives]

OBJECTIVES

Breast cancer is the leading cancer in women worldwide with about 2 million new cases and 685,000 deaths each year. Mammography is the most widely used screening and diagnostic method. Currently, digital technologies advances facilitate the development of connected and portable devices. To overcome some of the disadvantages of mammography (breast compression, difficulty in analyzing dense breasts, radiation, limited accessibility in some countries, etc.), portable devices, conventionally known as connected bras (CB), have been created to offer an alternative method to mammography. The objective of our review was to list all the published CBs in order to know their main characteristics, their potential indications and their possible limitations.

METHOD

A bibliographical search in the PUBMED database selecting only articles written in French or English, between 2011 and 2020, found 7 CBs under development.

RESULTS

These CBs use thermal, ultrasonic and impedance sensors. Their advantages are an absence of irradiation, an absence of breast compression and a flexibility of use (outside an X-ray cabinet). Mammary gland analysis times vary, depending on the device, between 30min and 24h. They are all connected to data transmission systems and models that analyze the results.

DISCUSSION AND CONCLUSION

These CBs are mostly still undergoing clinical validation (only [iTBra] has been evaluated in a clinical trial) and require evaluation steps that will eventually allow their future use for breast cancer detection in high-risk women, particularly in women with dense breasts and in women between screening waves.