Evaluation of the Accuracy of a Video and AI Solution to Measure Lower Leg and Foot Volume

The Utilization of e-Health in Lymphedema Care: A Narrative Review

Background: Electronic health (e-Health), refers to technologies that can be utilized to enhance patient care as well as collect and share health information. e-Health comprises several umbrella terms, including telehealth, mobile health, e-Health, wearables, and artificial intelligence. The types of e-Health technologies being utilized in lymphedema (LE) care are unknown. Method: In this narrative review, a search of published research on the utilization of e-Health technologies in LE-related care was conducted. Results: Five different types of e-Health modalities were found (robotics, artificial intelligence, electronic medical records, smart wearable devices, and instructive online information) spanning 14 use cases and 4 phases of care (preventative, diagnostic, assessment, and treatment phases). Broad e-Health utilization examples were found including robotic-assisted surgery to reduce the likelihood of LE after lymphadenectomy, machine learning to predict patients at risk of filarial-related LE, and a novel wearable device prototype designed to provide lymphatic drainage. Conclusions: e-Health has reported merit in the prevention, diagnoses, assessment, and treatment of LE with utilization demonstrating cutting edge applicability of e-Health for achieving optimal patient care and outcomes. As technology continues to advance, additional research into the utilization of e-Health in LE care is warranted.

Remote Monitoring for Heart Failure Management at Home

Early telemonitoring of weights and symptoms did not decrease heart failure hospitalizations but helped identify steps toward effective monitoring programs. A signal that is accurate and actionable with response kinetics for early re-assessment is required for the treatment of patients at high risk, while signal specifications differ for surveillance of low-risk patients. Tracking of congestion with cardiac filling pressures or lung water content has shown most impact to decrease hospitalizations, while multiparameter scores from implanted rhythm devices have identified patients at increased risk. Algorithms require better personalization of signal thresholds and interventions. The COVID-19 epidemic accelerated transition to remote care away from clinics, preparing for new digital health care platforms to accommodate multiple technologies and empower patients. Addressing inequities will require bridging the digital divide and the deep gap in access to HF care teams, who will not be replaced by technology but by care teams who can embrace it.

Artificial intelligence and lymphedema: State of the art

Background

Lymphedema practice is facing many challenges. Some of these challenges include eradication of tropical lymphedema, preclinical diagnosis of cancer-related lymphedema, and delivery of appropriate individualized care. The past two decades have witnessed an increasing implementation of artificial intelligence (AI) in health-care services. The nature of the challenges facing the lymphedema practice is suitable for AI applications.

Aim

The aim of this study was to explore the current AI applications in lymphedema prevention, diagnosis, and management and investigate the potential future applications.

Methods and Results

Four databases were searched: PubMed, Scopus, Web of Science, and EMBASE. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis as our basis of organization. Our analysis showed that several domains of AI, including machine learning (ML), fuzzy models, deep learning, and robotics, were successfully implemented in lymphedema practice. ML can guide the eradication campaigns of tropical lymphedema by estimating disease prevalence and mapping the risk areas. Robotic-assisted surgery for gynecological cancer was associated with a lower risk for the lower limb lymphedema. Several feasible models were described for the early detection and diagnosis of lymphedema. The proposed models are more accurate, sensitive, and specific than current methods in practice. ML was also used to guide and monitor patients during the rehabilitation exercises.

Conclusion

AI offers a variety of solutions to the most challenging problems in lymphedema practice. Further, implementation into the practice can revolutionize many aspects of lymphedema prevention, diagnosis, and management.

Relevance to Patients

Lymphedema is a chronic debilitating disease that is affecting millions of patients. Developing new modalities for prevention, early diagnosis, and treatment are critical to improve the outcomes. AI offers a variety of solutions for some of the complexities of lymphedema management. In this systematic review, we summarize and discuss the latest AI advances in lymphedema practice.

Heart failure decompensation alerts in a patient's home using an automated, AI-driven, point-of-care device

Heart failure (HF) is a major challenge worldwide and needs continuous monitoring of patients even after hospital discharge. This case report summarises the data collected and experience gained from the first usage of an automated, point-of-care device (Heartfelt device) in a patient’s home in the UK. The device monitors the onset of peripheral oedema and alerts clinicians if an increase in volume outside an expected normal range for the patient is detected. This may provide a reliable method of remotely and automatically monitoring HF patients in the home for those who do not reliably use weighing scales. The device successfully provided data for about 15 months and generated alerts in advance, which supported decisions for the patient’s care. The rate of data acquisition was very high and consistent throughout this period. The patient was satisfied with the device and agreed that it helped in her decision to seek medical attention.