AI Foot Scanner Revolutionizes Heart Failure Home Monitoring

Breakthrough AI Technology Detects Early Heart Failure Signs

Researchers have announced a groundbreaking development in home healthcare: an AI-powered foot scanner designed to identify early warning signs of heart failure. This innovative device aims to monitor patients remotely, potentially reducing hospital admissions and improving patient outcomes.

The scanner operates by capturing and analyzing nearly 2,000 images per minute, employing a technique similar to facial recognition. Its primary function is to precisely calculate the level of fluid retention in a patient's feet and ankles. This condition, known as oedema, is a critical indicator that heart failure might be worsening and could become life-threatening.

How the Smart Scanner Works

Roughly the size of a smart speaker, the AI scanner is typically mounted on a wall at the patient's bedside. Developed by Heartfelt Technologies, a Cambridge-based start-up, the device focuses on scanning the legs up to a height of 50cm from the floor. It automatically takes 1,800 pictures a minute from multiple angles and uses artificial intelligence to determine fluid levels. Notably, the scanner can function effectively even without a Wi-Fi connection, ensuring continuous monitoring. When concerning levels of fluid are detected, it can alert healthcare professionals, enabling them to intervene promptly, perhaps by adjusting medication.

Insights from The Foot Study

Findings from "The Foot Study," presented at the British Cardiovascular Society annual conference in Manchester, are promising. The research suggests that alerts from the device can precede hospitalization by an average of 13 days. The study monitored 26 heart failure patients from five NHS trusts between 2020 and 2022. Participants were also asked to use Bluetooth-enabled scales for weight tracking.

During the study, seven instances of worsening heart failure were detected in six patients, and one death from the condition was recorded. For patients monitored for at least two weeks before an alert, the average lead time to potential hospital admission was 13 days. When considering all five triggers detected by the device, this lead time averaged eight days.

Interestingly, the study found that traditional weight monitoring using scales failed to predict any heart failure-related hospital admissions. Researchers attribute this to difficulties patients experienced in consistently tracking their weight, a task the AI device performs automatically without requiring patient action. You can learn more about health apps to stay updated on similar technologies.

The Importance of Early Warning and Expert Opinions

The early warning capability of this AI scanner is crucial. It allows specialist medical staff to react swiftly to changes in a patient's condition, potentially managing the issue before hospitalization becomes necessary.

Dr Philip Keeling, a senior author of the study and a consultant cardiologist at Torbay and South Devon NHS Foundation Trust, highlighted the device's potential. He stated, "Only about half of people admitted to hospital with heart failure currently get assigned an early review by a heart failure nurse who can check to see if they are suffering a harmful build up of fluid because their heart is not working properly. Amid a shortage of heart failure nurses, a device like this can be like a virtual nurse, tracking people's health."

Heart failure is a chronic condition where the heart struggles to pump blood effectively, often because it has become too weak or stiff. It affects an estimated 920,000 people in the UK. The three primary symptoms indicating a worsening condition are increased breathlessness, weight gain, and swelling in the legs or ankles.

Professor Bryan Williams, chief scientific and medical officer at the British Heart Foundation (BHF), commented on the findings: "This small study suggests a simple device could significantly improve outcomes for at risk patients with heart failure by keeping them out of hospital. This study is a good example of how technology might aid earlier interventions and treatment, by allowing people to track a key sign of their heart health at home."