It is estimated that more than 230M people worldwide suffer from Peripheral Arterial Disease (PAD) and each year more than 22M of them develop Critical Limb Ischemia - a life-threatening condition with 50% combined incidence of amputation and/or death in a 3-year period. Timely PAD diagnosis is the most important factor to avoid complications by treatment procedures (revascularisation) and proper disease management (daily walks, change of dietary habits, etc.). Unfortunately, half of the PAD-suffering population is asymptomatic and therefore lately diagnosed. Moreover, patients with Diabetes Type 2, who ironically have the highest risk of developing complications as a result of the disease, are often undertreated as current non-invasive diagnostic methods such as Ankle-Brachial Index (ABI) give high percentage false negatives due to a condition called Medial Arterial Calcification. Studies reveal that diabetics bear 7x higher risk of amputation than non-diabetic PAD patients.
The high incidence of PAD-related complications mostly comes from the fact that there are no mass-screening programmes and patients visit a cardiovascular specialist for disease confirmation too late. Pain in the limbs, which is often the first symptom of PAD, leads to very slow triage as patients unnecessarily visit neurologists and orthopedists in more than 30% of the PAD cases. This not only delays proper treatment and increases chances for complications but also negatively impacts healthcare budgets. Again, diebitic patients suffer most from the lack of population-wide screening solutions because of their higher tolerance to pain.
Current PAD diagnostic methods have significant drawbacks. The ABI (comparison between blood pressure in upper and lower limbs) is the most commonly used approach for initial examination, however it is highly unreliable, especially for patients with diabetes. Doppler Ultrasound (DUS) is another non-invasive method for blood flow observation. It has much higher specificity rate than ABI but it is applied very local (per artery) and considered error prone unless performed by a rigorously trained specialist. X-ray Angiography is often used for final examination prior to intervention. It is highly invasive as the patient is injected with a dye (contrast agent) and radiated for about 3 hours. All methods require hospital visits and very often more than one is applied, leading for instance to over-care by 30% of the X-ray Angiographies considered unnecessary.
We decided to apply our long-lasting experience in AI, machine learning, and image recognition to build a diagnostic solution as simple and patient-friendly as the specialist, or the GP, or the Nurse Practitioner, or even the patient's partner, taking a thermal image of him. Using a mobile thermal camera we can capture the heat of the body, detect local blood flow anomalies and notify the healthcare professional or the person involved, if needed.
That way we can enable widely accessible & precise (even for patients with diabetes) rapid non-invasive vascular diagnostics.
