Artificial Intelligence (AI) the powerful next generation of health

Artificial intelligence for global health

Artificial intelligence for global health (AI) has demonstrated great progress in the detection, diagnosis, and treatment of diseases. Deep learning has enabled applications with performance levels approaching those of trained professionals in tasks including the interpretation of medical images and discovery of drug compounds 

What exactly is Artificial Intelligence (AI)?

According to The Newscientist web, Artificial intelligence is a potentially world-changing technology. It could help cure cancers, control autonomous cars, and augment human intelligence. AI simply means the software used by computers to mimic aspects of human intelligence.
 
Artificial intelligence for global health is an overarching term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to mimic human cognition in the analysis, presentation, and comprehension of complex medical and health care data.

Artificial intelligence for global health

Artificial intelligence is becoming a transformational force in healthcare

Case n#1 of Artificial intelligence for global health

Nature.com has published a scientific report about ” Blood pressure measurements with the OptiBP smartphone app”, saying that High blood pressure (BP) remains the leading risk factor for death and disability in both high and low-income countries. Its complications are responsible for the deaths of approximately ten million people annually, a 50% increase over the estimates from 1990. By 2025, the number of people suffering from hypertension will reach 1.5 billion. The impact of this disease represents a daunting burden to any healthcare system.

Mobile health diagnostics have been shown to be effective and scalable for chronic disease detection and management. By maximizing the smartphones’ optics and computational power, they could allow the assessment of physiological information from the morphology of pulse waves and thus estimate cuffless blood pressure (BP).

Methodology for training OptiBP

We trained the parameters of an existing pulse wave analysis algorithm (oBPM), previously validated in anesthesia on pulse oximeter signals, by collecting optical signals from 51 patients fingertips via a smartphone while simultaneously acquiring BP measurements through an arterial catheter.

We then compared smartphone-based measurements obtained on 50 participants in an ambulatory setting via the OptiBP app against simultaneously acquired auscultatory systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) measurements. Patients were normotensive (70.0% for SBP versus 61.4% for DBP), hypertensive (17.1% vs. 13.6%) or hypotensive (12.9% vs. 25.0%).

The difference in BP (mean ± standard deviation) between both methods were within the ISO 81,060–2:2018 standard for SBP (− 0.7 ± 7.7 mmHg), DBP (− 0.4 ± 4.5 mmHg), and MBP (− 0.6 ± 5.2 mmHg). These results demonstrate that BP can be measured with accuracy at the finger using the OptiBP smartphone app.

This may become an important tool to detect hypertension in various settings, for example in low-income countries, where the availability of smartphones is high but access to health care is low.

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