Boston Research AI Tool
Studies by WHO (World Health Organization) and various health research organizations indicate that more than 2 billion people worldwide have respiratory system-related health burdens. Five of the most prominent respiratory diseases are among the most common causes of severe illness worldwide, affecting different age-group populations.
The recent havoc created by COVID-19 is well known, with around 110 million people infected and resulting in more than 2.3 million deaths. Early detection and diagnosis of the infection are crucial in the treatment and mitigation of the spread; however, this continues to be a significant challenge.
A threat of a potential second wave makes it imperative for a continued focus in identifying better tools for early detection and diagnosis of COVID-19 and other respiratory ailments.
Because of the impact of respiratory illness on the global population, Boston Research has taken to the social cause to solve the medical problems underlying lung-affected respiratory diseases using cutting-edge AI solutions and tools.
Boston Research is at the confluence of integrating healthcare know-how with technology for developing solutions that improve curative and preventive health care outcomes and aid in democratizing healthcare. The latest developments in Artificial Intelligence (AI) & Machine Learning form the core of Boston Research’s endeavours.
The Boston AI research team comprises global leading post-doctoral university experts in the biomedical industry, international professionals in fields of neurology, computer science, and data science. The expert project team includes Ted Huang, Director of NTHU APAC EMBA, National Tsing Hua University Asia Pacific. James Weis is the interim CEO of Boston Research AI.
Boston Research AI Tool
Using AI technology, we have developed a quick and accurate solution to identify vital signs & detection of viruses by studying the data received through different imaging sources.
The AI tool replaces the cognitive process of the human brain, where-in it can learn, train, adapt and equip itself with information that is being fed and evolve its understanding. The solution is an integration of proprietary clinical AI software and Terminal Gateway Hardware.
It accurately detects the person’s facial landmarks using the terminal gateway hardware and extracts the regions of interest for vital sign estimation.
The doctors can gain critical information about various vital signs – SPO2 Level, heartbeats per minute, respiration rate, temperature, age range, the coloration of eyes, etc. This information is integrated with Docsun Medical Data Bank.
The Boston Research AI solution offers health care professionals and authorities an efficient tool for early detection and diagnosis of the critical signals to take pre-emptive action. It cuts down the time taken for review from days and hours to within minutes while ensuring detection accuracy as high as 98 %.
The AI Tool is customized to fit into various setups – Health care Centres, Open Spaces, etc.
Self-Diagnostic AI Tool for COVID-19
In an effort for effective detection & management of the COVID-19 pandemic, Boston Research has developed a COVID-19 self-screening solution – an AI-powered solution that integrates with the existing workflows.
The camera on the device captures the user’s dynamic features and then sent to the Docsun Medical Algorithm to get the readings as mentioned earlier for early detection of COVID-19 infection. The screen displays the diagnosis on the screen, and the whole detection takes less than 15 seconds. An added feature is the entry of facial features, user readings & diagnosis into the database, which maintains a track record of the user symptoms.
Boston Research successfully launched its AI Solution on 26th February 2021.
Further details of Boston Research, the AI Tool & COVID-19 Self-diagnostic Tool are available on our website:
Boston Research has achieved a significant breakthrough in early and accurate detection of airborne diseases, including COVID-19, COPD, Asthma, Acute Lower Respiratory Tract Infection, Tuberculosis, and Lung Cancer, by introducing Neuroknowlogy learning formation in the clinical diagnostic mechanism.