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The disruption triggered by the coronavirus (COVID-19) has induced unplanned growth across the healthcare industry. Despite these challenges, leaders in healthcare see tremendous potential in AI and analytics to deliver on the promise of higher quality care at a lower cost by empowering their executives, business leaders, clinicians, and nurses by harnessing the power of predictive and prescriptive analytics. Many healthcare organizations are seeking to harness the vast potential of Artificial Intelligence (AI) and its four components -- machine learning (ML), natural language processing (NLP), deep learning, and robotics -- to transform their clinical and business processes. They seek to apply these advanced technologies to make sense of an ever-increasing "tsunami" of structured and unstructured data, and to automate iterative operations that previously required manual processing. I have analyzed and calibrated these technologies leveraging a seminal strategy framework from John Gourville, Harvard Business School professor, predicated on the resistance to patient adoption, as well the degree of change behavior needed from physicians, clinicians, nurses, providers, payers, policy makers and the government, which will likely assure a high probability of success, in my humble opinion and will inform post-pandemic strategy blueprints and scenario/policy planning from these entities.
Aim To develop a deep learning (DL) model that predicts age from fundus images (retinal age) and to investigate the association between retinal age gap (retinal age predicted by DL model minus chronological age) and mortality risk. Methods A total of 80 169 fundus images taken from 46 969 participants in the UK Biobank with reasonable quality were included in this study. Of these, 19 200 fundus images from 11 052 participants without prior medical history at the baseline examination were used to train and validate the DL model for age prediction using fivefold cross-validation. A total of 35 913 of the remaining 35 917 participants had available mortality data and were used to investigate the association between retinal age gap and mortality. Results The DL model achieved a strong correlation of 0.81 (p<0·001) between retinal age and chronological age, and an overall mean absolute error of 3.55 years. Cox regression models showed that each 1 year increase in the retinal age gap was associated with a 2% increase in risk of all-cause mortality (hazard ratio (HR)=1.02, 95% CI 1.00 to 1.03, p=0.020) and a 3% increase in risk of cause-specific mortality attributable to non-cardiovascular and non-cancer disease (HR=1.03, 95% CI 1.00 to 1.05, p=0.041) after multivariable adjustments. No significant association was identified between retinal age gap and cardiovascular- or cancer-related mortality. Conclusions Our findings indicate that retinal age gap might be a potential biomarker of ageing that is closely related to risk of mortality, implying the potential of retinal image as a screening tool for risk stratification and delivery of tailored interventions. Data are available in a public, open access repository.
The eyes may offer a "window into the soul," as poets say, but they also have a lot to say about your health. Dry eyes can be a sign of rheumatoid arthritis. High levels of cholesterol can cause a white, gray or blue ring to form around the colored part of your eye, called the iris. A coppery gold ring circling the iris is a key sign of Wilson's disease, a rare genetic disorder that causes copper to build up in the brain, liver and other organs, slowing poisoning the body. And that's not all: Damage to blood vessels in the back of your eye, called the retina, can be early signs of nerve damage due to diabetes, high blood pressure, coronary artery disease, even cancer, as well as glaucoma and age-related macular degeneration.
Mike Kisch, Aktiia CEO, told MailOnline that having constant blood pressure measurements in all settings was a'game changer' for doctors and patients That will be for doctors, allowing them to remotely gauge the progress of patients, even see what time of day medication should be taken. 'Right now, after they do the initial diagnosis and prescribe medication, they don't get a lot of data from the patient, so the likelihood that the first time it will work is low, so now they get ongoing data to see if they need to modify treatment. 'That is a game changer for the physician,' explains Mr Kisch. Data gathered by this device is fed into large scale cohort studies, with nine currently running or about to run around the world. One is about the way patient engagement in hypertension management programmes increase when using these products and how a doctors decision making process improves.
Can't get your partner to ever tell you how they really feel? There may be an app for that…one day. Scientists can now predict how someone is feeling using radio waves to measure heart rate and breathing. The wireless signals can detect a person's feelings even in the absence of any other visual cues such as facial expressions. This AI technology could be used to help reveal our inner emotions.
A strong spasm, or abrupt contraction, of a coronary artery, which can block blood flow to the heart muscle, is a less common reason. As a result of improvements in machine learning and artificial intelligence, it is now possible to detect and diagnose diabetes in its early stages using an automated procedure that is more efficient than manual diagnosis. Based on autonomous comparison with a huge collection of typical fundus photos, the server uses IDx-DR software and a "deep-learning" algorithm to discover retinal abnormalities compatible with DR. One of two outcomes is provided by the software: (1) Refer to an eyecare professional (ECP) if more than moderate DR is discovered; (2) If the results are negative for more than mild DR, rescreen in 12 months. Machine learning algorithms and their ability to synthesize extremely complex data may open up new avenues for tailoring drugs to a person's genetic composition.
The healthcare sector is no different--particularly in the wake of the global pandemic, during which rapid and remote healthcare practices have had to take shape almost overnight. Healthcare software development services and data science solutions have become an integral part of the industry today. In fact, data science in healthcare represents arguably one of the most critical and long-overdue sector revolutions of modern times. With data science, healthcare institutions can harness analytics to bring about faster and far more accurate diagnoses while providing treatments that carry a higher efficacy and lower risk to patients' health. And with over a billion clinical documents being produced every year in the US alone, there's a deep mine of healthcare data out there to be drilled.
Scientists from the Cambridge Baker Systems Genomics Initiative have used machine learning to create genetic predictors of blood cell traits, such as white blood cell counts, that are linked to chronic disease. The research, published today in the journal Cell Genomics, identified shared genetic architecture between blood cell traits and various common diseases, including coronary artery disease. Senior author Professor Michael Inouye, Munz Chair of Cardiovascular Prediction and Prevention at the Baker Institute, said the findings could pave the way for novel, personalized methods to better predict, prevent and treat a variety of conditions, including heart disease, the world's biggest killer. Blood cells play essential roles in a variety of biological processes that keep our bodies working well. Blood cell traits--such as the number of cells and the proportions of different types--are among the most common tests in healthcare.