AI helps spot early signs of glaucoma progression to blindness

Using Artificial Intelligence (AI), researchers have developed a quick test to identify which people with glaucoma are at risk of rapid progression to blindness.

A new test can detect glaucoma progression 18 months earlier than the current gold standard method, said the study published in the journal Expert Review of Molecular Diagnostics.

Glaucoma, the leading global cause of irreversible blindness, affects over 60 million people, which is predicted to double by 2040 as the global population ages.

Loss of sight in glaucoma is caused by the death of cells in the retina, at the back of the eye.

“Being able to diagnose glaucoma at an earlier stage, and predict its course of progression, could help people to maintain their sight, as treatment is most successful if provided at an early stage of the disease,” said study first author Eduardo Normando from Imperial College London.

The test, called DARC (Detection of Apoptosing Retinal Cells), involves injecting into the bloodstream (via the arm) a fluorescent dye that attaches to retinal cells, and illuminates those that are in the process of apoptosis, a form of programmed cell death.

The damaged cells appear bright white when viewed in eye examinations — the more damaged cells detected, the higher the DARC count.

One challenge with evaluating eye diseases is that specialists often disagree when viewing the same scans, so the researchers have incorporated an AI algorithm into their method.

In the Phase II clinical trial of DARC, the AI was used to assess 60 of the study participants — 20 with glaucoma and 40 healthy control subjects.

The AI was initially trained by analysing the retinal scans (after injection of the dye) of the healthy control participants.

The AI was then tested on the glaucoma patients.

Those taking part in the AI study were followed up 18 months after the main trial period to see whether their eye health had deteriorated.

The researchers were able to accurately predict progressive glaucomatous damage 18 months before that seen with the current gold standard OCT retinal imaging technology, as every patient with a DARC count over a certain threshold was found to have progressive glaucoma at follow-up.

“These results are very promising as they show DARC could be used as a biomarker when combined with the AI-aided algorithm,” said lead researcher Francesca Cordeiro from University College London (UCL) Institute of Ophthalmology.

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AI tool predicts which coronavirus patients get deadly 'wet lung'

Researchers in the US and China reported Monday they have developed an artificial intelligence tool that is able to accurately predict which newly infected patients with the novel coronavirus go on to develop severe lung disease.

Once deployed, the algorithm could assist doctors in making choices about where to prioritize care in resource-stretched health care systems, said Megan Coffee, a physician and professor at New York University's Grossman School of Medicine who co-authored a paper on the finding in the journal Computers, Materials & Continua.

The tool discovered several surprising indicators that were most strongly predictive of who went on to develop so-called acute respiratory disease syndrome (ARDS), a severe complication of the COVID-19 illness that fills the lungs with fluid and kills around 50 percent of coronavirus patients who get it.

The team applied a machine learning algorithm to data from 53 coronavirus patients across two hospitals in Wenzhou, China, finding that changes in three features -- levels of the liver enzyme alanine aminotransferase (ALT), reported body aches, and hemoglobin levels –- were most accurately predictive of subsequent, severe disease.

Using this information along with other factors, the tool was able to predict risk of ARDS with up to 80 percent accuracy.

By contrast, characteristics that were considered to be hallmarks of COVID-19, like a particular pattern in lung images called "ground glass opacity," fever, and strong immune responses, were not useful in predicting which of the patients with initially mild symptoms would get ARDS.

Neither age nor sex were useful predictors either, even though other studies have found men over 60 to be at higher risk.

"It's been fascinating because a lot of the data points that the machine used to help influence its decisions were different than what a clinician would normally look at," Coffee told AFP.

Using AI in medical settings isn't a brand new concept -- a tool already exists to help dermatologists predict which patients will go on to develop skin cancer, to give just one example.

What makes this different is that doctors are learning on the fly about COVID-19, and the tool can help steer them in the right direction, in addition to helping them decide which patients to focus on as hospitals become overwhelmed, said co-author Anasse Bari, a computer science professor at NYU.

The team is now looking to further refine the tool with data from New York and hope it is ready to deploy sometime in April. 

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Experimental AI Tool Predicts Which Patients with Pandemic Virus Will Develop Serious Respiratory Disease

An artificial intelligence tool accurately predicted which patients newly infected with the COVID-19 virus would go on to develop severe respiratory disease, a new study found.

The work was led by NYU Grossman School of Medicine and the Courant Institute of Mathematical Sciences at New York University, in partnership with Wenzhou Central Hospital and Cangnan People's Hospital, both in Wenzhou, China.

Named "SARS-CoV-2," the new virus causes the disease called "coronavirus disease 2019" or "COVID-19." As of March 30, the virus had infected 735,560 patients worldwide. According to the World Health Organization, the illness has caused more than 34,830 deaths to date, more often among older patients with underlying health conditions. The New York State Department of Health has reported more than 33,700 cases to date in New York City.

Published online March 30 in the journal Computers, Materials & Continua, the study also revealed the best indicators of future severity, and found that they were not as expected.

"While work remains to further validate our model, it holds promise as another tool to predict the patients most vulnerable to the virus, but only in support of physicians' hard-won clinical experience in treating viral infections," says corresponding study author Megan Coffee, MD, PhD, clinical assistant professor in the Division of Infectious Disease & Immunology within the Department of Medicine at NYU Grossman School of Medicine.

"Our goal was to design and deploy a decision-support tool using AI capabilities – mostly predictive analytics – to flag future clinical coronavirus severity," says co-author Anasse Bari, PhD, a clinical assistant professor in Computer Science at the Courant institute. "We hope that the tool, when fully developed, will be useful to physicians as they assess which moderately ill patients really need beds, and who can safely go home, with hospital resources stretched thin."

Surprise Predictors

For the study, demographic, laboratory, and radiological findings were collected from 53 patients as each tested positive in January 2020 for the SARS-CoV2 virus at the two Chinese hospitals. Symptoms were typically mild to begin with, including cough, fever, and stomach upset. In a minority of patients, however, severe symptoms developed with a week, including pneumonia.

The goal of the new study was to determine whether AI techniques could help to accurately predict which patients with the virus would go on to develop Acute Respiratory Distress Syndrome or ARDS, the fluid build-up in the lungs that can be fatal in the elderly.

For the new study, the researchers designed computer models that make decisions based on the data fed into them, with programs getting "smarter" the more data they consider. Specifically, the current study used decision trees that track series of decisions between options, and that model the potential consequences of choices at each step in a pathway.

The researchers were surprised to find that characteristics considered to be hallmarks of COVID-19, like certain patterns seen in lung images (e.g. ground glass opacities), fever, and strong immune responses, were not useful in predicting which of the many patients with initial, mild symptoms would go to develop severe lung disease. Neither were age and gender helpful in predicting serious disease, although past studies had found men over 60 to be at higher risk.

Instead, the new AI tool found that changes in three features – levels of the liver enzyme alanine aminotransferase (ALT), reported myalgia, and hemoglobin levels – were most accurately predictive of subsequent, severe disease. Together with other factors, the team reported being able to predict risk of ARDS with up to 80 percent accuracy.

ALT levels – which rise dramatically as diseases like hepatitis damage the liver – were only a bit higher in patients with COVID-19, researchers say, but still featured prominently in prediction of severity. In addition, deep muscle aches (myalgia) were also more commonplace, and have been linked by past research to higher general inflammation in the body.

Lastly, higher levels of hemoglobin, the iron-containing protein that enables blood cells to carry oxygen to bodily tissues, were also linked to later respiratory distress. Could this explained by other factors, like unreported smoking of tobacco, which has long been linked to increased hemoglobin levels? Of the 33 patients at Wenzhou Central Hospital interviewed on smoking status, the two who reported having smoked, also reported that they had quit.

Limitations of the study, say the authors, included the relatively small data set and the limited clinical severity of disease in the population studied. The latter may be due in part to an as yet unexplained dearth of elderly patients admitted into the hospitals during the study period. The average patient age was 43.  

"I will be paying more attention in my clinical practice to our data points, watching patients closer if they for instance complain of severe myalgia," adds Coffee. "It's exciting to be able to share data with the field in real time when it can be useful. In all past epidemics, journal papers only published well after the infections had waned."

Along with Coffee and Bari, authors of the study included first author Xiangao Jiang, along with Jianping Huang, Jichan Shi, Jianyi Dai, Jing Cai, Zhengxing Wu, and Guiqing He, in the Department of Infectious Diseases at Wenzhou Central Hospital. Also from Wenzhou Central Hospital was author Yitong Huang of Department of Gynaecology.

Also study authors were Junzhang Wang of the Courant Institute of Mathematical Sciences at New York University, Xinyue Jiang of Columbia University, and Tianxiao Zhang in Department of Infectious Diseases at Cangnan People's Hospital. Coffee is also adjunct faculty in the Department of Population and Family Health at the Mailman School of Public Health at Columbia.

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US researchers use AI to develop powerful new antibiotic

In a first, US researchers have used artificial intelligence to identify a powerful new antibiotic capable of killing several drug-resistant bacteria.

Antibiotics have been a cornerstone of modern medicine since the discovery of penicillin, but their effectiveness has seriously diminished in recent years as overuse has led to bacteria becoming resistant.

The scientists at MIT and Harvard trained a machine learning algorithm to analyze chemical compounds capable of fighting infections using different mechanisms than those of existing drugs.

Their findings were published in the journal Cell on Thursday.

"Our approach revealed this amazing molecule which is arguably one of the more powerful antibiotics that has been discovered," said James Collins, a professor of medical engineering at MIT and one of the paper's senior authors.

The team trained the model on about 2,500 molecules, identifying a compound they called "halicin" -- after the fictional artificial intelligence system from "2001: A Space Odyssey" -- for real world testing on strains of bacteria taken from patients and grown in lab dishes.

It was able to kill many bacteria that are resistant to treatment, including Clostridium difficile, Acinetobacter baumannii, and Mycobacterium tuberculosis.

It also cured two mice with A. baumannii, which has infected many US soldiers in Iraq and Afghanistan.

The strain of the infection in the mice was resistant to all known antibiotics, but a halicin ointment completely cured the mice within 24 hours.

The idea of using predictive computer models for discovery of drugs is not new, but had never been successful until now.

"The machine learning model can explore... large chemical spaces that can be prohibitively expensive for traditional experimental approaches," said Regina Barzilay, a professor of computer science at MIT.

The development raises hope for the future of antibiotics, and comes at a critical time: It is predicted that without immediate action to discover and develop new drugs, deaths attributable to resistant infections will reach 10 million a year by 2050.

The researchers plan to study halicin further and work with a pharmaceutical company or nonprofit to develop it for use in humans.

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Sleep disorders might now be treated with AI

Artificial intelligence can improve the precision of sleep medicines, resulting in more patient-centered care and better outcomes, suggest a new study.

The position statement of the study was published in the journal - Journal of Clinical Sleep Medicine.

The position statement was developed by the AASM's Artificial Intelligence in Sleep Medicine Committee.

According to the statement, the electrophysiological data collected during polysomnography -- the most comprehensive type of sleep study -- is well-positioned for enhanced analysis through AI and machine-assisted learning.

"When we typically think of AI in sleep medicine, the obvious use case is for the scoring of sleep and associated events," said the lead researcher Dr Cathy Goldstein.

"This would streamline the processes of sleep laboratories and free up sleep technologist time for direct patient care," she added.

Because of the vast amounts of data collected by sleep centres, AI and machine learning could advance sleep care, resulting in more accurate diagnoses, prediction of disease and treatment prognosis, characterization of disease subtypes, precision in sleep scoring, and optimization and personalization of sleep treatments.

Goldstein noted that AI could be used to automate sleep scoring while identifying additional insights from sleep data.

"AI could allow us to derive more meaningful information from sleep studies, given that our current summary metrics, for example, the apnea-hypopnea index, aren't predictive of the health and quality of life outcomes that are important to patients," Dr Cathy Goldstein said.

"Additionally, AI might help us understand mechanisms underlying obstructive sleep apnea, so we can select the right treatment for the right patient at the right time, as opposed to one-size-fits-all or trial and error approaches," she added.

Important considerations for the integration of AI into the sleep medicine practice include transparency and disclosure, testing on novel data, and laboratory integration.

The statement recommends that manufacturers disclose the intended population and goal of any program used in the evaluation of patients; test programs intended for clinical use on independent data; and aid sleep centres in the evaluation of AI-based software performance.

"AI tools hold great promise for medicine in general, but there has also been a great deal of hype, exaggerated claims and misinformation," said Goldstein.

"We want to interface with industry in a way that will foster safe and efficacious use of AI software to benefit our patients. These tools can only benefit patients if used with careful oversight," Goldstein added.

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