AI applications are becoming the one-stop solution for diagnosing critical diseases

Artificial intelligence and machine learning, are dominating every aspect of our lives. AI is used in various areas like healthcare, education, and defense. With the advancement of technology, better computing power, and the availability of large datasets containing valuable information, the use of AI and ML models has increased. The healthcare sector generates enormous amounts of data in terms of images, and patient data, which helps the healthcare companies to understand the patterns and make predictions.

Artificial intelligence is capable of predicting acute critical illness with greater accuracy than the traditional early warning system (EWS), primarily used by healthcare providers. Even though AI is used in healthcare companies for various purposes but predicting critical diseases and their risks beforehand have been one of its greatest contributions.

Recently, researchers and healthcare providers have been using machine learning algorithms to automate the diagnosis of critical diseases like cancer, and other cardiovascular complexities, which has caused a paradigm shift in healthcare facilities. They are using ML models for the real-time diagnosis of disease by developing mobile applications. Some mobile apps can even predict the risk of a certain disease in the future and recommend a diagnosis based on the individual’s medical history and other habits.

Even though machine learning and artificial intelligence have brought a revolutionary change in medical facilities, efficient early detections and diagnosis are still a problem.

Transparency and explainability, are of absolute importance when it comes to the widespread introduction of AI models into clinical practices. Incorrect predictions carry serious consequences. Healthcare providers must understand the underlying reasoning and technical patterns followed by the application to understand potential cases where it might end up with false or incorrect predictions. AI-based early warning systems carry robust and accurate models to predict acute critical diseases.

• Acceleration Of Artificial Intelligence In The Healthcare Industry
• A Surge In The Adoption Of AI By The Healthcare Sector
• Analytics Insight Predicts Healthcare Sector To Touch US$68 Billion In Revenue By 2025

Using Electronic Health Records for Efficient Prediction of Critical Diseases

Electronic health records contain information for both medical providers and patients. These records also contain information that could interfere with the machine’s ability to make correct predictions. Researchers are aiming to eliminate the unnecessary data that can hinder the model’s capability by deploying a machine learning algorithm, called LSAN.

LSAN, is a deep neural network that uses the two-pronged approach to scan electronic health records and identify information that could predict if the patient is facing a risk of developing a deadly disease in the future.

Electronic records use a double-level hierarchical structure to interpret the medical journey of a patient using the International Classification of Diseases (ICD) codes. It begins with the patient’s current situation and follows through the chronological sequence of visits made by the patient. It records the symptoms and the patient’s condition from the last visit to the current state.

Researchers conducted experiments on patients with symptoms of health failure, kidney disease, and dementia and determined that this newly developed machine learning model called LSAN has outperformed the traditional and the current medical technologies and deep learning models.

These models and tools can be effectively used to predict cardiovascular diseases using the patient’s age, cholesterol, weight, blood pressure, and several other factors, and the potential risks that might occur in the next ten years. Hospitals are also increasing the use of business analytics in transportation, patient retention, and other areas to provide the patients a wholesome experience and cost-effective treatments.

The application of AI in this diagnostic process can be of immense support to healthcare providers and patients. The implementation of AI in the medical infrastructure speeds up the identification of relevant medical data from multiple sources, which saves time and resources for the patients and medical practitioners.

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Heart disease and cancer are the leading causes of death in the United States, and it’s increasingly understood that they share common risk factors, including tobacco use, diet, blood pressure, and obesity. Thus, a diagnostic tool that could screen for cardiovascular disease while a patient is already being screened for cancer, has the potential to expedite a diagnosis, accelerate treatment, and improve patient outcomes. 

In research published today in Nature Communications, a team of engineers from Rensselaer Polytechnic Institute and clinicians from Massachusetts General Hospital developed a deep learning algorithm that can help assess a patient’s risk of cardiovascular disease with the same low-dose computerized tomography (CT) scan used to screen for lung cancer. This approach paves the way for more efficient, more cost-effective, and lower radiation diagnoses, without requiring patients to undergo a second CT scan. 

“In this paper, we demonstrate very good performance of a deep learning algorithm in identifying patients with cardiovascular diseases and predicting their mortality risks, which shows promise in converting lung cancer screening low-dose CT into a dual screening tool,” said Pingkun Yan, an assistant professor of biomedical engineering and member of the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer.

Numerous hurdles had to be overcome in order to make this dual screening possible. Low-dose CT images tend to have lower image quality and higher noise, making the features within an image harder to see. Using a large dataset from the National Lung Screening Trial (NLST), Yan and his team used data from more than 30,000 low-dose CT images to develop, train, and validate a deep learning algorithm capable of filtering out unwanted artifacts and noise, and extracting features needed for diagnosis. Researchers validated the algorithm using an additional 2,085 NLST images.

The Rensselaer team also partnered with Massachusetts General Hospital, where researchers were able to test this deep learning approach against state-of-the-art scans and the expertise of the hospital’s radiologists. The Rensselaer-developed algorithm, Yan said, not only proved to be highly effective in analyzing the risk of cardiovascular disease in high-risk patients using low-dose CT scans, but it also proved to be equally effective as radiologists in analyzing those images. In addition, the algorithm closely mimicked the performance of dedicated cardiac CT scans when it was tested on an independent dataset collected from 335 patients at Massachusetts General Hospital.

“This innovative research is a prime example of the ways in which bioimaging and artificial intelligence can be combined to improve and deliver patient care with greater precision and safety,” said Deepak Vashishth, the director of CBIS.

Yan was joined in this work by Ge Wang, an endowed chair professor of biomedical engineering at Rensselaer and fellow member of CBIS. The Rensselaer team was joined by Dr. Mannudeep K. Kalra, an attending radiologist at Massachusetts General Hospital and professor of radiology with Harvard Medical School. This research was funded by the National Institutes of Health National Heart, Lung, and Blood Institute. 

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The rise of precision medicine is being augmented by greater use of deep learning technologies that provide predictive analytics for earlier diagnosis of a range of debilitating diseases.

The latest example comes from researchers at Michigan-based Beaumont Health who used deep learning to analyze genomic DNA. The resulting simple blood test could be used to detect earlier onset of Alzheimer’s disease.

In a study published this week in the peer-reviewed scientific journal PLOS ONE, the researchers said their analysis discovered 152 “significant” genetic differences among Alzheimer’s and healthy patients. Those biomarkers could be used to provide diagnoses before Alzheimer’s symptoms develop and a patient’s brain is irreversibly damaged.

“The holy grail is to identify patients in the pre-clinical stage so effective early interventions, including new medications, can be studied and ultimately used,” said Dr. Ray Bahado-Singh, a Beaumont Health geneticist who led the research.

The need to identify the early signs of Alzheimer’s disease grows as the global population ages. For example, the annual World Alzheimer Report estimates 75 million will be stricken by 2030. Researchers are working to prevent some of those predicted cases by leveraging new deep learning tools to accelerate the diagnoses of a disease that often goes undetected until it is too late to stop the damage.

The Beaumont researchers said they used deep learning and other machine learning platforms along with “genome-wide” DNA analysis of leukocytes, a type of blood cell manufactured in bone marrow and associated with the body’s immune system.

“We used and compared conventional machine learning and deep learning classification algorithms which typically begin with an established set of data … and a certain understanding of how that data is classified” as either Alzheimer’s or healthy patients, said co-investigator Buket Aydas, analytics manager at Blue Cross Blue Shield of Michigan.

“These algorithms are intended to find patterns in data that can be applied to an analytics process,” Aydas added in an email.

The researchers compared the performance of their deep learning framework with five other machine learning algorithms, including a prediction analysis tool. The six platforms scanned about 800,000 changes in the leukocytes genome.

The deep learning algorithm performed best.

“We also found out the important genetic features that contribute most to the [deep learning] prediction and were able to predict the absence or presence of Alzheimer’s by the help of these important genetic features,” Aydas said.

The genetic analysis ultimately predicted either the absence or presence of the disease, “allowing us to read what is going on in the brain through the blood,” Dr. Bahado-Singh said.

One problem encountered by the investigators was “overfitting,” which occurs when data sets fit a machine learning too precisely. Counterintuitively, the snug fit often produces unreliable results.

To avoid overfitting in the deep learning framework, the researchers said they employed standard parameters to tune models and overcome the overfitting problem.

The researchers said the next step is an expanded study over the next year designed to replicate the initial findings of the Alzheimer’s analysis. Advances in this branch of precision medicine could lead to development of targeted treatments to “interrupt the disease process,” according to Dr. Bahado-Singh.

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Deep learning methods analyzed genomic data from whole blood samples and found differences in patients with Alzheimer’s disease.

Using deep learning and genomic data, researchers from Beaumont Health have discovered a simple blood test that may help predict Alzheimer’s disease in patients.

In a study published in PLOS ONE, the team described using deep learning processes to analyze extracted genomic DNA from whole blood samples. The analysis uncovered 152 significant genetic differences in patients with Alzheimer’s compared to healthy patients.

The new deep learning method has the potential to diagnose patients much earlier in the disease process, before symptoms develop and the brain is irreversibly damaged. Experts believe that the brain changes in Alzheimer’s disease precede the onset of symptoms by years.

Globally, more than 47 million individuals have Alzheimer’s, with women making up more than 60 percent of patients. As the population continues to age, it’s expected that 75 million people will be affected by Alzheimer’s by 2030, with a subsequent rise to 131 million by 2050.

“The holy grail is to identify patients in the pre-clinical stage so effective early interventions, including new medications, can be studied and ultimately used,” said Ray Bahado-Singh, chairman of the Beaumont Department of Obstetrics and Gynecologist and an expert in women’s health. “That’s why we are excited about the results of this research.”

Most patients with Alzheimer’s aren’t diagnosed until later stages of the disease, when the brain has already suffered irreversible damage. There is currently no cure for Alzheimer’s, and treatment is limited to drugs that attempt to treat symptoms and have little impact on the disease’s progression.

“Drugs used in the late stage of the disease do not seem make much difference, so there is a tremendous amount of interest in diagnosis in the early stages of the disease,” said Khaled Imam, Beaumont Health’s Director of Geriatric Medicine.

“Any delay in symptom onset is likely to be very beneficial.  Also, a spinal tap or MRI can identify the start of the disease. But that is invasive and/or expensive. And you cannot do a spinal tap on everyone over age 65. So, blood is thought to be a desirable way of approaching this. And it would be relatively cheap and minimally invasive as compared to an MRI or spinal tap.”

In the analysis, researchers compared blood samples from 24 Alzheimer’s patients and 24 cognitively healthy patients. The team analyzed white blood cells in the blood samples and compared biomarkers to see if they had been generally affected in patients with Alzheimer’s disease.

Part of the Alzheimer’s disease process is brain inflammation thought to trigger the production of white blood cells, or leukocytes, which then become genetically altered while fighting the disease. Researchers looked for telling genetic markers, or methylation marks, an important chemical modification of genes leading to altered gene function that indicate the disease process has started.

“It’s almost as if the leukocytes have become a newspaper to tell us, ‘This is what’s going on in the brain,’” Bahado-Singh said.

The team used six different artificial intelligence and deep learning platforms to look at about 800,000 changes in the genome of the leukocytes.

Researchers noted that the results could potentially advance precision medicine for Alzheimer’s disease, and provide evidence that epigenetic factors may play a critical role in Alzheimer’s development.

Going forward, the group will aim to organize a much larger study to replicate the study’s initial findings over the next year or so.

“What the results said to us is there are significant changes in accessible blood cells that we can use possibly to detect Alzheimer’s,” Bahado-Singh said.

“We found that the genetic analysis accurately predicted the absence or presence of Alzheimer’s, allowing us to read what is going on in the brain through the blood.  The results also gave us a readout of the abnormalities that are causing Alzheimer’s disease. This has future promise for developing targeted treatment to interrupt the disease process.”

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The Center for Noise and Vibration Control at KAIST announced that their coughing detection camera recognizes where coughing happens, visualizing the locations. The resulting cough recognition camera can track and record information about the person who coughed, their location, and the number of coughs on a real-time basis.

Professor Yong-Hwa Park from the Department of Mechanical Engineering developed a deep learning-based cough recognition model to classify a coughing sound in real time. The coughing event classification model is combined with a sound camera that visualizes their locations in public places. The research team said they achieved a best test accuracy of 87.4 %.

Professor Park said that it will be useful medical equipment during epidemics in public places such as schools, offices, and restaurants, and to constantly monitor patients’ conditions in a hospital room.

Fever and coughing are the most relevant respiratory disease symptoms, among which fever can be recognized remotely using thermal cameras. This new technology is expected to be very helpful for detecting epidemic transmissions in a non-contact way. The cough event classification model is combined with a sound camera that visualizes the cough event and indicates the location in the video image.

To develop a cough recognition model, a supervised learning was conducted with a convolutional neural network (CNN). The model performs binary classification with an input of a one-second sound profile feature, generating output to be either a cough event or something else.

In the training and evaluation, various datasets were collected from Audioset, DEMAND, ETSI, and TIMIT. Coughing and others sounds were extracted from Audioset, and the rest of the datasets were used as background noises for data augmentation so that this model could be generalized for various background noises in public places.

The dataset was augmented by mixing coughing sounds and other sounds from Audioset and background noises with the ratio of 0.15 to 0.75, then the overall volume was adjusted to 0.25 to 1.0 times to generalize the model for various distances.

The training and evaluation datasets were constructed by dividing the augmented dataset by 9:1, and the test dataset was recorded separately in a real office environment.

In the optimization procedure of the network model, training was conducted with various combinations of five acoustic features including spectrogram, Mel-scaled spectrogram and Mel-frequency cepstrum coefficients with seven optimizers. The performance of each combination was compared with the test dataset. The best test accuracy of 87.4% was achieved with Mel-scaled Spectrogram as the acoustic feature and ASGD as the optimizer.

The trained cough recognition model was combined with a sound camera. The sound camera is composed of a microphone array and a camera module. A beamforming process is applied to a collected set of acoustic data to find out the direction of incoming sound source. The integrated cough recognition model determines whether the sound is cough or not. If it is, the location of cough is visualized as a contour image with a ‘cough’ label at the location of the coughing sound source in a video image.

A pilot test of the cough recognition camera in an office environment shows that it successfully distinguishes cough events and other events even in a noisy environment. In addition, it can track the location of the person who coughed and count the number of coughs in real time. The performance will be improved further with additional training data obtained from other real environments such as hospitals and classrooms.

Professor Park said, “In a pandemic situation like we are experiencing with COVID-19, a cough detection camera can contribute to the prevention and early detection of epidemics in public places. Especially when applied to a hospital room, the patient’s condition can be tracked 24 hours a day and support more accurate diagnoses while reducing the effort of the medical staff.”

This study was conducted in collaboration with SM Instruments Inc.

/Public Release. The material in this public release comes from the originating organization and may be of a point-in-time nature, edited for clarity, style and length. View in full here.

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June 16, 2020 – A clinical decision support system that leverages machine learning techniques could help patients control their glucose levels and enhance type 1 diabetes management, according to a study published in Nature Metabolism.

People with type 1 diabetes do not produce their own insulin, so they have to take it continuously throughout the day using an insulin pump or with multiple daily injections. Dosing errors can result in life-threatening hypoglycemia events and hyperglycemia, which increases an individual’s risk of neuropathy, retinopathy, and nephropathy.

Additionally, because patients with type 1 diabetes can typically go three to six months between appointments with their endocrinologist, they can be at high risk of these dangerous complications if their glucose levels rise too high or fall too low.

To improve diabetes management, researchers from Oregon Health & Science University (OHSU) leveraged a machine learning algorithm that could generate insulin injection recommendations.

The team trained the algorithm using over 50,000 glucose observations. The algorithm was trained to identify causes of hypoglycemia or hyperglycemia and determine necessary insulin adjustments from a set of 12 potential recommendations. When paired with a smartphone app called DailyDose, the recommendations from the algorithm were shown to be in agreement with physicians 67.9 percent of the time.

Researchers then validated the system by monitoring 16 people with type 1 diabetes over the course of four weeks, showing that the model can help reduce hypoglycemia.

“Our system design is unique,” said lead author Nichole Tyler, an MD-PhD student in the OHSU School of Medicine. “We designed the AI algorithm entirely using a mathematical simulator, and yet when the algorithm was validated on real-world data from people with type 1 diabetes at OHSU, it generated recommendations that were highly similar to recommendations from endocrinologists.”

The researchers noted that their study advances previous findings on using machine learning tools to help patients manage glucose levels.

“There are other published algorithms on this, but not a lot of clinical studies,” said Peter Jacobs, PhD, associate professor of biomedical engineering in the OHSU School of Medicine and senior author on the study.

“Very few have shown a statistically relevant outcome – and most do not compare algorithm recommendations with those of a physician. In addition to showing improvement in glucose control, our algorithm-generated recommendations that had very high correlation with physician recommendations with over 99 percent of the algorithm’s recommendations delivered across 100 weeks of patient testing considered safe by physicians.” 

Investigators have recognized the potential for artificial intelligence to improve diabetes management. A 2019 study from Rensselaer Polytechnic Institute leveraged AI and big data analytics to evaluate information from thousands of glucose monitors and insulin pumps. The team will use the data to enhance the algorithms that control these devices, resulting in better quality of life for people with type 1 diabetes.

“If we look at hundreds of people we can say, ‘Oh, certain problems occur more often in this age group, this type of population, or with this particular type of sensor,’” said Wayne Bequette, professor of chemical and biological engineering at Rensselaer Polytechnic Institute.

“If, for example, you find that it’s more likely that people 8 to 12 years old have these types of irregularities, then you can account for that in your algorithm, and provide more personalized control while reducing burden.”

Going forward, the OHSU team will continue to refine and develop the clinical decision support tool to further improve patients’ management of type 1 diabetes.

“We have plans over the next several years to run several larger trials over eight and then 12 weeks and to compare DailyDose with other insulin treatment strategies, including automated insulin delivery,” said co-author Jessica Castle, MD, associate professor of medicine (endocrinology, diabetes and clinical nutrition) in the OHSU School of Medicine.

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An AI-driven health monitoring and disease detection platform was able to catch the signs of the Wuhan viral outbreak approximately a week before government agencies warned the public, providing a look at how AI can be used to catch disease outbreaks in a timely fashion.

While the official World Health Organization notification of the Wuhan virus went out on January ninth and the US Center for Disease Control and Prevention (CDC) received word of the outbreak on January sixth, the first warning signs of the outbreak were picked up by a Canadian health monitoring system almost a week prior. As Wired reported, the AI-driven health system BlueDot warned its clients about the possible outbreak on December 31st. Bluedot uses AI algorithms to monitor different global news sources and detect patterns in health reports. It also takes into account information on plant and animal disease networks. Using the information it collects, BlueDot epidemiologists then delivers warnings and predictions about possible health risks and outbreaks to its subscribers.

When dealing with an outbreak of disease, early detection is always better. The earlier the detection, the more time health officials have to respond. In the case of the Wuhan virus and other disease outbreaks in China, the Chinese government has often been slow in sharing information with global public health officials. This possesses a problem as the CDC and WHO rely on communications from other government agencies to plan their own responses. However, if an AI system like BlueDot can make accurate predictions based on the information that leaks through across many individual news reports, blogs, and forums, this could potentially enable health organizations to act quicker in response to outbreaks.

According to Kamran Khan, the found of BlueDot, the company doesn’t use social media data when predicting the spread of diseases because the data is too variable and messy to be of use. Instead, news reports, data on known animal disease networks, and airline ticketing data is combined to create a model that predicts where infections begin and where infected people may travel next. BlueDot was correctly able to predict that the Wuhan virus would spread to Taipei, Tokyo, Seoul, and Bangkok within a few days of its manifestation.

BlueDot was launched by Khan in 2014, and the company currently has 40 employees, including data scientists, physicians, and programmers who work together to create the disease surveillance and prediction models. Machine learning algorithms and natural language processing techniques are used to mine data from news reports spanning the globe and covering 65 different languages. Khan said to Wired:

“What we have done is use natural language processing and machine learning to train this engine to recognize whether this is an outbreak of anthrax in Mongolia versus a reunion of the heavy metal band Anthrax.”

After the automated data collection and initial analysis are complete, human analysts double-check the data and ensure that the model’s conclusions seem sound. Finally, a report is generated and sent out to the clients of the application.

BlueDot’s system is far from the first attempt by the AI field to predict the spread of diseases. Data scientists have been using big data and machine learning models to track the spread of various diseases like for some time now, with some attempts being more successful than others. Google tried its own hand at tracking the spread of disease with Google Flu Trends, but its attempts to predict the severity of the 2013 flu seasons were reportedly off by about 140%. Only time will tell if BlueDot can consistently predict the spread of diseases, but if it can it could pave the way for faster, more accurate estimates of disease outbreaks.

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During the past years, artificial intelligence (AI)—the capability of a machine to mimic human behavior—has become a key player in high-tech areas like drug development projects. AI tools help scientists to uncover the secret behind the big biological data using optimized computational algorithms. AI methods such as deep neural network improves decision making in biological and chemical applications i.e., prediction of disease-associated proteins, discovery of novel biomarkers and de novo design of small molecule drug leads. These state-of-the-art approaches help scientists to develop a potential drug more efficiently and economically.

A research team led by Professor Hongzhe Sun from the Department of Chemistry at the University of Hong Kong (HKU), in collaboration with Professor Junwen Wang from Mayo Clinic, Arizona in the United States (a former HKU colleague), implemented a robust deep learning approach to predict disease-associated mutations of the metal-binding sites in a protein. This is the first deep learning approach for the prediction of disease-associated metal-relevant site mutations in metalloproteins, providing a new platform to tackle human diseases. The research findings were recently published in a top scientific journal Nature Machine Intelligence.

Metal ions play pivotal roles either structurally or functionally in the (patho)physiology of human biological systems. Metals such as zinc, iron and copper are essential for all life, and their concentration in cells must be strictly regulated. A deficiency or an excess of these physiological metal ions can cause severe disease in humans. It was discovered that mutations in the human genome are strongly associated with different diseases. If these mutations happen in the coding region of DNA, they might disrupt metal-binding sites of the proteins and consequently initiate severe diseases in humans. Understanding of disease-associated mutations at the metal-binding sites of proteins will facilitate discovery of new drugs.

The team first integrated omics data from different databases to build a comprehensive training dataset. By looking at the statistics from the collected data, the team found that different metals have different disease associations. A mutation in zinc-binding sites has a major role in breast, liver, kidney, immune system and prostate diseases. By contrast, the mutations in calcium- and magnesium-binding sites are associated with muscular and immune system diseases, respectively. For iron-binding sites, mutations are more associated with metabolic diseases. Furthermore, mutations of manganese- and copper-binding sites are associated with cardiovascular diseases with the latter being associated with nervous system disease as well.

The researchers used a novel approach to extract spatial features from the metal binding sites using an energy-based affinity grid map. These spatial features have been merged with physicochemical sequential features to train the model. The final results show that using the spatial features enhanced the performance of the prediction with an area under the curve (AUC) of 0.90 and an accuracy of 0.82. Given the limited advanced techniques and platforms in the field of metallomics and metalloproteins, the proposed deep learning approach offers a method to integrate experimental data with bioinformatics analysis. The approach will help scientist to predict DNA mutations which are associated with diseases like cancer, cardiovascular diseases and genetic disorders.

Professor Sun said: “Machine learning and AI play important roles in the current biological and chemical sciences. In my group we worked on metals in biology and medicine using an integrative omics approach including metallomics and metalloproteomics, and we already produced a large amount of valuable data using in vivo/vitro experiments. We are now developing an artificial intelligence approach based on deep learning to turn these raw data into valuable knowledge, leading us to uncover secrets behind the diseases and to fight them. I believe this novel deep learning approach can be used in other projects, which is ongoing in our laboratory.”

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LONDON — Artificial intelligence robots are turbocharging the race to find new drugs for the crippling nerve disorder ALS, commonly called Lou Gehrig’s disease.

The condition attacks and kills nerve cells controlling muscles, leading to weakness, paralysis and, ultimately, respiratory failure.

There are only two drugs approved by the U.S. Food and Drug Administration to slow the progression of ALS (amyotrophic lateral sclerosis), one available since 1995 and the other approved just this year. About 140,000 new cases are diagnosed a year globally, and there is no cure.

“Many doctors call it the worst disease in medicine, and the unmet need is huge,” said Richard Mead of the Sheffield Institute of Translational Neuroscience, who has found artificial intelligence (AI) is already speeding up his work.

Such robots — complex software run through powerful computers — work as tireless and unbiased super-researchers.

They analyze huge chemical, biological and medical databases, alongside reams of scientific papers, far quicker than humanly possible, throwing up new biological targets and potential drugs.

Cell deaths prevented

One candidate proposed by AI machines recently produced promising results in preventing the death of motor neurone cells and delaying disease onset in preclinical tests in Sheffield.

Mead, who aims to present the work at a medical meeting in December, is now assessing plans for clinical trials.

He and his team in northern England are not the only ones waking up to the ability of AI to elucidate the complexities of ALS.

In Arizona, the Barrow Neurological Institute last December found five new genes linked to ALS by using IBM’s Watson supercomputer. Without the machine, researchers estimate the discovery would have taken years rather than only a few months.

Mead believes ALS is ripe for AI and machine-learning because of the rapid expansion in genetic information about the condition and the fact there are good test-tube and animal models with which to evaluate drug candidates.

That is good news for ALS patients seeking better treatment options. Famous sufferers include Gehrig, the 1923-39 New York Yankees baseball player; actor and playwright Sam Shepard, who died last month; and cosmologist Stephen Hawking, a rare example of someone living for decades with the condition.

If the research goes on to deliver new medicines, it would mark a notable victory for AI in drug discovery, bolstering the prospects of a growing batch of startup companies focused on the technology.

Those firms are based on the premise that while AI robots won’t replace scientists and clinicians, they should save time and money by finding drug leads several times faster than conventional processes.

British ‘unicorn’

Mead from Sheffield is working with BenevolentAI, one of a handful of British “unicorns” — private companies with a market value above $1 billion, in this case $1.7 billion — which is rapidly expanding operations at its offices in central London.

Others in the field include Scotland’s Exscientia and U.S.-based firms Berg, Numerate, twoXAR, Atomwise and InSilico Medicine — the last of which recently launched a drug discovery platform geared specifically to ALS.

A view of BenevolentAI’s home page.

“What we are trying to do is find relationships that will give us new targets in disease,” said Jackie Hunter, a former drug hunter at GlaxoSmithKline (GSK) who now heads Benevolent’s pharma business. “We can do things so much more dynamically and be really responsive to what essentially the information is telling us.”

Unlike humans, who may have pet theories, AI scans through data and generates hypotheses in an unbiased way.

Conventional drug discovery remains a hit-and-miss affair, and Hunter believes the 50 percent failure rates seen for experimental compounds in mid- and late-stage clinical trials due to lack of efficacy is unsustainable, forcing a shift to AI.

A key test will come with a study by Benevolent to assess a previously unsuccessful compound from Johnson & Johnson in a new disease area — this time for treating Parkinson’s disease patients with excessive daytime sleepiness.

Big pharmaceutical companies like GSK, Sanofi and Merck are now exploring the potential of AI through deals with startups.

Being careful

They are treading cautiously, given the failure of “high throughput screening” in the early 2000s to improve efficiency by using robots to test millions of compounds. Yet AI’s ability to learn on the job means things may be different this time.

CPR Asset Management fund manager Vafa Ahmadi, for one, believes it is a potential game-changer.

“Using artificial intelligence is going to really accelerate the way we produce much better targeted molecules. It could have a dramatic impact on productivity, which in turn could have a major impact on the valuation of pharmaceutical stocks,” he said.

Drugmakers and startups are not the only ones chasing that value. Technology giants including Microsoft, IBM and Google’s parent Alphabet are also setting up life sciences units to explore drug R&D.

For Benevolent’s Hunter, today’s attempts to find new drugs for ALS and other difficult diseases amount to an important test vehicle for the future of AI, which is already being deployed in other high-tech areas such as autonomous cars.

“The aim is to show that we can deliver in a very difficult and complex area, ” Hunter said. “I believe if you can do it in drug discovery and development, you can show the power of AI anywhere.”

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